机器学习学术速递[7.22]

AI 总结：提出用于端到端射频频谱监测的E-SpecFormer，引入LiTAN注意力机制，有四种可扩展变体。在相关数据集测试中，Nano变体参数少、速度快，准确率高，以低成本超越现有边缘模型，是物联网设备实时频谱智能的高效解决方案。

链接：https://arxiv.org/abs/2607.18285

机构：Sorbonne Université, CNRS, LIP6（索邦大学、法国国家科学研究中心、巴黎第六大学信息与自动化实验室）

作者：Zhifan Song, Haralampos-G. Stratigopoulos, Hassan Aboushady

英文摘要：We present E-SpecFormer (Edge Spectrum monitoring Transformer) for end-to-end automatic modulation and covert channel (CC) recognition. We introduce LiTAN (Linear Tanh Attention Network), a Softmax- and LayerNorm-free attention mechanism that reduces complexity while increasing accuracy in RF tasks. E-SpecFormer is parameterized in four scalable variants (Nano, Small, Medium, Large) to accommodate diverse hardware constraints. Using the RadioML2018 dataset for modulation recognition, the Nano variant achieves 86.5% average accuracy for Signal-to-Noise Ratios (SNRs)>0 dB, and on the hardware Trojan (HT)-based CC dataset it reaches 94.2% accuracy, both with fewer than 10k parameters and up to speed of 92 {\mu}s per frame on FPGA/CPU co-execution, surpassing state-of-the-art edge models at a fraction of their cost. These results establish E-SpecFormer as an edge-efficient solution for real-time spectrum intelligence on Internet of Things (IoT) devices. GitHub link to the repository: this https URL.

AI 总结：研究利用BearingNAS框架，通过轻量级无导数搜索策略及单数据流搜索空间，针对微预算优化轴承故障诊断架构，在笔记本电脑CPU上运行，为意法半导体目标实现高诊断准确率，证明可将机器学习负载移至传感器封装内进行低成本故障诊断。

链接：https://arxiv.org/abs/2607.18287

机构：Case Western Reserve University（凯斯西储大学）； STMicroelectronics（意法半导体）

作者：Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo, Antonio Frisoli, Rodolfo Zunino

英文摘要：This paper introduces BearingNAS, a Hardware-Aware Neural Architecture Search (HW-NAS) framework designed to shift the intelligence directly onto the sensor die via in-sensor processing. BearingNAS frames the search as a constrained optimization problem targeting extreme micro-budgets (4 to 8 kiB of RAM and 16 to 32 kiB of Flash). To eliminate the reliance on expensive discrete GPUs, we propose a lightweight, derivative-free search strategy paired with a single data-flow search space that leverages a decaying kernel growth formulation to prevent parameter explosion. We evaluate our framework on the Case Western Reserve University (CWRU) bearing benchmark, optimizing architectures for three STMicroelectronics targets: two commodity microcontrollers and the LSM6DSO16IS Intelligent Sensor Processing Unit (ISPU). Running entirely on a laptop CPU, the search converges in less than an hour. The resulting best in-sensor architecture achieves a highly competitive diagnostic accuracy of 99.50\% on the ISPU. These results demonstrate the viability of shifting the machine learning workload inside the sensor package, enabling low-cost, production-scale bearing fault diagnosis.

AI 总结：研究提出基于双曲正割函数的SechKAN架构，利用双曲正割基及一维线性变换减少参数，在函数拟合、PDE问题和图像分类任务中表现出色，相比MLP和其他KAN变体性能更优，虽运行时间稍长但仍有优势。

链接：https://arxiv.org/abs/2607.18290

机构：University of Information Technology（信息技术大学）； Vietnam National University（越南国立大学）

作者：Hoang-Thang Ta

英文摘要：In recent years, Kolmogorov-Arnold Networks (KANs) have attracted increasing attention due to their effectiveness in machine learning and scientific computing tasks, offering a new paradigm for neural network design. In this paper, we present SechKAN, a KAN architecture based on hyperbolic secant (sech) functions. The hyperbolic secant basis is used for its smooth bell-shaped form, localized responses, and stable gradients. We employ 1D linear transformations to reduce the number of parameters, allowing SechKAN to remain comparable to multilayer perceptrons (MLPs) in model size. Experimental results indicate the effectiveness of SechKAN in function fitting, PDE problems, and image classification tasks on benchmark datasets, including MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100. SechKAN achieves superior performance compared to MLPs and other KAN variants while maintaining a similar number of parameters. However, its running time, while better than that of other KAN variants, is slightly longer than that of MLPs.

AI 总结：该研究针对交通事故风险预测中存在的问题，提出MambaLSTM框架，通过开发挤压激励时间特征融合模块、新补丁嵌入模块、引入Mamba块及MambaLSTM单元，有效捕捉时空特征与依赖性来识别风险模式，实验表明其优于现有方法。

链接：https://arxiv.org/abs/2607.18353

作者：Zhen Yu, Yachao Yuan, Zixiang Peng, Muting Li, Thar Baker

英文摘要：In traffic accident risk prediction, most studies overlook the extra noise that could be incorporated when fusing temporal features into spatial features, and some models struggle to capture global correlations among spatial regions. To address these challenges, we propose a novel traffic accident risk prediction framework named MambaLSTM. First, we develop a squeeze-and-excitation temporal feature fusion module to integrate temporal information without compromising spatio-temporal integrity. Second, we introduce a new patch embedding module for effectively capturing semantic relationships among spatially adjacent regions. Additionally, we introduce a Mamba block based on state-space models to model global spatial semantics in urban regions. Finally, we propose a MambaLSTM unit to efficiently capture long- and short-term temporal dependencies for identifying dynamic risk patterns. Extensive experiments on real-world datasets demonstrate the proposed model's superiority over state-of-the-art methods. The code is released at this https URL.

AI 总结：本文提出用多跳MIMO中继网络构建深度无线物理神经网络，以功率放大器非线性为激活函数，实现可端到端训练的空中全连接网络，针对不同CSI场景开发两种收发器设计，仿真显示该架构能准确进行图像分类，凸显利用硬件非线性增强推理能力的优势。

链接：https://arxiv.org/abs/2607.18354

机构：Department of Electrical and Electronic Engineering, Imperial College London（伦敦帝国理工学院电气与电子工程系）； Faculty of Engineering, Bar-Ilan University（巴伊兰大学工程学院）

作者：Meng Hua, Itsik Bergel, Deniz Gündüz

英文摘要：Wireless physical neural networks (WPNNs) embed neural computation directly into analog hardware, offering lower energy consumption and latency than conventional digital implementations. In this paper, we propose a deep WPNN in which nonlinear activations are realized by a multi-hop multiple-input multiple-output (MIMO) relay network, in which each relay implements a trainable complex linear gain and bias, followed by the power amplifier's intrinsic nonlinearity acting as an activation function. The cascade of multiple relays therefore realizes an over-the-air fully connected network whose parameters can be trained end-to-end. We develop two transceiver designs for different channel state information (CSI) availability scenarios: a least squares (LS)-based scheme requiring only receiver-side CSI, and a singular-value-decomposition (SVD)-based scheme requiring both transmitter-side and receiver-side CSI. Simulation results show that the proposed architecture enables accurate over-the-air inference for image classification. In particular, the results highlight the advantage of exploiting hardware nonlinearity for enhanced inference capability.

AI 总结：研究深度线性编码器-only变压器推理时行为，通过相互作用粒子系统视角，在二维嵌入维度揭示其动力学可表述为广义Kuramoto型模型，还发现多种长期行为及相关特性，高维数值实验表明这些行为在更高维度持续存在。

链接：https://arxiv.org/abs/2607.18584

机构：University of Wisconsin–Madison（威斯康星大学麦迪逊分校）； University of Maryland（马里兰大学）； University of Warsaw（华沙大学）； Rice University（莱斯大学）； University of Oxford（牛津大学）

作者：Sixu Li, Thomas Jacob Maranzatto, Jan Peszek, Trevor Teolis, Semih Akkoc, Konstantin Riedl, Sennur Ulukus, Nicolás García Trillos

英文摘要：We study the inference-time behavior of deep linear encoder-only transformers through the lens of interacting particle systems. In this perspective, tokens are modeled as particles that interact dynamically through successive linear self-attention layers. We show that in embedding dimension two, for any key, query, and value matrices, the dynamics can be reformulated as a generalized Kuramoto-type model with pure second-harmonic coupling. This formulation is amenable to Watanabe--Strogatz theory which reveals the dynamics are intrinsically low-dimensional regardless of the parameter matrices. For a class of token initializations associated with the Ott--Antonsen (OA) manifold, we show that the parameter matrices induce a diverse variety of long-time behaviors in linear transformers, including clustering, oscillations, and bifurcations. The oscillations and bifurcations are characterized by uncovering a hidden Hamiltonian structure in the dynamics. By establishing a structural stability result, we further show that dynamics initialized near the OA manifold exhibit the same long-time behavior as those initialized exactly on the manifold. Motivated by our theory in dimension two, we conduct numerical experiments for analogous parameter regimes in higher-dimensional transformers. Our numerical experiments suggest that the long-time behaviors characterized in our theoretical results persist in higher dimensions.

AI 总结：研究受肾脏逆流倍增机制启发的可微序列算子CCM层，作为残差迭代细化的替代方案，用于实现尿液浓缩类似效果，有望为神经架构带来新方法。

链接：https://arxiv.org/abs/2607.18829

作者：Snigdha Chandan Khilar

英文摘要：The mammalian kidney concentrates urine using a mechanism with no analogue in current neural architectures: the countercurrent multiplier. Two anti-parallel flows joined at a hairpin recirculate a weak magnitude-bounded local pump into a large axial gradient achieving a four-fold concentration increase from a single-effect gradient that never exceeds 200 mOsm at any point. We formalize this mechanism as a differentiable sequence operator the Countercurrent Multiplier (CCM) layer and study it as an alternative to residual iterative refinement.

AI 总结： 研究针对大语言模型在金融决策任务中泄露参数知识的问题，提出HindsightBench黑盒行为审计协议，通过特定矩阵、探测及指标分析参数后见之明，应用于多模型获三个主要模式，揭示模型特性及审计结果与服务的关系，还发布相关数据。

链接：https://arxiv.org/abs/2607.18867

机构：University College Dublin（都柏林大学学院）

作者：Haozhe Jia

英文摘要：Large language models leak parametric knowledge of realized outcomes into historical financial decision tasks. Existence is settled; what users lack is a cheap way to audit a given model for it. We present HindsightBench, a black-box behavioral audit protocol that profiles parametric hindsight in any time-indexed LLM decision task at probe-level cost (no backtests, no logprobs, no corpus access). The protocol chains a four-arm date-manipulation matrix (revealed/date-only/masked/transplanted), dual memory probes (date recovery; outcome recall), and six per-model metrics -- trigger strength, transplant effect, post-cutoff placebo, recoverability, behaviorally effective knowledge cutoff, and a recall-accuracy dissociation coefficient -- with explicit gates where identifiability is data-dependent. Applying it to 15 models from seven vendors on a 258-node vintage-correct macro panel yields three headline patterns: (i) the date-trigger reflex tracks training generation, not scale -- absent across the 2024 open-weight generation from 1B to 70B, present in every tested 2026-generation model, and switching on within one vendor lineage (Qwen3 -> Qwen3.6) at fixed MoE architecture and 3B active parameters; (ii) effective cutoffs span 22 months across vendors and precede vendor-reported dates by up to eight months, invalidating calendar-window placebo designs; (iii) audit results are not invariant to serving -- BF16 serving of an FP8-referenced model breaks the trigger estimate's stability while AWQ-INT4 preserves it, and a provider-locked reasoning regime makes one probe non-convergent -- so the protocol ships with operational requirements (pin quantization and thinking regime; disclose parser and sampling policy). We release the panel, frozen preregistrations, per-model audit rows with measured dollar costs, transcripts, and one-command regeneration.

AI 总结：研究神经网络逼近的函数等价性与几何多样性，通过分析单层与多层感知器在有无噪声下的情况，从松散度视角研究几何性质，揭示相关等价类网络特点，还提出基于简约性等的模型选择标准。

链接：https://arxiv.org/abs/2607.18930

作者：Anuragine S A, Prem Jagadeesan

英文摘要：The Universal Approximation Theorem states that a neural network with a single hidden layer is sufficient to approximate any continuous univariate function on a compact domain to arbitrary error. However, the uniqueness of such neural network representations is not guaranteed, raising questions about practical identifiability. In this work, we address this concern by analyzing functional equivalence and geometric diversity of neural network approximations to a few elementary mathematical functions. The analysis includes an extensive study of single-layer neural networks and multilayer perceptrons under noisy and noise-free conditions. Beyond just network capacity, we study the geometric properties through the lens of sloppiness, characterized by the eigen spectrum of the Hessian of the cost function and the effective rank to quantify the dimensionality of parameter space. The study reveals large equivalence classes of functionally indistinguishable yet geometrically diverse networks that consistently exhibit low effective rank and structural redundancy. Finally, a model select criterion is proposed for identifying optimal models based on parsimony, ease of estimation, and inference efficiency.

AI 总结：研究MoE训练中优化器状态存储位置，提出SkewAdam方法，根据参数群体差异分层分配状态，大幅减少内存占用，提升训练效率，验证了优化器状态存储位置对训练效果的重要性。

链接：https://arxiv.org/abs/2607.19058

作者：Nuemaan Malik

英文摘要：Optimizer state is the largest single line item in the memory budget of mixture-of-experts (MoE) training: on a 6.78B-parameter MoE language model, AdamW keeps 50.6 GB of first and second moments to update 12.6 GB of bfloat16 weights. We study SkewAdam, an optimizer built on the observation that the three parameter populations of an MoE - the dense backbone, the experts, and the router - differ enough in size and gradient statistics that they should not receive the same state. SkewAdam keeps float32 momentum plus a factored second moment for the backbone (5% of parameters), a factored second moment alone for the experts (95%), and an exact second moment for the router (<0.01%). The resulting state occupies 1.29 GB, 2.6% of AdamW's, and peak training memory falls from 81.4 GB to 31.3 GB, within the budget of a 40 GB accelerator. In a controlled comparison from identical initializations over 82M tokens, SkewAdam reaches validation perplexity 108.4, ahead of AdamW (126.8), Muon (120.2), and Lion (393.7), and settles router load balance to within 1% of its uniform floor. The allocation is not what earns that perplexity: a tier ablation matches it with twenty times the state, and Adafactor, which shares the factored estimator but drops momentum, plateaus 40 points behind. The tiers buy memory at no cost to accuracy; the accuracy comes from keeping momentum, which a uniform optimizer shares too. Sweeping the baselines' learning rates narrows but does not close the gap: the best tuned AdamW reaches 118.5, tuned Adafactor 139.7. Where optimizer state lives, these results suggest, matters at least as much as how much of it there is.

AI 总结：研究提出基于隐马尔可夫模型的生成状态空间模型及优化框架，能从稀疏观测学习，通过期望最大化算法训练，交替重建与优化，实验验证其可提升海洋建模性能，为地球系统模型提供新途径。

链接：https://arxiv.org/abs/2607.19147

机构：Ocean University of China（中国海洋大学）； School of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）

作者：Yangyang Kong, Yutong Jiang, Yanhai Gan, Junyu Dong, Feng Gao, Xiaopei Lin

英文摘要： Data-driven methods have revolutionized ocean modeling, yet current approaches rely heavily on complete reanalysis datasets, imposing computational constraints and limiting model performance to that of the training data. Here, we present a generative state-space model and an optimization framework that enable learning directly from sparse and noisy observations. The model is essentially a hidden Markov model with a continuous state space, where oceanic physical quantities are treated as hidden states and measurements as observations, enabling a unified representation of ocean fields and observational data. Both the initial-state and state-transition modules are implemented as neural networks to capture the complexity and temporal evolution of ocean states, while the emission module is formulated as a masked Gaussian distribution. To train the model from sparse observations, we derive an optimization framework based on the expectation-maximization (EM) algorithm. The framework alternately reconstructs high-fidelity ocean fields via Langevin dynamics and optimizes deep neural networks to capture temporal evolution. Theoretical analysis shows that the framework maximizes the likelihood of observations under the generative model. For efficiency, we assume that ocean-state evolution follows a stationary, ergodic, and Markovian stochastic process and adopt only length-two state sequences during optimization. Experiments on CMIP6 simulation data and FY-3D satellite data demonstrate high-fidelity reconstruction and accurate prediction, showing that sparse observations can directly improve the model's representation of ocean-state dynamics. This work offers a scalable pathway for next-generation Earth system models to learn directly from sparse, incomplete real-world observations.

AI 总结：研究针对基于IMU传感中数据标注成本高、扩展性受限等问题，提出物理自监督学习范式，通过自适应物理解码器等方法，在无标签情况下实现IMU传感，经实验验证在惯性跟踪和运动捕捉中显著降低误差，优于现有基线。

链接：https://arxiv.org/abs/2607.18361

机构：George Mason University（乔治梅森大学）； Global Technology Applied Research, JPMorgan Chase（摩根大通全球技术应用研究部）

作者：Yuyang Leng, Renyuan Liu, Shaohan Hu, Peijun Zhao, Chun-Fu Chen (Richard), Songqing Chen, Shuochao Yao

英文摘要：Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogeneous devices, placements, and users. Existing unsupervised and self-supervised methods reduce but do not remove this dependence, still requiring labeled data for domain adaptation and largely ignoring known physical structure. We propose physical self-supervised learning, an autoencoder-style paradigm for label-free IMU sensing. We replace the conventional neural decoder with an auto-adaptive physics decoder, a learnable family of kinematic equations that enforces explicit physical structure while adapting across environments, and adopt a hybrid two-stage IMU encoder with reconstruction in a structured latent space to mitigate sensor noise. Our framework further introduces probabilistic frequency-spatial constraints to disentangle sensor and object motion, a multi-view kinematic tree to exploit sparse physical self-supervised signals, and an uncertainty-aware formulation to handle the inherent ambiguity of IMU inference. Evaluated on inertial tracking and full-body motion capture over public datasets and realistic deployments, physical self-supervised learning reduces errors by up to 5x for tracking and 4x for motion capture in challenging generalization scenarios, consistently outperforming state-of-the-art supervised and self-supervised baselines without any labels.

AI 总结：研究在多视图分类中处理噪声标签问题，提出基于全局锚点的标签审核方法GALA，构建全局锚点衡量实例与锚点接近程度，融合评估与置信度得审核分数，据此处理可疑样本、重写标签，实验表明该方法性能优于多种先进方法。

链接：https://arxiv.org/abs/2607.18561

机构：College of Science, Beijing Forestry University（北京林业大学理学院）； School of Software, North University of China（中北大学软件学院）

作者：Yuliang Yang, Hongzhe Zhang, Huiru Wang

英文摘要：In recent years, multi-view learning has attracted increasing attention, as it integrates the complementary information of heterogeneous views. Most existing multi-view classification methods rely on accurate annotations to guarantee performance. However, noisy labels are ubiquitous in practice due to imperfect annotation, and the refinement signals that existing methods derive from models trained on such noisy supervision can gradually lose their reliability. To deal with this problem, we propose a novel Global Anchor-based Label Auditing method (GALA) for multi-view classification to resist the negative impact of noisy labels. Specifically, we construct a global anchor for each class in every view, which aggregates the samples of the whole class and thus offers a stable reference insensitive to individual predictions. Then, each view measures how close an instance is to the anchor of its observed label relative to the nearest competing anchor, and the per-view evaluations are fused with the classifier confidence into a cross-view audit score. Based on the audit scores, suspicious samples are assigned small weights, and an adaptive correction strategy rewrites a label only when the anchor-based candidate agrees with the classifier prediction. Finally, the corrected labels in turn refine the anchors and supervise noise-robust representation learning. Extensive experiments on six datasets demonstrate that GALA outperforms eight state-of-the-art methods, especially under high noise rates.

AI 总结：研究公交信号优先中平衡多目标的问题，提出偏好条件TSP控制器，能在运行时通过偏好参数调整权衡公交与整体交通延误，无需重训练。通过扩展场景生成实现，实验表明该控制器优于基线，保持约束可行性，且能揭示不同偏好下的非公交外部性情况。

链接：https://arxiv.org/abs/2607.18286

作者：Philip-Roman Adam, Stefanie Schmidtner

英文摘要： Transit signal priority (TSP) requires balancing competing objectives: reducing bus delay while limiting adverse impacts on non-bus traffic and avoiding extreme waits for a subset of vehicles. Existing reinforcement-learning (RL) approaches to TSP typically encode transit-aware features (e.g., occupancy and schedule deviation) but optimize a fixed reward or fixed scalarization, which limits operational flexibility when agency priorities change across time-of-day or disruption conditions. We present a preference-conditioned TSP controller, $\pi(a \mid s,w)$, that selects the next signal phase under minimum/maximum green and transition-feasibility constraints and can be tuned at runtime via a preference parameter $w$ to trade off bus-priority emphasis against overall traffic delay without retraining. We implement this on top of IntersectionZoo by introducing a constrained signal-control/TSP wrapper, and we extend scenario generation with bus-prevalence augmentation and timetable-based bus insertion to address sparse transit-priority events during training. Experiments against fixed-time control, a rule-based TSP overlay, and fixed-weight PPO specialists show that a single learned conditioned policy spans a smooth empirical trade-off frontier across runtime preferences, outperforms fixed-time and rule-based baselines, and maintains constraint feasibility, while tail-delay diagnostics reveal that non-bus externalities remain limited for moderate preference settings but can increase substantially under high bus-priority weights. The source code of this work is available at this https URL.

AI 总结：针对边缘云网络负载不平衡致延迟性能下降问题，提出联合服务放置等的JSCP问题。利用决策动态差异分解问题，构建双时间尺度多层深度强化学习框架2T-MDRL-LA，引入潜行动作表示，有效优化相关内容，相比传统方法性能更优且收敛更快。

链接：https://arxiv.org/abs/2607.18288

作者：Vo Phi Son, Van-Dinh Nguyen, Ngoc Hung Nguyen, Trinh Van Chien, Symeon Chatzinotas

英文摘要：Load imbalance across edge and cloud layers degrades latency performance in hierarchical edge-cloud computing (HECC) systems under dynamic task arrivals and heterogeneous resources, leading to severe queuing delays and inefficient resource utilization. To address this challenge, we study a joint service placement, computational delegation, and power control (JSCP) problem to minimize the average end-to-end (e2e) latency. The resulting JSCP problem is a mixed-integer nonconvex and NP-hard optimization problem due to the strong coupling between discrete and continuous variables. To enable tractable optimization and stable system adaptation, we exploit the inherent difference in decision dynamics and decompose the problem into long-term system configuration and short-term resource allocation subproblems. Based on this formulation, we propose a two-timescale multi-layer deep reinforcement learning framework with a latent action space (2T-MDRL-LA) to jointly optimize service placement, user association, computational delegation, task offloading, and user transmit power. A latent action representation based on a variational autoencoder is introduced to efficiently compress the high-dimensional combinatorial action space. Simulation results demonstrate that the proposed framework effectively adapts to dynamic network conditions and achieves near-optimal performance compared to branch-and-bound solutions. It achieves up to a 20.8% reduction in average e2e latency and a 13% improvement in resource utilization over the scheme without the computational delegation, while converging approximately 50% faster than conventional proximal policy optimization.

AI 总结：研究针对实时模型训练更改需特定代码的问题，提出交互式训练2这一开源控制平面，通过共享协议指导训练，经五个工作流程演示，其代码和跟踪为可审计训练提供了可重用基础。

链接：https://arxiv.org/abs/2607.18314

机构：University of Waterloo（滑铁卢大学）； University of Wisconsin-Madison（威斯康星大学麦迪逊分校）

作者：Wentao Zhang, Xuanhe Pan, Han Zhou, Yang Lu, Yuntian Deng

英文摘要：Experiment trackers show how training is progressing, but changing a live run still usually requires trainer-specific code. We present Interactive Training 2, an open-source control plane for steering training through a shared protocol. Training applications declare which settings and actions they expose, humans and automated controllers submit requests through the same interface, and the training loop validates and applies them at safe control points. A customized Aim workspace combines live metrics and controls with a chronological record of requests and outcomes. We demonstrate the system across five NLP and reinforcement-learning workflows. The released code and traces provide a reusable foundation for auditable human- and agent-guided training.

AI 总结：研究表明可学习新奇性产生智能不同投影，基于廉价可微储层计算机给出闭式估计器。此估计器无监督可恢复复杂度分类，作目标能推动神经细胞自动机发展，作奖励可提升强化学习智能体表现，让智能相关研究有了共同定量基础。

链接：https://arxiv.org/abs/2607.18433

机构：Allen Discovery Center at Tufts University（塔夫茨大学艾伦发现中心）； Wyss Institute for Biologically Inspired Engineering at Harvard University（哈佛大学威斯生物启发工程研究所）

作者：Yanbo Zhang, Michael Levin

英文摘要：Intelligence appears under different names in different fields: as data compression in statistics and machine learning, as universal computation in dynamical systems, and as adaptive behavior in agents. Each field carries its own objective, and the two most influential drives often fail in mirror image: novelty search, which seeks surprise, is transfixed by a noisy television screen, while the free-energy principle, which avoids surprise, is most content in a dark room. Both failures have a single cause: each objective treats as one quantity the surprise a learner can convert into knowledge and the surprise it never can. Here we show that the learnable part of that information, which we call learnable novelty, yields the seemingly disparate projections of intelligence, and we give a closed-form estimator of it built on a cheap and differentiable reservoir computer. Used as a measure, with no supervision of any kind, the estimator recovers decades of complexity classification, ranking the Turing-complete rule~110 highest among the elementary cellular automata. Used as an objective, its gradient carries a neural cellular automaton from simple dynamics into a regime of solitons, the traveling, colliding structures by which rule~110 computes, as well as organizes the representation of an image encoder around the ten digit classes of MNIST, fully unsupervised: no label ever enters training. Handed to a reinforcement-learning agent as an intrinsic reward, it supplies the exploration that task rewards lack, improving on the task baseline in nine of ten environments and collapsing in none. Complexity generation, abstraction, and exploration, ordinarily pursued with unrelated objectives in separate fields, thus emerge from ascent on one differentiable quantity, and the projections of intelligence gain a common quantitative footing.

AI 总结：研究提出RRPO，通过参考相对对比比较推广GRPO。用分层条件展开构建锚集，训练投影头定义对比优势，在策略优化中评估。不依赖任务真实验证器，在多种设置下有竞争力，优于弱监督基线，监督微调后有额外收益。

链接：https://arxiv.org/abs/2607.18470

机构：University of California San Diego（加利福尼亚大学圣地亚哥分校）； Adobe Research（Adobe研究院）

作者：Yuxin Xiong, Xunyi Jiang, Rohan Surana, Xintong Li, Sheldon Yu, Nikki Lijing Kuang, Ryan A. Rossi, Jingbo Shang, Tong Yu, Julian McAuley, Junda Wu

英文摘要：Group Relative Policy Optimization (GRPO) has shown strong effectiveness in reinforcement learning from verifiable feedback, where sampled rollouts can be compared within a group using task-provided correctness signals. However, extending group-relative optimization beyond verifiable settings is challenging because success in many tasks is not captured by a single correctness criterion. We propose \textbf{Reference-Relative Policy Optimization (RRPO)}, which generalizes GRPO by replacing direct correctness-based advantage construction with reference-relative contrastive comparisons. RRPO first uses \emph{stratified conditional rollouts} to construct positive and negative anchor sets, and then trains a metric projection head with a set-contrastive objective to compare candidate rollouts against these anchors. The resulting alignment scores directly define contrastive advantages: during policy optimization, the projection head is frozen, and the scores are centered within each rollout group in a standard group-relative objective. We evaluate RRPO using anchor-based contrastive advantages throughout policy optimization, without relying on task ground-truth verifiers. Across verifiable reasoning, open-ended generation, and post-SFT settings, RRPO remains competitive with verifier-based optimization, improves over weakly supervised baselines, and provides additional gains after supervised fine-tuning.

AI 总结：研究无模型强化学习中规划作为涌现行为的现象，发现神经架构的隐藏状态结构是决定因素，关系隐藏状态网络能获规划机制，恢复环境转移结构并改进策略，解释了涌现规划现象并引发相关问题。

链接：https://arxiv.org/abs/2607.18589

机构：ETH Zurich（苏黎世联邦理工学院）

作者：Armin Sommer

英文摘要：Reinforcement learning is conventionally divided into model-based and model-free methods. In this taxonomy, model-based methods perform lookahead planning over a learned world model, whereas model-free methods learn a reactive state-action mapping. Recent work, however, has shown that planning can emerge from model-free reinforcement learning alone. The conditions under which this behavior emerges from a pure reward-maximization objective have so far remained unclear. In this paper, we present evidence that, in the observed cases, the hidden-state structure of the neural architecture is the deciding factor. We find that a network of relational hidden states, each anchored to an environment state and exchanging messages along learned relations, acquires a planning mechanism. These hidden states recover the environment's transition structure in their learned relations, and improve the policy at decision time by planning over the learned graph. In a matched control agent that must additionally discover which cells represent which states, no such binding arises, and no planning follows from it. We argue that this explains the observed phenomenon of emergent planning in model-free reinforcement learning and raises the question of how common such emergent planning might be more generally. Finally, we hypothesize that the discovered mechanism could describe how planning emerges from pure reward maximization in the human brain through a neural architectural prior.

AI 总结：研究连续动作空间合作任务中反事实信用分配难题，提出SAFE框架，利用自进化默认动作构建反事实基线，解决偏差和收敛问题，实验证明其性能优于现有模型。

链接：https://arxiv.org/abs/2607.18597

机构：XJTLU（西交利物浦大学）

作者：Shuangyao Huang

英文摘要：Counterfactual credit assignment has proven effective in multi-agent reinforcement learning (MARL) for discrete action spaces, yet its extension to continuous-action cooperative tasks remains challenging. Existing methods that approximate the counterfactual baseline via Monte Carlo sampling often introduce bias into policy gradients and fail to guarantee convergence to local optima, as the sampled actions may not have been sufficiently trained. To address these limitations, we propose SAFE, a novel MARL framework that employs a counterfactual baseline conditioned on a self-evolving default action sampled from each agent's experience buffer. This design naturally extends to continuous action spaces without relying on additional simulations, reward models, or environment-specific prior knowledge. The baseline accurately quantifies each agent's contribution, and introduces no bias into the deterministic policy gradient, ensuring convergence to local optima. Extensive experiments on cooperative vehicular tasks demonstrate that SAFE consistently outperforms state-of-the-art models.

AI 总结：研究排序学习的强化学习方法，针对标准RL在LTR中低效及现有方法复杂的问题，通过聚焦方差减少和GPU计算，提出新的梯度估计抽象，实现与自动微分无缝集成，实验证明新方法收敛快、性能高且无额外计算成本。

链接：https://arxiv.org/abs/2607.18689

机构：University of Amsterdam（阿姆斯特丹大学）； Google DeepMind（谷歌深度思维）

作者：Harrie Oosterhuis, Rolf Jagerman, Zhen Qin, Xuanhui Wang

英文摘要： Reinforcement learning (RL) methods for learning-to-rank (LTR) can optimize (almost) any ranking goal, e.g., from precision or discounted cumulative gain to fairness-of-exposure or ranking distillation. However, standard RL is ineffective and computationally costly due to the enormous action space in LTR settings. Existing methods reach computational efficiency through custom gradient computation algorithms, but they are very complex to implement and often clash with auto-differentiation. Consequently, existing RL for LTR is not attractive to many practitioners. We reconsider RL for LTR while actively avoiding reliance on custom gradients. Contrary to the existing approaches, we focus on variance reduction and GPU computation. In doing so, we discover that high sample-efficiency can be reached through baseline corrections and partial marginalization. Furthermore, we propose an abstraction that places gradient estimation behind a document-exposure distribution, this enables seamless plug-and-play integration with auto-differentiation. Thereby, one only has to implement a loss as a differentiable function of exposure and RL for LTR can optimize it using auto-differentiation. Our experimental results reveal that our new exposure-based RL for LTR approach converges considerably faster and at significantly higher ranking performance than existing custom gradients, with no additional costs in computation time when using GPUs. In contrast, existing custom gradients result in severe stability issues when converging over many epochs, which never occur for our methods. Thus, we considerably improve RL for LTR methodology by increasing its effectiveness, efficiency, and ease of application.

AI 总结：研究针对异步强化学习中陈旧问题，提出陈旧自适应信赖域（SAT）方法，利用分离采样对数比率等技术识别高不匹配尾部并收缩PPO区间端点，实验表明该方法有效稳定异步强化学习，在特定设置下取得较好结果。

链接：https://arxiv.org/abs/2607.18722

作者：Junyao Yang, Yucheng Shi, Zongxia Li, Zhongzhi Li, Ruhan Wang, Xiangxin Zhou, Kishan Panaganti, Haitao Mi, Leowei Liang

英文摘要：Asynchronous reinforcement learning improves throughput by decoupling rollout generation from optimization, but staleness is an inevitable byproduct compounded by policy lag, engine delays, and mixture-of-experts routing. From a trust-region perspective, this mismatch is critical: training-inference divergence governs approximation error in finite-horizon bounds, whereas PPO clipping only gates sampled outward updates, acting as a sampled surrogate rather than a full-policy constraint. As a result, high-staleness updates remain weakly controlled in the asynchronous regime where stale rollouts matter most. We introduce the Staleness-Adaptive Trust Region (SAT), which uses the detached sampled log-ratio as a practical staleness proxy, identifies high-mismatch tails within each batch via staleness-based kernel scaling, and contracts only the sign-selected endpoint of the nominal PPO interval. This preserves baseline behavior on ordinary tokens while enforcing more conservative updates on newly intercepted outward bands. We prove local interval containment and pointwise pessimism relative to PPO, showing how the adaptive rule reshapes update geometry under heterogeneous staleness. We evaluate SAT in a decoupled asynchronous RL setup built on Qwen3-30B-A3B-Base, using SGLang as the inference engine and Megatron for training. In this setting, SAT-GSPO w/ R3 achieves the best observed AIME24 avg@8, reaching 35.83 at lag 1 and 34.79 at lag 8, while SAT-GSPO reaches 34.17 at lag 1. Adaptive clipping and routing replay act as complementary stabilizers targeting mismatch tails and routing inconsistency, respectively. Overall, aligning clip intervals with staleness heterogeneity effectively stabilizes asynchronous RL.

AI 总结：研究如何改进强化学习中模型无关元学习的内循环适应机制，提出LA - MAML，利用任务语言指令替代传统的轨迹收集和梯度更新，实验证明其在降低训练时间的同时性能相当或更优。

链接：https://arxiv.org/abs/2607.18830

机构：International Institute of Information Technology Bangalore（班加罗尔国际信息技术学院）

作者：Garvit Singla, Uma Maheswari Natarajan, Raghuram Bharadwaj Diddigi

英文摘要：Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning global policy parameters that can be rapidly adapted to new tasks. MAML training proceeds in two loops: an inner loop where the global parameters are adapted to task-specific parameters, and an outer loop where these task-specific parameters are evaluated and losses are back-propagated to improve the global parameters. Traditionally, the inner loop adaptation is performed by collecting trajectories from the task environment and applying gradient updates on the empirical expected return, which can be a costly operation. We note that it is the outer loop that drives the actual learning of global parameters, and therefore the inner loop adaptation mechanism need not be restricted to be gradient-based. This observation leads us to ask: Can we replace the inner loop trajectory collection and gradient update with a simpler, task-specific signal? In many practical settings, tasks are naturally accompanied by language instructions. Leveraging these instructions as a direct task-specific signal, we propose LA-MAML (Language Adapted MAML), which modifies the inner loop by adapting the global policy parameters in a single step through a learned embedding of the task instruction, replacing the inner loop trajectory collection and gradient-based updates. Experiments on the BabyAI benchmark demonstrate that LA-MAML achieves competitive or improved performance compared to baselines at a significantly lower per-iteration wall-clock training time. These results demonstrate that language instructions are an effective and efficient substitute for trajectory-based inner loop adaptation in meta RL.

AI 总结：研究动态环境中基于送货无人机的参与式传感问题，提出双时间尺度强化学习框架TSRL，该框架分宏观和微观两层协作决策，实验表明其显著优于基线，在杭州和上海分别提升系统利润20.1%和46.6%。

链接：https://arxiv.org/abs/2607.18874

机构：The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））； Beijing Institute of Technology（北京理工大学）

作者：Xin Ouyang, Songxin Lei, Xusen Guo, Yutian Jiang, Sijie Ruan, Yuxuan Liang

英文摘要：Using Unmanned Aerial Vehicle (UAV) for urban sensing has emerged as a powerful paradigm to monitor the status of the city, e.g., air quality and noise levels, through agile aerial crowdsourcing. Despite this potential, existing UAV-based sensing approaches overlook environmental disturbances like wind that drastically impact drone velocity and energy efficiency. Consequently, directly applying existing methods to this joint delivery and sensing paradigm in dynamic environments faces two severe challenges: (1) scalability bottlenecks as fleet sizes expand; and (2) multi-timescale decision heterogeneity between macro task dispatching and micro velocity control. To tackle these, we formalize the problem as SensUAV and propose a Two TimeScale Reinforcement Learning framework (TSRL). Specifically, TSRL separates decision-making into two cooperative layers. At the macro level, a task-embedding sensing dispatcher handles scalability by separately encoding distinct task features and sequentially evaluating UAV suitability before task selection. At the micro level, a wind-aware velocity controller learns fine-grained velocity scheduling to adapt to dynamic environmental variations. Extensive experiments on real-world datasets demonstrate that TSRL significantly outperforms baselines, achieving average system profit improvements of 20.1% in Hangzhou and 46.6% in Shanghai.

AI 总结：研究指出电路提取中保留行为不能唯一确定电路，其声明依赖提取及比较方式。通过合成基准测试，在不同检查点和证明类型下改变提取对象等设置，发现边重叠敏感，总结有稳定指标，强调明确提取及比较要素对电路声明的重要性并提炼报告实践。

链接：https://arxiv.org/abs/2607.18921

机构：Fudan University（复旦大学）； Shanghai Innovation Institute（上海创新研究院）； IQuest Research（IQuest研究公司）

作者：Yang Sheng, Jie Fu

英文摘要：Circuit extraction identifies a small set of model components whose presence preserves a target behavior under ablation, and the resulting circuit is often read as the mechanism behind that behavior. We argue that this reading is under-determined: preserving behavior does not single out one circuit, because the claim it supports depends on which circuit is reported and how two circuits are compared. We make this concrete in a synthetic Lean tactic-prediction benchmark -- predicting the next step of a proof -- where fixed proof rules with randomized surface form let differences between extracted circuits be attributed to these choices rather than to the task. Across dense and weight-sparse checkpoints (most weights constrained to zero) of the same transformer, evaluated on atomic (single-rule) and compositional (multi-rule) proofs, we vary which extracted object is reported (a compact prediction-preserving circuit, a broader graph that also keeps surrounding read, write, and routing structure, or the smallest subgraph meeting a post-ablation loss threshold), and whether each attention head's query and key are represented jointly or separately. Exact component-to-component edge overlap is low and sensitive to these choices, at times dropping to a random baseline, while two coarser summaries stay stable: the set of selected attention heads, and the circuit-size ranking of conditions that differ in which supervised checkpoint initializes reinforcement learning (RL). The largest accuracy gains from RL on compositional proofs come with the most structure beyond the atomic circuits. A circuit-level claim is therefore well defined only once one states which circuit is reported, the pruning threshold used to extract it, and the level at which circuits are compared. We distill these requirements into a reporting practice for circuit-extraction studies.

AI 总结：研究针对强化学习中奖励监督问题，提出 H$^2$SD 混合事后自蒸馏框架。成功轨迹用教师概率调更新幅度，失败轨迹基于参考提示调整教师并最小化反向 KL 散度，实验表明该框架优于基线，能稳定优化且效率良好。

链接：https://arxiv.org/abs/2607.18955

机构：Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）； Harbin Institute of Technology（哈尔滨工业大学）； Fudan University（复旦大学）； The Chinese University of Hong Kong（香港中文大学）

作者：Qiye Cai, Yichuan Ma, Linyang Li, Peiji Li, Yongkang Chen, Qipeng Guo, Yicheng Zou, Tao Gui, Xiaocheng Feng, Bing Qin

英文摘要：Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models on tasks such as mathematical reasoning and code generation. However, most RLVR methods assign a scalar outcome reward to an entire trajectory, resulting in sparse supervision and limited token-level credit assignment. On-policy distillation (OPD) provides denser supervision by distilling token-level distributions from a stronger teacher model, but requires an additional teacher and typically assumes a shared vocabulary. On-policy self-distillation (OPSD) removes this dependency by conditioning the same model on privileged information to construct a teacher policy. However, directly matching the teacher distribution may cause information leakage and unstable optimization. RLSD avoids direct matching by using the teacher signal only to modulate update magnitudes, but it cannot provide an explicit correction direction when the sampled reasoning fails. To address this tradeoff, we introduce $\mathrm{H}^{2}\mathrm{SD}$, a hybrid hindsight self distillation framework that uses the teacher differently according to trajectory correctness. For successful trajectories, the teacher receives the student response confirmed as correct together with a rephrasing instruction, and its probabilities on the original response tokens are used to modulate update magnitudes without changing the direction determined by the reward. For failed trajectories, we condition the teacher on a reference hint containing key reasoning steps and a verified answer, and minimize the reverse KL divergence from the student to the teacher. Experiments on multiple challenging reasoning benchmarks show that H$^2$SD consistently outperforms representative RLVR, OPSD, and RLSD baselines while maintaining stable optimization and favorable generation efficiency.

AI 总结：研究利用大语言模型进行分子生成时面临的问题，提出LLMol框架，采用监督学习与强化学习结合的两阶段训练范式，引入RLVR及GRPO算法，有效处理多种分子设计任务，实验证明其性能优于现有方法。

链接：https://arxiv.org/abs/2607.19044

作者：Mingxuan Ouyang, Hao Lan, Wanyu Lin

英文摘要：Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design. Current methods primarily rely on supervised training or fine-tuning with limited datasets, which are insufficient to capture complex molecular design objectives. While some approaches attempt to guide generation toward specific goals, they often lack direct optimization mechanisms, making it difficult to align generated molecules with desired properties. To tackle these challenges, we propose \textbf{LLMol}, a principled reinforcement learning framework that directly incorporates verifiable rewards for targeted molecule generation. The key insight is to formulate molecular design as a goal-conditioned sequence prediction task, where verifiable rewards serve as explicit supervision to drive generation toward desired objectives. LLMol follows a two-stage training paradigm combining supervised learning and reinforcement learning. In the first stage, large language models are supervised fine-tuned to capture chemical syntax and molecular distributions. In the second stage, we introduce Reinforcement Learning with Verifiable Rewards (RLVR), which directly integrates property-based reward signals to guide molecular generation toward task-specific objectives. To address the high variance and instability common in discrete sequence optimization, we adopt Group Relative Policy Optimization (GRPO), a stable on-policy algorithm that smooths reward signals and improves training robustness. This framework enables LLMol to effectively handle a range of molecular design tasks, including single-property targeting (e.g., penalized logP, QED) and structure-constrained optimization. Experimental results demonstrate that LLMol consistently outperforms existing methods, achieving higher success rates and improved efficiency across diverse molecular benchmarks.

AI 总结：针对离线强化学习面临的挑战，提出不确定性估计下的保守查询与自适应正则化框架，通过莫尔斯网络估计不确定性，采用保守查询策略与自适应正则化方案，与CQL集成并在D4RL基准测试，取得优异或有竞争力的性能。

链接：https://arxiv.org/abs/2607.19199

机构：Nanjing University of Aeronautics and Astronautics（南京航空航天大学）

作者：Li-Rong Zhou, Qin-Wen Luo, Sheng-Jun Huang

英文摘要：Offline reinforcement learning (RL) aims to learn an effective policy from a static dataset, but its performance is fundamentally limited by dataset coverage. Action preference queries leverage expert feedback without additional environment interaction, enabling policy improvement during offline training. However, existing methods still face two key challenges: selecting informative preference queries and effectively exploiting the collected feedback. Current approaches typically rely only on the distance between policy actions and dataset actions for query selection, while enforcing fixed constraints that keep the policy close to queried preferences. Such strategies often lead to unstable policy updates and integrate poorly with value regularization. To address these limitations, we propose Conservative Query and Adaptive Regularization under Uncertainty Estimation, a lightweight framework that jointly improves preference querying and preference exploitation. Specifically, we employ a Morse network to estimate the uncertainty of policy actions with respect to the offline dataset. Based on this uncertainty, we introduce a conservative query strategy that selectively queries actions near the dataset to preserve Bellman-update stability, together with an uncertainty-aware adaptive regularization scheme that dynamically adjusts data-level constraints during policy optimization. We integrate our framework with CQL and evaluate it extensively on the D4RL benchmark. Experimental results demonstrate superior or competitive performance across a wide range of tasks.

AI 总结：研究分层强化学习中高层智能体子目标选择问题，提出用粗粒度动力学提供动力学感知内在动机，通过最小化预测不确定性稳定高层策略，经实验验证该方法在非平稳长期环境中表现优于现有方法。

链接：https://arxiv.org/abs/2607.19232

机构：University of Massachusetts, Lowell（马萨诸塞大学洛厄尔分校）

作者：Kshitij Kumar Srivastava, Kshitij Jerath

英文摘要：Hierarchical Reinforcement Learning (HRL) intends to separate strategic planning from primitive execution. It has been widely successful in solving long-horizon and complex tasks, where flat-RL algorithms have difficulty in learning. However, while the low-level agent in HRL benefits from dense feedback and abundant trial opportunities, the high-level agent receives sparse, delayed feedback from the environment and its performance depends on the low-level execution capability. In this paper, we study whether subgoal selection by the high-level agent can be performed more strategically, by providing it with dynamics-aware intrinsic motivation. Since motivation based on primitive transition dynamics would require broad coverage of the state-action space, we propose to use coarse dynamics, i.e., environment transitions aggregated over multiple steps at the temporal scale at which the high-level agent operates. This approach stabilizes the high-level policy by learning to minimize the predictive uncertainty associated with the coarse dynamics, and provides a guided structure for navigation. We model the predictive uncertainty by evaluating different dispersion metrics as approximated by a Mixture Density Network (MDN). Empirically, we observe that a dense, dynamics-aware intrinsic reward leads to risk-averse subgoal selection, enabling it to outperform state-of-the-art HRL methods in non-stationary long-horizon environments.

AI 总结：研究提出强化学习框架预测燃气轮机燃烧室贫油熄火，采用多阶段聚类-分类策略，借助初始聚类和演员-评论家RL智能体生成优化反应器区域，验证研究显示该框架预测保真度高、速度快，有潜力作为降阶建模技术辅助高保真模拟。

链接：https://arxiv.org/abs/2607.19281

机构：Louisiana State University（路易斯安那州立大学）； Argonne National Laboratory（阿贡国家实验室）

作者：Philip John, Eloghosa Ikponmwoba, Pinaki Pal, Opeoluwa Owoyele

英文摘要：This study introduces a reinforcement learning (RL) framework for generating optimal liquid-fueled reactors to improve lean blowout (LBO) predictions in gas turbine combustors. Existing approaches for determining cluster boundaries rely on manual heuristics or distance-based metrics in the input space. In contrast, the proposed method is goal-oriented, explicitly accounting for the target metric (e.g., LBO prediction accuracy) during cluster formation. The framework employs a multi-stage clustering--classification strategy: an initial clustering step (e.g., $k$-means clustering) generates a large set of homogeneous micro-clusters, followed by an actor-critic RL agent that merges them into optimal reactor zones. The validation study, performed using a Jet-A mechanism (119 species, 841 reactions), shows the RL framework offers improved predictive fidelity compared to $k$-means and captures the correct LBO trends, while achieving substantial speedups relative to the high-fidelity computational model. Overall, the RL-driven approach demonstrates strong potential as a computationally efficient reduced-order modeling technique that can complement high-fidelity simulations for rapid design-space exploration.

AI 总结：研究利用特权信息解决强化学习难题，提出脱离上下文的广义信赖域策略优化算法（OC - GRPO），通过引导展开和重要性校正目标避免训练不匹配，在标准数学推理基准上比普通GRPO有显著提升且成本低。

链接：https://arxiv.org/abs/2607.19313

机构：Meta AI（元人工智能公司）； Columbia University（哥伦比亚大学）

作者：Priyank Agrawal, Ankur Samanta, Shervin Ghasemlou, Jalaj Bhandari, Kavosh Asadi, Daniel Jiang, Aditya Modi

英文摘要： Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models. Yet, typical RLVR approaches fail on difficult problems: when a model cannot generate any correct solutions, it receives \textit{zero} learning signal. Providing privileged guidance during training, such as solution prefixes, can help overcome this learning cliff by steering the model towards {correct solutions with non-zero reward}. {We call these rollouts \textit{off-context}: they are generated from a training prompt that contains privileged guidance, while the target objective is defined by the original prompt without that guidance.} {We introduce} Off-Context GRPO (OC-GRPO), a minimally modified variant of GRPO that uses guided rollouts but applies an importance-corrected objective to steer the update back toward the original unguided objective, avoiding the mismatch that destabilizes uncorrected guided training. Empirically, our algorithm achieves a 3.9\% absolute improvement (13.8\% relative gain) over vanilla GRPO on average across standard mathematical reasoning benchmarks with negligible additional cost.

AI 总结：研究 RLVR 中缺失的优化层，提出等谱优化（ISO）框架，包括离线的 ISO-Merger 和在线的 ISO-Optimizer。通过光谱继承，在推理和编码任务中，用更少训练步骤提高准确率，为 RLVR 优化层问题提供了具体解决方案。

链接：https://arxiv.org/abs/2607.19331

机构：The University of Texas at Austin（德克萨斯大学奥斯汀分校）； UIUC（伊利诺伊大学香槟分校）； Emory University（埃默里大学）； Together AI（联合人工智能公司）； Recursive Superintelligence Inc（递归超级智能公司）； ELLIS Institute Tübingen（图宾根埃利斯研究所）

作者：Hanqing Zhu, Wenyan Cong, Zhizhou Sha, Sagnik Mukherjee, Xinyuan Song, David González-Martínez, Xiaoxia Wu, Yuandong Tian, Shiwei Liu, David Z. Pan, Zhangyang "Atlas" Wang

英文摘要：Reinforcement learning with verifiable rewards (RLVR) is rapidly advancing the reasoning capabilities of language models, yet the optimization layer that converts reward feedback into weight-space updates remains poorly understood. Building on our prior analysis (Zhu et al., 2025), we study this missing layer through the singular structure of model weights and identify spectral inheritance: RLVR can reuse the base model's weight spectra while acquiring new behavior through changes in the associated input and output singular frames. We operationalize spectral inheritance as Isospectral Optimization (ISO), an RLVR-native, fixed-spectrum optimization framework with complementary offline and online instantiations. Offline, ISO-Merger combines the frame changes of shared-base specialists into a single fixed-spectrum model, requiring no post-merge data, rollouts, gradient updates, or on-policy distillation (OPD). It recovers complementary specialist capabilities and achieves the strongest aggregate performance among the compared data-free merging methods. Online, ISO-Optimizer applies a chosen base optimizer, including AdamW and Muon, to the frame variables while keeping the base spectra fixed. Across reasoning and coding tasks ranging from 1.5B to 8B parameters, ISO-Optimizer improves accuracy in the reported runs and reaches matched scores with substantially fewer training steps. On Qwen3-8B-Base, AdamW reaches an aggregate accuracy of 0.495 after 270 training steps. ISO-AdamW reaches the same accuracy after only 100 training steps and improves further to 0.509 after 210 training steps. Together, ISO offers a concrete answer to RLVR's missing optimization layer: rather than inheriting pre-training optimization wholesale, design post-training around the structure of reward-driven adaptation: inherit the spectrum, optimize the frames.

AI 总结：研究供应链规划中供应商提前期预测问题，提出审查感知上下文学习模型LeadTime-ICL，结合Transformer与条件归一化流头，经预训练可适应新数据集，在多数据集上评估表现优异，为工业规划提供准确低成本预测。

链接：https://arxiv.org/abs/2607.18530

机构：Queen’s University（女王大学）； Kinaxis Inc.（Kinaxis公司）

作者：Christopher Wang, Sebastien Ouellet, Behrouz Haji Soleimani, Ali Etemad

英文摘要：Supplier lead time forecasting is a central input to material requirements planning, inventory optimization, and supply chain risk management. However, many industrial lead time datasets are naturally right-censored: at the time forecasts are required, some orders have not yet arrived. Standard regression and classification approaches discard this information, while conventional survival models require task-specific modeling. We propose LeadTime-ICL (LT-ICL), a censoring-aware in-context learning model for probabilistic lead time forecasting. LT-ICL combines a transformer backbone with a conditional normalizing-flow head, producing a full predictive distribution over lead times. The model is pretrained on synthetic right-censored lead time tasks, enabling in-context adaptation to new industrial datasets without task-specific parameter updates. We provide theoretical support for this formulation by showing that excess CRPS is bounded by prior misspecification and amortized approximation errors, providing clear direction for improving forecasting performance. We evaluate LT-ICL on 24 proprietary supply-chain datasets spanning seven industries. LT-ICL achieves the lowest point-forecasting error on 15 of the 24 datasets, and the lowest probabilistic forecasting error on 14 datasets, yielding the best average rank across both. These results support right-censored probabilistic forecasting as a practical formulation for supplier lead time prediction and demonstrate that pretrained in-context models can provide accurate, low-adaptation-cost forecasting for industrial planning systems.

AI 总结：研究基于QUBO的机器学习公式级自动调优，通过Optuna框架解决多量子启发退火器上SVM参数联合选择问题，有内外两个优化级别，经实验对比传统网格搜索，在分类任务中平均增益显著，表明要联合评估公式与后端能力。

链接：https://arxiv.org/abs/2607.18774

作者：Naoya Mizuki, Takahiro Katagiri, Daichi Mukunoki, Tetsuya Hoshino

英文摘要：This paper presents an Optuna-based formulation-level auto-tuning framework for support vector machines (SVMs) implemented on multiple quantum-inspired annealers. In an annealing-based SVM, continuous dual variables are discretized and converted into a quadratic unconstrained binary optimization (QUBO) model. This transformation introduces three coupled classes of parameters: representation parameters-the encoding base B and bit depth K-which determine numerical range, resolution, and QUBO size; the RBF kernel parameter {\gamma}, which determines classifier geometry; and the equality-constraint penalty {\xi}, which controls feasibility and coefficient balance. We formulate their joint selection as a mixed discrete-continuous black-box optimization problem. The framework has two optimization levels: an inner annealer minimizes the generated QUBO, while an outer Optuna loop reconstructs the formulation in every trial and maximizes validation accuracy. The same solver-agnostic procedure is applied to Fixstars Amplify Annealing Engine, Toshiba SQBM+, and Fujitsu Digital Annealer using TPE and Gaussian-process samplers and is compared with conventional grid search. Experiments on linear and nonlinear classification tasks with 0-20% label noise show mean gains over grid search of approximately 0.8 and 2.1 percentage points, respectively. The results demonstrate that formulation quality and backend capability must be evaluated jointly and that task-level feedback can compensate for discretization, penalty imbalance, and backend-dependent approximate optimization.

AI 总结：研究预测性维护中罕见故障检测，提出故障类型感知生成增强程序，采用专门多生成器GAN架构，通过防泄漏实验设计比较多种不平衡处理方法，实验表明该框架能产生更真实少数样本，提升PR-AUC和召回分数。

链接：https://arxiv.org/abs/2607.19153

作者：Alexis Lazanas, Georgios Kampouropoulos

英文摘要：Supervised learning models in the predictive maintenance field are regularly trained on highly imbalanced industrial datasets: machine failures occur rarely but have a disproportionate effect on operations. In addition to the clear class disparity, failure data are typically non-homogeneous, with different failure modes arising from distinct physical processes and exhibiting a multimodal distribution across minorities and classes. Traditional imbalance-management methods, e.g., undersampling, SMOTE-based interpolation, or cost-sensitive learning, typically assume that the minority population is homogeneous. This means their effectiveness is severely limited in the multifaceted conditions encountered in industrial practice. This paper determines the possibility of a failure-type-conscious generative augmentation program to improve the identification of infrequent failures in predictive maintenance systems. An experimental design that is leakage-safe is used to compare five imbalance-handling methods: cost-sensitive learning, random undersampling, SMOTE oversampling, single-generator GAN augmentation, and a specialized multi-generator GAN architecture that has independent generators that are asked to learn individual failure subtypes. Precision/Recall-oriented measures are used to quantify model performance; the main evaluation measure is the PR-AUC. Experiments conducted on the AI4I 2020 predictive maintenance dataset indicate that the proposed multi-generator GAN framework produces more realistic minority samples, yielding higher PR-AUC and recall scores compared to traditional resampling methods and individual-generator GAN augmentation.

AI 总结：研究旨在利用结构预测模型设计高亲和力小分子。核心方法是引入DBMol框架，经交替优化和投影过程，结合结构预测模型优化分子。主要贡献是有效优化亲和力代理，提高口袋覆盖率，保持分子多样性，在无参考配体监督下有竞争力。

链接：https://arxiv.org/abs/2607.19237

机构：EPFL（洛桑联邦理工学院）； MRC-LMB（医学研究委员会分子生物学实验室）； University of Cambridge（剑桥大学）

作者：Yiming Qin, Kai Yi, Miruna Cretu, Sjors H.W. Scheres, Pietro Liò, Pascal Frossard

英文摘要：Designing small molecule ligands that bind with high affinity to specific protein pockets is a fundamental goal in drug discovery, as small molecules constitute a major fraction of approved therapeutics. Recent breakthroughs in structure prediction, such as AlphaFold-3 and Boltz-2, enable accurate biomolecular interaction prediction and show promise as foundation models for downstream tasks, including binding affinity prediction. We propose to leverage these models and introduce DBMol, a new structure predictor-guided framework for de novo small molecule design. DBMol formulates an alternating optimization and projection process. In the optimization stage, DBMol starts from an initial molecule and uses gradient-based optimization to improve pocket-specific interactions and predicted binding affinity using a structure prediction model. In the projection stage, a flow-matching model maps the optimized molecular graph to discrete and chemically valid molecules. Experiments show that DBMol effectively optimizes the Boltz-2 affinity proxy and generates molecules with strong predicted affinity and specificity under Boltz-2 evaluation. To reduce self-confirmation bias, we further evaluate generated molecules using held-out metrics, including AF3-based evaluation. DBMol substantially improves pocket coverage while maintaining molecular diversity over unconditional generation, and is competitive under held-out metrics despite the absence of reference-ligand supervision. These results support the promise of structure prediction models as effective optimization signals for de novo molecular design.

AI 总结：质疑生成模型中逐步变换噪声分布的必要性，采用极简方法，选IMLE为训练目标、卷积网络为模型，经添加关键要素得到单步参数高效生成模型，在ImageNet 256上取得良好效果。

链接：https://arxiv.org/abs/2607.19332

作者：Chirag Vashist, Ke Li

英文摘要：Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching. Along the way, the underlying techniques have become more complicated and various beliefs about what drives strong empirical performance have taken hold. Due to the success of diffusion models and flow matching, one of the more common beliefs is the importance of transforming the noise distribution to the data distribution gradually through many small transformations. We ask whether this is truly necessary, and take a minimalist approach to designing a competitive generative model. We start with the bare-bones essentials, namely just a training objective and a model. We purposefully make both simple. For the training objective, we choose Implicit Maximum Likelihood Estimation (IMLE), and eschew more complicated alternatives such as variational inference, adversarial training and numerical integration. For the model, we eschew transformers and instead choose a moderately sized convolutional network. Then we judiciously added elements that are truly essential, which surprisingly do not include iterative denoising. The result is a single-step parameter-efficient generative model that produces high quality samples at fast speed: it achieves an FID of 2.56 on ImageNet 256 and simultaneously attains good precision and recall.

AI 总结：研究针对贝叶斯优化中核选择依赖目标未知结构的问题，提出ALAS核族，通过学习稳定性参数α适应数据，给出两种参数化，实验表明其在多样设置下性能强大且稳健。

链接：https://arxiv.org/abs/2607.18282

作者：Weibo Huang, Cheng Hua

英文摘要： Bayesian Optimization is widely used for expensive black-box optimization, yet its success often depends on choosing a kernel that matches the objective's unknown structure. In this work, we propose ALAS, a flexible Gaussian Process kernel family built from symmetric $\alpha$-stable spectral components. By learning the stability parameter $\alpha$, ALAS adapts its effective smoothness from data, capturing both smooth trends and sharp irregularities. We present two parameterizations: ALAS, a single stationary component with joint spectral modulation, and ALAS-Sep, a separable variant that learns dimension-wise tail behavior to improve robustness on approximately decomposable objectives. Experiments on standard benchmarks and real-world surrogates demonstrate strong and robust performance across diverse settings.

AI 总结：研究针对标准SAM在参数块间分配扰动预算不合理的问题，提出GEAR-SAM，以平方块梯度的EMA为灵敏度信号，通过闭式约束优化分配预算，实验表明其在多种任务中提升了泛化和鲁棒性，还提供了动态优化视图。

链接：https://arxiv.org/abs/2607.18306

机构：Faculty of Information Technology, Beijing University of Technology（北京工业大学信息技术学院）

作者：Zhen Huang, Jiaxin Deng, Junbiao Pang

英文摘要：Sharpness-Aware Minimization (SAM) improves generalization by minimizing the worst-case loss in a local parameter neighborhood. Standard SAM implicitly allocates its global perturbation budget across parameter blocks according to instantaneous minibatch gradient norms. Such an allocation can be noisy and may not reflect the sensitivity that blocks accumulate throughout training. We propose Gradient-Energy Adaptive Radius SAM (GEAR-SAM), which maintains an exponential moving average (EMA) of squared block gradients as a lightweight, curvature-related sensitivity signal and allocates the fixed SAM budget through a closed-form constrained optimization. GEAR-SAM preserves the global SAM radius, requires no Hessian-vector products or explicit Fisher estimation, and adds only scalar state beyond SAM. Experiments on image classification, transfer learning, noisy-label learning, and partition studies demonstrate improved generalization and robustness across architectures and tasks. More broadly, GEAR-SAM provides a dynamic view of sharpness-aware optimization: a fixed perturbation budget should be redistributed as the sensitivity of functional network blocks evolves during training.

AI 总结：研究灰箱仿真模型校准问题，提出用大语言模型作优化器的智能校准方法，在肛门癌仿真模型上评估，结果表明该方法在减少模型评估次数上有优势，虽迭代推理时间增加，但可审计和解释，适用于仿真时间占主导的情况。

链接：https://arxiv.org/abs/2607.18308

机构：Catalan Institute of Oncology-IDIBELL（加泰罗尼亚肿瘤研究所-IDIBELL）； Autonomous University of Barcelona（巴塞罗那自治大学）； Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública（国家公共卫生与流行病学网络生物医学研究中心）； Artificial Intelligence Research Institute, IIIA-CSIC（人工智能研究所，西班牙科学研究委员会-人工智能研究所）

作者：David Gómez-Guillén, Mireia Diaz, Josep Lluis Arcos, Jesús Cerquides

英文摘要：Calibration of grey-box simulation models is a constrained optimization problem in which model evaluations are expensive, the parameter space can be high-dimensional, and the search must respect plausibility constraints. Although the simulation code is fully available to the analyst, the joint effect of multiple parameters remains difficult to predict analytically. Classical optimizers such as Nelder--Mead (NM) are simple to deploy but sample-inefficient, particularly under constraints. Modern Bayesian Optimization methods achieve competitive solutions with far fewer evaluations but require non-trivial modeling machinery for constraint handling. We introduce an agentic calibration method in which a large language model acts as the optimizer, with constraints incorporated as a plain-language section of the system prompt. We evaluate the agentic method, NM, and Bayesian Optimization (BO) on an anal cancer simulation model under both unconstrained and clinically constrained calibration. Under unconstrained calibration, the agentic method achieves substantially lower best error than BO and NM, while requiring fewer model evaluations. Under constrained calibration, the agentic method reaches comparable error levels and both outperform NM. These results are obtained at the cost of increased inference time per iteration. Agentic calibration achieves competitive performance with substantially fewer model evaluations, and constraint handling is essentially free at the modeller-facing interface through simple textual specifications rather than additional modelling machinery. The main trade-off lies in increased per-iteration inference cost, making the approach particularly suitable when simulation time dominates. Beyond performance, the per-iteration rationale makes the search auditable and explainable, so its decisions can be scrutinised and justified to third parties.

AI 总结：研究过参数化模型参数对称性下的 PAC - 贝叶斯分析，通过在商预测器空间构造规范参数化及考虑几何体积，将中性先验转换为反映模型隐式偏差的先验，实验验证其能降低相关指标。

链接：https://arxiv.org/abs/2607.18422

机构：University of Trieste（的里雅斯特大学）； McGovern Institute, MIT（麻省理工学院麦戈文脑科学硏究所）

作者：Nicola Aladrah, Fabio Anselmi

英文摘要：Overparameterized models often have continuous parameter symmetries, so different parameters define the same predictor. We show that PAC--Bayesian analysis should be performed on the quotient predictor space: pushing a prior and posterior to the quotient preserves the empirical and population Gibbs risks while removing the nonnegative KL contribution caused solely by how the two distributions differ among parameterizations of the same predictor. Quotienting alone does not determine which prior to use. We construct a canonical choice of one parameterization for each predictor and account for the geometric volume of its equivalent parameterizations. This transforms a neutral reference prior into a data-independent prior that reflects the model's implicit bias. It approximates the ideal but inadmissible posterior-matched prior, which would minimize the KL term by depending on the training data. The resulting certificate is tighter exactly when this geometry-induced prior has smaller KL divergence from the learned quotient posterior than the neutral prior. We test this prediction in Fourier regression with a Hadamard parameterization and in Query-Key attention, using ordinary SGD without an explicit regularizer. The implicit-bias prior reduces the mean quotient-space KL by \(40.69\%\) and the mean PAC--Bayes certificate by \(21.40\%\) in the Fourier-Hadamard experiment. The smaller, prior-scale-dependent improvement in Query-Key attention confirms the predicted conditional nature of the effect.

AI 总结：研究针对熔融沉积建模过程数据集，围绕强化学习启发的策略更新机制，利用SHAP XAI生成特征子集，训练多个机器学习模型，探索最优模型-特征配置，提升了预测准确性和稳定性，改进了测试集AUC等指标。

链接：https://arxiv.org/abs/2607.18515

机构：Michigan Technological University（密歇根理工大学）

作者：Saleh Valizadeh Sotubadi, Nazanin Mahjourian, Vinh Nguyen

英文摘要：This study contributes toward development of an Automated Data Processing (ADP) framework designed to evaluate and reinforce optimal machine learning model-feature combinations for predictive tasks in fused deposition modeling (FDM) process datasets. The methodology is centered around a reinforcement learning-inspired policy updating mechanism, where multiple machine learning models are trained on both full feature sets and feature subsets selected through Shapley-based Explainable AI (SHAP XAI) across 217 datasets. At each episode, the framework assesses the predictive accuracy and F1-scores of each model-feature pair, computes a scalar reward, and updates $Q$ values to guide future model selection. SHAP XAI feature importance was employed to generate reduced yet informative feature subsets to enable the framework to explore performance with dimensionality. The policy was shown to evolve over multiple episodes, with reward distributions used to visualize performance stability. Overall, results indicate that leveraging the ADP framework through XAI algorithms successfully converges toward optimal model-feature configurations with improved accuracy and stability. Specifically, the proposed framework improves the test-set AUC from 0.9248 to 0.9731 and increases the mean reward value by more than fifty percent compared with the baseline full-feature configuration.

AI 总结：研究Transformer中长度泛化现象，通过优化解释，证明旋转编码使注意力逻辑仅与相对偏移有关，绝对编码则不然，还将相关现象和特性转移到多层多头Transformer，联系了注意力隐式偏差等多方面内容。

链接：https://arxiv.org/abs/2607.18759

机构：Mississippi State University（密西西比州立大学）

作者：Subham Singh, Ashutosh Mishra, Subha Raut

英文摘要：Transformers with relative positional encodings often extrapolate to sequences longer than those seen during training, whereas transformers with learned absolute encodings typically do not. This is a robust empirical regularity, and the explanations offered for it so far are chiefly about expressivity, that is, about whether a length-generalizing solution exists. We give an optimization explanation. On a minimal fixed-offset retrieval task that isolates positional selection, the gap is governed by the implicit bias of the trained attention head: among the many solutions that fit short sequences, which one gradient descent actually selects. We prove that rotary encodings make the attention logit a function of relative offset alone, an exact equivariance, so whatever selection rule is learned at training lengths is reproduced verbatim at every longer length. Learned absolute encodings instead leave out-of-range positions unconstrained, and the trained head pins to a fixed absolute position inside the training range. We characterize the learned rotary rule as a low-rank ``carrier'' kernel aligned with the target offset, and we derive the resulting graceful accuracy decay as an attention-dilution law; both predictions are confirmed across seeds and offsets. A linear-attention control shows the mechanism is specific to softmax: without normalization, training selects a min-norm interpolant that does not extrapolate. The phenomenon, the equivariance, and the carrier all transfer to a multi-layer, multi-head transformer trained on a full-sequence length-generalization task. The account connects the implicit bias of attention, implicit bias for extrapolation in recurrent models, and the learning side of the RASP-L conjecture.

AI 总结：研究在INT8 NPU上零阶设备端训练中梯度样本数量q的影响及优化难题，提出自适应算法QScheduler，可依训练进度调整q，实验表明该算法在EuroSAT和STL-10数据集上，与固定q配置效果相当且无需事先优化超参数。

链接：https://arxiv.org/abs/2607.18802

作者：Victor Felipe Domingues Do Amaral (GeePs), Pierre Demaj, Erwan Libessart (GeePs), Laurent Folliot, Anthony Kolar (GeePs), Philippe Bénabès (GeePs)

英文摘要：Zeroth-Order (ZO) optimization enables On-Device Learning (ODL) on NPU-equipped microcontrollers by estimating gradients through forward passes alone, bypassing the need for backpropagation primitives and reducing memory requirements. The number of gradient samples q critically affects training: insufficient samples produce noisy gradients that plateau early, while excessive samples consume more computational resources. However, finding an optimal q typically requires costly hyperparameter searches. This work introduces QScheduler, an adaptive algorithm that adjusts q based on training progress, and provides the first proof-of-concept of INT8 quantized on-device training on the STM32N6's Neural-ART NPU. Experiments on EuroSAT and STL-10 show that QScheduler matches well-tuned fixed-q configurations for both ResNet18 and MobileNetV2, without requiring prior q hyperparameter optimization.

AI 总结：研究人工智能驱动碰撞模拟代理的不确定性量化，对蒙特卡洛随机失活和深度集成两种方法在开源管道上比较，用具体随机失活解决常见批评，经汽车碰撞模拟实验，揭示权衡，表明可低成本实现良好校准和无超参数的不确定性估计。

链接：https://arxiv.org/abs/2607.18294

作者：Sudeep Chavare

英文摘要： Machine learning surrogate models are increasingly being explored in engineering product development to augment simulation-driven design, offering near-instantaneous predictions that complement computationally expensive high-fidelity analyses. However, a critical gap limits their adoption in safety-critical workflows: a point prediction without an accompanying uncertainty estimate cannot tell an engineer when the model should not be trusted. This work presents a systematic, head-to-head comparison of two widely used uncertainty quantification approaches -- Monte Carlo Dropout and Deep Ensembles -- applied to an open-source surrogate pipeline built on NVIDIA PhysicsNeMo. A key contribution is the use of concrete dropout, a built-in PhysicsNeMo capability that eliminates the dropout rate as a manual hyperparameter by learning it end-to-end during training, directly addressing the most common criticism of Monte Carlo Dropout-based uncertainty quantification. Automotive crash simulation is used as the application domain, with a steel bumper beam impact problem serving as the benchmark. Both methods are evaluated on identical held-out simulations and compared on point accuracy, uncertainty band calibration, and computational cost. The results reveal a fundamental trade-off between accuracy and calibration that challenges the common assumption that deep ensembles are the default gold standard for surrogate uncertainty quantification. The findings demonstrate that well-calibrated, hyperparameter-free uncertainty estimates are achievable within a fully open-source engineering workflow at a fraction of the computational cost of ensemble approaches.

AI 总结：研究语言模型中罕见事件概率估计难题，提出梯度激活自适应多级分裂方法及移位幂布雷格曼损失，通过实验揭示偏差 - 方差权衡，强调估计器与部署上下文匹配，确立激活空间为易处理域。

链接：https://arxiv.org/abs/2607.18454

机构：Johns Hopkins University（约翰·霍普金斯大学）

作者：Nikita Y. Parulekar, Anqi Liu

英文摘要：Quantifying the risk of rare failures in language models, such as those triggered by adversarial distribution shifts or very large-scale deployments, requires estimating probabilities far too small for random sampling. While recent work has formalized Low Probability Estimation, existing pipelines remain fragile in the rarest regimes: estimators can suffer zero-estimate collapse or systematic bias, and standard evaluation losses can become unstable or poorly matched to asymmetric safety costs. In this work, we introduce Gradient Activation Adaptive Multi-Level Splitting (GA-AMLS), which adapts rare-event Monte Carlo methods to the continuous activation space of language models. Specifically, GA-AMLS uses a gradient-based MCMC kernel to navigate activation space, eliminating the zero-estimate collapse of input-space search and replacing the independence assumptions of prior activation-space estimators with conditional sampling under an explicit, heavier-tailed activation prior. We also propose the Shifted-Power Bregman (SPB) Loss, a proper scoring rule that remains finite for zero-estimates and offers tunable asymmetry between underestimation and overestimation penalties. Experiments on small transformer models reveal a bias-variance tradeoff: GA-AMLS achieves the lowest loss under symmetric evaluation, reducing average log-space squared error relative to the strongest baseline across model sizes, while methods with overestimation bias prevail under asymmetric penalties. Our findings highlight that estimator choice should be matched to deployment context. More broadly, our work establishes activation space as a tractable domain for rare-event estimation in language models, circumventing the brittleness of discrete input-space search.

AI 总结：研究针对科学领域机器学习评估难题，引入PertReason基准和框架套件，通过PertReasonQA测试模型，结合多数据与知识图谱，发现现有模型预测与推理差距，提出PertReasonLM，为科学系统中忠实推理失败提供诊断框架。

链接：https://arxiv.org/abs/2607.18777

机构：Texas A&M University（德克萨斯农工大学）

作者：Dongkwan Kim, Yiming Gao, Yining Yang, Yang Shen

英文摘要：Evaluating machine learning in scientific domains requires separating correct predictions from correct reasons under realistic distribution shifts. We introduce PertReason, a knowledge-grounded benchmark and framework suite for cell-state--conditioned reasoning about perturbation effects. At its core, PertReasonQA is a benchmark that tests whether models can generate mechanistically faithful explanations while remaining robust to complex shifts, such as new cells and unseen perturbations. PertReasonQA combines single-cell genetic and chemical perturbation data across multiple cellular contexts with knowledge graphs, and dynamically conditions pathways on cell-specific basal states to avoid generic memorization. Evaluations on state-of-the-art models reveal systematic gaps between predictive accuracy and mechanistic reasoning. Specifically, these models exhibit failure modes largely invisible to standard benchmarks, such as deriving correct answers through flawed logic, ignoring cellular context, and generating directionally inconsistent mechanisms. As a reference probe of the benchmark, we present PertReasonLM, a large language model trained to align outcome predictions with context-specific mechanistic reasoning. Our model targets the identified failure modes by grounding rationales in context-specific pathways and tightening agreement between outcomes and mechanisms. Together, we provide a diagnostic framework for exposing and mitigating failures in faithful reasoning in data-rich scientific systems.

AI 总结：研究非定常翼型空气动力学的时空预测问题，核心方法是将图神经网络与增强神经常微分方程结合形成GNODE，主要贡献是该方法能稳定准确地预测表面力，适合建模含外部输入的非线性时空系统。

链接：https://arxiv.org/abs/2607.18309

机构：German Aerospace Center (DLR)（德国航空航天中心）； Airbus Operations（空客运营公司）

作者：Henrik Lange, Reik Thormann, Philipp Bekemeyer

英文摘要： Unsteady aerodynamic phenomena, such as gusts, turbulence, and fluid-structure interactions affect an aircraft during flight. For design, optimisation and certification, it is indispensable to quantify such unsteady aerodynamic effects. Industry-standard computational fluid dynamics methods, such as solving the unsteady Reynolds-averaged Navier-Stokes equations or the linearized frequency domain method, are either computationally expensive or restricted by assumptions like linearity. Once trained, machine learning methods are capable of computing non-linear relationships very fast, making them suitable as surrogate models. By autoregressively applying graph neural networks (GNNs), operating on a discretised spatial domain, spatio-temporal predictions can be made. However, autoregressive GNNs suffer from error accumulation leading to unstable rollouts over time. Here we show that combining GNNs with augmented Neural Ordinary Differential Equations yields temporally stable predictions of the surface forces on a pitching airfoil. We found that our approach, called GNODE, based on Graph Neural Ordinary Differential Equations, provides temporally more stable, spatially smoother, and overall more accurate results than an autoregressive GNN baseline. Tests are conducted on a dataset consisting of a simulations of a pitching airfoil, including transonic shocks, transient behaviour and dynamic non-linearities. Augmenting GNODEs with additional latent dimensions improves the expressivity and accuracy by capturing underlying history effects. The developed method demonstrates an approach that is suitable to model non-linear spatio-temporal systems with exogenous inputs.

AI 总结：研究多类标签聚合问题，提出AHEAD框架，通过图神经网络学习交叉注释器上下文，聚合特征得到注释器嵌入并解码为混淆矩阵，结合高置信度注释器缓解训练问题，实验表明该方法显著提高标签准确性和可扩展性。

链接：https://arxiv.org/abs/2607.18465

作者：Ju Chen, Sijia Xu, Jun Feng, Zhiqiang Gao, Zhengyi Yang

英文摘要：Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video. Label aggregation aims to infer latent true labels from noisy and biased annotations, with the key lying in annotator reliability estimation. Despite promising progress, existing approaches struggle with one real-world bottleneck: most individual annotators label only a small subset of tasks, making accurate annotator estimation highly intractable. In this paper, we focus on the considerably more challenging multi-class label aggregation and propose AHEAD (cross-Annotator learning and High-confidEnce Annotator-guideD label aggregation), a cross-annotator learning framework that advances annotator reliability estimation by leveraging the population-level data. Specifically, AHEAD first learns high-dimensional cross-annotator contexts via a graph neural network, deriving multi-view, complementary annotator embeddings by aggregating individual-level annotator features with contextual information. These embeddings are then decoded into interpretable annotator-specific confusion matrices to fit the observed labels. We formulate a composite objective incorporating high-confidence annotators to alleviate the unsupervised training issues faced by prior models. Experiments on 10 real-world datasets spanning NLP, CV, Video, and Audio show that AHEAD substantially improves label accuracy, increasing average accuracy from 68.75% to 73.23%, with gains of up to 14.9% in the best case. Meanwhile, scalability experiments on the largest dataset further demonstrate the overall superiority of our method.

AI 总结：研究旅行商问题的算法选择，提出基于图神经网络的GNNAS - TSP框架，将其作为联合成本预测和排序任务，通过实验评估多种学习目标，在不同预算下所选配置优于单一最佳求解器，证明该框架是有用的元求解策略。

链接：https://arxiv.org/abs/2607.18632

作者：Zhaoxuan Li, Jiale Yang, Yifei Lu, Mustafa Misir

英文摘要：Automated Algorithm Selection (AS) aims to improve problem-solving performance by selecting, for each problem instance, the most suitable algorithm from a predefined portfolio. This is particularly relevant to the Traveling Salesman Problem (TSP), where solver performance is strongly instance-dependent. We introduce GNNAS-TSP, a Graph Neural Network (GNN)-based AS framework that learns TSP instance representations directly from raw graph data, avoiding manual feature engineering. GNNAS-TSP formulates AS as a joint cost-prediction and ranking task. We evaluate cost-based (mean squared error (MSE), mean absolute error (MAE), and Huber), rank-based (RankNet, ListNet, and LambdaRank), and hybrid learning objectives for a portfolio comprising Chained Lin-Kernighan, Edge Assembly Crossover, Lin-Kernighan-Helsgaun, Multiagent Optimization System, and Concorde. Experiments use fixed computational budgets of 10 and 60 seconds. On the held-out test set, the selected configurations improve on the Single Best Solver (SBS) in normalized solution cost at both budgets. For the 10s budget, AS achieves substantial and statistically significant cost improvement over SBS. Overall, the results suggest that GNNAS-TSP is a useful meta-solving strategy when exploitable variation exists across solver performance.

AI 总结：研究针对等变图神经网络重用受限问题，提出配置驱动框架GEqTrain，可通过配置重新定位到新任务，在多3D科学任务展示灵活性，还引入GEqDiff，验证其能力，目标是提升等变建模的可重复性、扩展性和可重用性。

链接：https://arxiv.org/abs/2607.19083

机构：Faculty of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana（瑞士意大利语区大学 生物医学科学学院欧拉研究所）

作者：Daniele Angioletti, Marco Nobile, Vittorio Limongelli

英文摘要： Equivariant graph neural networks provide a powerful modeling language for three-dimensional scientific data, but their reuse is often limited by implementations tied to specific tasks, outputs, and training regimes. We present GEqTrain, a configuration-driven framework that separates dataset semantics, model composition, and training objectives. Raw data are mapped to typed node-, edge-, and graph-level fields, while model stacks, losses, and training workflows are assembled declaratively through Hydra configurations. A shared equivariant backbone and training infrastructure can therefore be retargeted to a new task primarily through configuration. We demonstrate this flexibility on three different problems handled within one software stack: coarse-grained-to-atomistic backmapping of biomolecular systems, prediction of NMR chemical shifts in molecular solids, and equivariant generative modeling. Our aim is not to surpass individually optimized task-specific systems, but to show that a shared representation and training infrastructure can achieve competitive accuracy across qualitatively different tasks at the cost of a configuration change. We further introduce GEqDiff, a generative extension based on equivariant flow matching. GEqDiff treats user-defined equivariant fields as first-class generation targets, jointly transporting Cartesian positions and non-scalar node fields spanning representations up to l=3 within a single equivariant flow. We validate this capability on a controlled synthetic benchmark inspired by protein secondary-structure motifs, showing that fields with heterogeneous transformation properties can be reconstructed jointly and with high fidelity. By reducing the software overhead of moving between predictive and generative, scalar and tensorial settings, GEqTrain aims to make equivariant modeling more reproducible, extensible, and reusable.

AI 总结：研究针对神经马尔可夫逻辑网络在大结构上表现欠佳的问题，通过用图神经网络增强势函数表达能力，结合受并行回火启发的并行噪声训练和推理算法，提升其在图生成及小分子结构生成中的性能。

链接：https://arxiv.org/abs/2607.19126

机构：Czech Technical University（捷克技术大学）； KU Leuven（鲁汶大学）

作者：Peter Jung, Giuseppe Marra, Ondrej Kuzelka

英文摘要：Neural Markov Logic Networks (NMLNs) are a flexible neurosymbolic relational model. Previous work has shown that, although NMLNs achieve strong performance as generative models for small relational structures, they underperform diffusion-based generative graph models on larger structures. In this paper, we strengthen NMLNs along two main dimensions: (i) we increase the expressive capacity of their potential functions using graph neural networks, and (ii) we develop a new training and inference algorithm inspired by parallel-tempering Markov chain Monte Carlo methods, which we name parallel noising. Together, these enhancements enable NMLNs to attain strong performance in graph generation relative to general diffusion-based generative graph models. Furthermore, they allow NMLNs to match the performance of specialized text-based recurrent models when generating small molecular structures.

AI 总结：研究利用视觉语言模型解决属性图模态异质性问题，提出OMG-VLM框架，以预训练VLM为共享主干，引入结构感知图适配器，在节点分类和链接预测等任务中表现出色，优于现有基线且泛化能力强。

链接：https://arxiv.org/abs/2607.19128

机构：New York University Shanghai（纽约大学上海分校）； New York University（纽约大学）

作者：Jiayi Yang, Yifang Chen, Yuanfu Sun, Jiajin Liu, Qiaoyu Tan

英文摘要：Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data remains largely unexplored. In practice, attributed graphs exhibit substantial modality heterogeneity: some graphs contain only textual node attributes, others only visual attributes, while still others provide both. Existing graph learning approaches are typically designed for fixed modality schemas, requiring separate models for different settings and limiting scalability and cross-graph generalization. To bridge this gap, we present OMG-VLM (One Model, Many Graphs with Vision-Language Models), a unified framework for learning over attributed graphs across heterogeneous modality schemas. OMG-VLM leverages a pretrained VLM as a shared backbone and introduces structure-aware graph adapters that integrate neighborhood information while remaining compatible with the VLM's native embedding space. This design enables effective learning over text-attributed, image-attributed, and multi-attributed graphs within a single model. Extensive experiments across diverse domains show that OMG-VLM consistently outperforms state-of-the-art GNN- and LLM-based baselines on attributed graph learning tasks such as node classification and link prediction, while exhibiting strong generalization to unseen graphs and varying modality schemas. The source code is available at this https URL.

AI 总结：针对胎儿超声图像中胼胝体定位难题，提出FedCC框架，集成冻结的DINOv2主干与轻量级YOLO检测头，并引入LoRA模块。在多中心数据集上评估，该框架性能出色，减少了可训练参数与通信成本，迈向可临床部署的胎儿神经超声AI系统。

链接：https://arxiv.org/abs/2607.18283

机构：unich（那不勒斯费德里克二世大学）

作者：Alessandro Di Matteo, Sara Moccia, Giuseppe Rizzo, Gianpaolo Grisolia, Ricciarda Raffaelli, Lorenzo Vasciaveo, Francesco D'Antonio, Maria Chiara Fiorentino

英文摘要：Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopmental abnormalities. However, this task remains highly challenging due to the intrinsic limitations of US imaging, including low contrast, speckle noise, and the considerable anatomical variability of the CC. We propose FedCC, a federated learning (FL)-based framework for CC localization in fetal US images, specifically designed for realistic multi-center and resource-constrained clinical settings without requiring data sharing. The framework integrates a frozen DINOv2 backbone with a lightweight YOLO-based detection head. To enable parameter-efficient adaptation, Low-Rank Adaptation (LoRA) modules are incorporated, allowing only a small subset of parameters to be optimized and exchanged among clients. This strategy substantially reduces both computational and communication overhead, making the framework suitable for low-resource environments. The proposed approach was evaluated on a multi-center dataset comprising 10,970 ultrasound frames acquired from 58 pregnant women during routine neurosonographic examinations across three clinical sites using heterogeneous imaging devices. The proposed framework achieved strong performance in the federated setting. In particular, the combination of DINOv2 and LoRA under the FedAvg strategy achieved an average mAP@50 of 0.857 and an F1-score of 0.803, outperforming both full fine-tuning and encoder-freezing baselines. Notably, the proposed approach reduced the number of trainable parameters to 2.9M compared with 24.4M in full fine-tuning, corresponding to an approximately 8.5$\times$ reduction in communication cost. These findings represent a promising step toward scalable, privacy-preserving, and clinically deployable AI systems for fetal neurosonography.

AI 总结：研究针对自回归语言模型序列开头冷启动惩罚问题，提出领域条件位置偏移方法，通过添加单个学习向量减少困惑度，在多模型上效果显著，且具有轻量级、可热切换特点，能改善检索重排等，是短域内评分和校准的有效工具。

链接：https://arxiv.org/abs/2607.18302

作者：Ye Qiao

英文摘要：Autoregressive language models are least accurate at the beginning of a sequence, where little context forces reliance on a generic pretraining prior. We show that this cold-start penalty is domain dependent and reduce it with a domain-conditional position offset: a single learned vector added to the embedding activation at the first sequence positions while all model weights remain frozen. The offset trains in minutes on roughly one hundred documents, switches between domains without added sequence state, and has no measurable latency overhead. Across eight Mamba, GPT-NeoX, and Llama models spanning 410M to 8B parameters, it reduces held-out in-domain perplexity by up to 27%; the effect persists at 70B, and one position captures most of the benefit. A matched, converged direct logit-bias correction reaches at most only 7.9% and leaves later-token loss unchanged, showing that the offset propagates through model state rather than merely recalibrating the output prior. A tuned LoRA reaches lower perplexity but uses two to three orders of magnitude more parameters and an active low-rank weight path, while soft prompts add sequence positions. With wrong-domain controls, offsets improve retrieval reranking and domain classification when decisions depend on early in-domain tokens, For the few-shot reasoning whose signal occurs later, the results maintains unchanged. Position-aware prefill application also help generation tasks, whereas naive application at every cached decoding step causes repetition. The offset is therefore not the strongest adapter, but a lightweight, hot switchable tool for short in-domain scoring and calibration.

AI 总结：研究语言模型能否读取计算质量及外部干预能否改善结果，通过在Ouro-RLTT中实验，利用隐藏状态等预测成功，构建相关机制，虽有可读属性但干预未提升能力，此为操作原语内省。

链接：https://arxiv.org/abs/2607.18553

作者：Jan Kirin

英文摘要：Can a language model read the quality of ongoing computation, and can an external intervention turn that readout into better outcomes? We test both questions in a frozen 2.6B looped transformer, Ouro-RLTT. On GSM8K, a strict pre-answer probe excludes the answer region and gold value yet predicts eventual success: hidden states plus length and log-probability shortcuts reach AUROC 0.797, versus 0.731 for the shortcuts alone (incremental +0.066; task-clustered 95% CI [+0.021, +0.112]; 170 tasks, 680 candidates). Low-capacity taps also read role-specialized properties: task-disjoint branch survival reaches 0.9697 oracle retention, content ranking reaches 0.6310 macro top-1, and generated-branch correctness reaches AUROC 0.7755. A non-looped control replicates a candidate-quality readout, so recurrence is not required for every signal. We build branch/carry/prune machinery over Ouro's 192-slot recurrent cache, including branch-specific cache lineage and a bit-exact residual-capture splice that recomputes only the affected suffix and saves up to 88% of per-branch layer passes. No frozen intervention produces a validated capability gain. Directional steering is an established negative; a four-task matched-sampling comparison removes evidence for a frozen-fork gain but cannot estimate a general deficit; terminal selection remains unresolved and underpowered; and bounded LoRA changes surface behavior without improving net reachability. A two-null audit does not support the simplest span-misalignment explanation. We call this readable-but-not-yet-usable property operational proto-introspection. The model is not consulting our probes: we read its hidden trajectories, and our interventions fail to convert those readouts into validated capability. The pre-answer result is limited to one domain. Load-bearing values use source-item-disjoint splits and antisymmetrized evaluation where applicable.

AI 总结：研究非平稳和部分可观测环境下序列决策问题，提出效用增强Transformer（UAT），通过紧凑效用状态调制注意力检索，解决现有模型反馈盲目性问题，在四个非平稳基准测试中性能优于其他基线。

链接：https://arxiv.org/abs/2607.18910

机构：The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））； Shenzhen Research Institute of Big Data（深圳大数据研究院）； University of International Business and Economics（对外经济贸易大学）

作者：Yuyang Shen, Shan Dai, Daimin Chen

英文摘要：Sequential decision making in non-stationary and partially observable environments requires rapid adaptation to latent regime changes. However, existing Transformer decision models face a structural bottleneck in the retrieval mechanism: even when reward is used for training or exposed as an input token, attention retrieval remains primarily driven by observation-derived similarity. We formalize this limitation as feedback-blind retrieval, and formally show that, on feedback-informative tasks, observation-equivalent histories with different action-reward outcomes cannot be distinguished by any observation-only attention, resulting in suboptimal choice. To address this mismatch, we propose the Utility-Augmented Transformer (UAT), a new feedback-conditioned retrieval attention architecture in which a compact utility state modulates the query, key, and value projections, allowing action-reward history to directly alter context retrieval during the forward pass. UAT also enjoys an exact zero-gate degradation property that recovers the Vanilla Transformer when feedback is uninformative. Under finite-horizon compactness and Lipschitz assumptions, we prove that UAT strictly enlarges the observation-only Transformer class and can uniformly approximate feedback-dependent decision maps. Across four non-stationary benchmarks: synthetic navigation with hidden goal shifts, non-stationary sepsis treatment, cross-market portfolio allocation, and delayed-feedback recommendation, UAT consistently improves performance over observation-only, test-time adaptation, and input-level feedback baselines, with particularly large gains in noisier regimes that require stronger adaptation.

AI 总结：研究针对神经网络在低数据下易过拟合且缺乏可靠不确定性量化的问题，提出基于摊销变分推理的流形元学习框架概率扩展，结合最大后验估计与拉普拉斯近似，在低数据任务中实现高精度并提供校准的不确定性界限。

链接：https://arxiv.org/abs/2607.18965

机构：SUPSI-DTI-IDSIA, Dalle Molle Institute for Artificial Intelligence（瑞士意大利语区大学-信息工程与数学系-人工智能研究所）

作者：Matteo Rufolo, Dario Piga, Marco Forgione

英文摘要： Deep learning has proven highly effective for nonlinear system identification, but heavily parameterized neural networks are prone to overfitting in low-data regimes and lack reliable uncertainty quantification. The recently developed manifold meta-learning framework addresses the data efficiency problem by restricting the model parameters to a meta-learned low-dimensional manifold. However, that method is purely deterministic. We propose a fully probabilistic extension of the manifold meta-learning framework, based on amortized Variational Inference, where a generative prior over the low-dimensional parameter manifold is learned. During task-specific adaptation, we combine Maximum A Posteriori estimation with the Laplace approximation to yield a mathematically grounded posterior approximation. Evaluated on a static regression task and the Bouc--Wen dynamical system benchmark, the proposed approach achieves predictive accuracy comparable to its deterministic counterpart while successfully providing calibrated uncertainty bounds in severely low-data regimes.

AI 总结：研究针对交通分配问题中GNN模型空间泛化差的问题，提出网络无关初始化层GUIDED，通过在虚拟链路注入需求标量属性标准化输入空间。实验表明其能提升模型性能、鲁棒性及参数效率，减少训练时间，为相关空间问题提供通用蓝图。

链接：https://arxiv.org/abs/2607.19270

机构：Technical University of Munich（慕尼黑工业大学）

作者：Alessandro Scalese, Santhanakrishnan Narayanan, Constantinos Antoniou

英文摘要：The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap. Standard models rely on transductive feature initializations that tie travel demand to fixed network topologies, preventing seamless transfer to new urban environments. To overcome this structural limitation, this research proposes a network-agnostic initialization layer, termed Geometrically Unconstrained Inductive Demand EmbeDding (GUIDED). By injecting travel demand as a scalar attribute on auxiliary virtual links rather than as specific node features, this modular framework standardizes the input space regardless of network scale. Extensive experimental evaluation across multiple urban topologies demonstrates that a Heterogeneous Graph Attention Network (HetGAT) model integrated with the proposed GUIDED layer maintains state-of-the-art predictive accuracy on single-network tasks, while demonstrating superior robustness to out-of-distribution demand patterns and maintaining a distinct performance advantage over the baseline even under severe data scarcity. Notably, the proposed feature initialization enables highly parameter-efficient domain adaptation for inter-network transfer learning without artificial input homogenization, establishing a robust foundation for truly inductive models. At the same time, the optimized scatter operations of the initialization layer yield an approximate 50% reduction in training time per epoch compared to the baseline approach. Furthermore, while demonstrated on vehicular traffic, this fundamental abstraction of spatial topology provides a versatile blueprint for generalized origin-destination spatial problems, such as freight logistics and multimodal network optimization.

AI 总结：研究语言模型学习的结构限制，引入信息阴影概念，包含语言无法表达的结构等三类。通过语言压缩残差等探针测试，揭示不同类型限制，发布探针套件并探讨其对基准设计等方面的影响。

链接：https://arxiv.org/abs/2607.18305

机构：Sirena Ai（Sirena人工智能公司）

作者：Priyansh Srivastava, Romit Chatterjee

英文摘要：Some limits on what language models know are not gaps in data coverage but structural properties of learning from text. We introduce the information shadow: the region of phenomena that a text-trained learner cannot acquire regardless of scale, comprising (I) structures language cannot express, (II) functions that are statistically non-identifiable from the training distribution, and (III) functions that are representable but unreachable by gradient-based training. We give each type a probe that is decisive because the premise of the shadow is, in that setting, provable. For Type I, Language Compression Residuals compare a text learner, which sees only a lossy text-like encoding of the signal, against a full-signal learner, which sees the underlying signal directly. The text learner sits at a computable expressibility ceiling while the full-signal learner pulls away by a gap that stays flat across 300x more data, so the deficit is a property of the channel, not of training. For Type II, the Counterfactual Distinction Test trains models on data exactly consistent with two incompatible rules. Across a provable string task and a language-like agreement task, behavior on counterfactuals is set by the model's inductive bias, while 5% disambiguating data steers the learned rule bidirectionally to either target (r = +/-1.0, p < 1e-10). For Type III, Basin Escape Mapping exhibits a function that is representable at 100% (by hand construction) yet reached 0% of the time by standard training and instantly from a nearby initialization, with width scaling providing no help (p = 1.6 x 10^-14). Each effect is isolated by a control that rules out a capacity or modality artifact. We release the probe suite and discuss implications for benchmark design, capability auditing, and shadow-aware uncertainty.

AI 总结：研究针对挖掘代码语料库的问题，提出意大利面架构师工具，通过反优化转译器生成多语言代码数据集，能抵抗污染、自带标签并沿难度轴标记，给出结构效度证据及基线，表明其在代码数据集生成上的有效性和优势。

链接：https://arxiv.org/abs/2607.18642

机构：Sunway College（双威学院）

作者：Yuxiang Ji

英文摘要： Mined code corpora are abundant but uncontrolled: a snippet's semantics, surface "messiness," and difficulty are whatever the wild contained; there is no known-optimal reference to grade against; and any public sample may already sit in a model's training set. We present Spaghetti Architect, a tool that mints code datasets with the control such corpora lack. An anti-optimization transpiler maps a clean, language-agnostic JSON intermediate representation to deliberately redundant, fully-flattened programs in five languages (Python, JavaScript, Go, Java, C++); every program is compiled, run, and checked against a reference oracle, so each instance is correct by construction. The clean IR is a known-optimal reference, messiness is dialed by strictly-nested anti-pattern profiles, each instance is labelled along two orthogonal difficulty axes, intrinsic (problem size) and incidental (presentation at fixed semantics), and contamination is resisted by minting fresh variants from a private held-out seed. We give construct-validity evidence that the quality order moves established complexity and readability metrics, and report baselines on a four-model open ladder: exact match rises with scale, and the intrinsic knob collapses arithmetic-aggregation accuracy of even the strongest model to zero. Further, development-set scores equal freshly re-minted held-out counterparts within $|\Delta|\le 0.012$ (comprehension) and $\le 0.011$ (refactoring); on identical programs, refactoring equivalence ($0.73 \rightarrow 0.99$) is scale-invariant while output prediction collapses; and ablating the generator's self-annotations shows they inflate the weakest model an order of magnitude more than the strongest ($-0.173$ vs $-0.017$): the annotated ladder resolves one of three adjacent pairs where the unannotated resolves all three. Open source (MIT), dependency-free, archived under a persistent DOI.

AI 总结：探讨脑电到文本在现实场景的可行性，利用受神经心理学启发的范式，发现现有基准忽略脑电不稳定性问题。通过实验为无教师强制的EEG2Text解码提供证据，构建COFETT基准，可区分模型性能，实现稳健评估，推动EEG2Text实际应用。

链接：https://arxiv.org/abs/2607.18749

机构：Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology（哈尔滨工业大学社会计算与交互机器人研究中心）； Shanghai Innovation Institute（上海创新院）； State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）； Zhongguancun Laboratory（中关村实验室）

作者：Zihan Zhang (1), Yu Bao (1 and 2), Xiao Ding (1), Tianyi Jiang (3), Kai Xiong (4) ((1) Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, (2) Shanghai Innovation Institute, (3) State Key Laboratory for Novel Software Technology, Nanjing University, (4) Zhongguancun Laboratory)

英文摘要：Translating brain signals into text could restore communication for people with severe paralysis, yet practically usable systems to date rely on invasive electrocorticography (ECoG). Electroencephalography (EEG) offers a non-invasive alternative, and EEG-to-text (EEG2Text) has been widely explored. Interestingly, however, EEG2Text models generally rely on teacher-forcing evaluation; without it, they fail to generate meaningful decoding. This reliance prevents EEG2Text from being applied in real-world, non-academic settings. This has fueled numerous debates about whether EEG2Text is a meaningful direction, by extension, and whether EEG truly contains decodable linguistic information. Here, using a neuropsychology-informed paradigm, we find that existing EEG2Text benchmarks have neglected EEG instability, a flaw that has confounded inference and sparked debate. Our experiments furnish key evidence for the feasibility of teacher-forcing-free EEG2Text decoding. Accordingly, we assemble the Corpus OF Eeg-To-Text (COFETT) using a 128-channel high-density EEG cap, providing a benchmark dedicated to evaluating EEG2Text models. In comparisons with multiple existing benchmarks, COFETT achieves SOTA ability to distinguish among model performances and enables robust, teacher-forcing-free evaluation, thereby opening a path toward practical EEG2Text applications. COFETT is open sourced in this https URL.

AI 总结：研究针对停留点检测缺乏标准基准及算法未系统评估的问题，引入16个大规模模拟数据集，评估九种停留点检测算法，发现现有算法在现实噪声条件下不佳，所提无监督方法有改进，监督方法优于基线，为后续研究提供了起点。

链接：https://arxiv.org/abs/2607.19312

机构：Emory University（埃默里大学）

作者：Lance Kennedy, Hossein Amiri, Yueyang Liu, Riyang Bao, Hanqi Chen, Mohammad Hashemi, Ruochen Kong, Xiaotong Liu, Joon-Seok Kim, Shengpu Tang, Liang Zhao, Andreas Züfle

英文摘要：Detecting staypoints from raw trajectory data is fundamental to numerous spatial computing applications. This process transforms raw numeric sequences of geolocations into semantically meaningful locations, such as homes, workplaces, or restaurants. Despite its importance for semantic trajectory analysis, staypoint detection lacks standard benchmarks, and existing algorithms have never been systematically evaluated. This gap persists because no publicly available datasets provide both raw individual trajectories and ground-truth staypoint annotations. This benchmark paper addresses this limitation with two key contributions: (1) we introduce 16 large-scale simulated datasets capturing thousands of agents with annotated staypoints across varying trajectory noise levels, and (2) we evaluate nine staypoint detection algorithms-including both state-of-the-art and novel methods-to analyze their robustness to noise. Our evaluation reveals that existing state-of-the-art algorithms perform poorly under realistic noise conditions. Conversely, our proposed unsupervised methods yield substantial improvements, while supervised approaches drastically outperform existing baselines. While these results are very promising, these datasets and methods are only meant as starting points for future research in staypoint detection.

AI 总结：研究持续异常检测，提出从现有表格异常检测数据集设计可重复基准场景的系统框架，能发现候选任务、过滤不合适任务并得出有原则排序，还能从三个数据集提供五个基准场景，涵盖单数据集和多数据集设置。

链接：https://arxiv.org/abs/2607.18289

机构：AGH University of Krakow（克拉科夫AGH科技大学）； American University（美国大学）

作者：Kamil Faber, Mateusz Smendowski, Roberto Corizzo

英文摘要：Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously observed regimes. CAD benchmarks, however, depend critically on how tasks are defined, filtered, ordered, and validated. In tabular domains, task boundaries are rarely given, and arbitrary splits can create unlearnable, redundant, or overly transferable tasks that obscure genuine continual-learning behavior. To this end, we introduce a systematic framework for reproducible benchmark scenario design from existing tabular anomaly-detection datasets. The framework discovers candidate tasks, filters unsuitable tasks, and derives principled orderings that expose diverse dynamics. The framework allows us to deliver five benchmark-ready scenarios from three large-scale cybersecurity anomaly detection datasets, yielding both single-dataset and multi-dataset CAD settings.

AI 总结：该研究针对分子ADMET预测问题，提出ChemHyperMag方法，通过构建官能团超图、定义势驱动不可逆流等，经磁拉普拉斯编码和磁切比雪夫编码器处理，在多基准实验中以更少样本实现改进，且具有可扩展性和可解释性。

链接：https://arxiv.org/abs/2607.18332

作者：Hexiao Ding, Hongzhao Chen, Jing Lan, Yufeng Jiang, Zihong Luo, Zehua Xiong, Tianlong Ruan, Yunlin Mao, Nga Chun Ng, Gwing Kei Yip, Gerald W.Y. Cheng, Kate Inyoung Oh, Jing Cai, Liang-Ting Lin, Jung Sun Yoo

英文摘要：Accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is important for drug discovery. Most predictors use undirected molecular graphs and pairwise edges. This choice misses asymmetric interactions, nonreversible dynamics, and motif level effects from functional groups and ring systems. We propose ChemHyperMag for multitask ADMET prediction under missing labels. ChemHyperMag builds a functional group hypergraph from rings, BRICS fragments, Bemis-Murcko scaffolds, and bonds. It also defines a potential driven nonreversible flow guided by electronegativity and Gasteiger partial charges. The resulting circulation is encoded by a Hermitian magnetic Laplacian and processed with a magnetic Chebyshev encoder. We perturb magnetic phases to form stochastic views and train with an InfoNCE objective. Experiments on multiple ADMET benchmarks show improvements over recent methods with fewer labeled samples and no conformers. ChemHyperMag is scalable and provides interpretable directional signals through its magnetic phases.

AI 总结：针对现实世界动态图标记数据稀缺问题，提出SG-JEPA架构，通过沿时间维度划分节点并利用脉冲神经元编码，学习相互预测的嵌入，在节点分类任务中性能优于判别基线，且训练效率和内存可扩展性高。

链接：https://arxiv.org/abs/2607.18412

机构：Sun Yat-sen University（中山大学）

作者：Huizhe Zhang, Yuchang Zhu, Huazhen Zhong, Liang Chen, Zibin Zheng

英文摘要：Dynamic graph learning aims to capture evolving structural and semantic patterns in real-world systems, such as fraud detection and recommender systems. Due to the scarcity of labeled data in real-world dynamic graphs, recent studies have introduced generative or contrastive paradigms (e.g., masked graph autoencoders or graph contrastive learning) to generate task-agnostic graph embeddings. However, these methods typically rely on complex edge-level reconstruction objectives and tailored graph augmentation strategies. This incurs substantial computational overhead when scaling to large-scale dynamic graphs. In this paper, we propose SG-JEPA, a joint spiking embedding predictive architecture for large-scale dynamic graphs. In contrast to existing self-supervised methods, SG-JEPA partitions nodes into context and target sets along the temporal dimension to learn embeddings that are predictive of each other via additional spatial-temporal information. Furthermore, through encoding sequential inputs into coarse-to-fine spike count embeddings, spiking neurons enable SG-JEPA to adapt to the varying computational constraints of downstream tasks. Extensive experiments demonstrate that SG-JEPA achieves competitive or even superior performance over discriminative baselines on node classification, while effectively scaling to the dynamic graph with 13 million edges. SG-JEPA avoids the complex machinery (negative sampling, graph augmentations, edge-level reconstruction, etc.), resulting in superior training efficiency and memory scalability compared with prior self-supervised dynamic graph baselines.

AI 总结：研究针对网络安全中恶意异常活动检测难题，提出混合潜在结构融合（HLSF）框架，整合CP-APR结构异常分数与归一化流潜在空间密度分数，实验证明该框架能提升在特定数据集上的异常检测性能。

链接：https://arxiv.org/abs/2607.18479

机构：Sandia National Laboratory（桑迪亚国家实验室）

作者：Dorianis M. Perez, Maksim E. Eren, Bryan E. Kaiser

英文摘要：Malicious anomalous activity detection is a fundamental challenge for cyber security systems. Both tensor decomposition under statistical framework with CANDECOMP-PARAFAC alternating Poisson regression (CP-APR) and normalizing flows have proven to be powerful unsupervised machine learning methods that model multi-dimensional data and capture complex and multi-faceted details of behavior profiles in cyber security applications. In this study, we propose Hybrid Latent-Structural Fusion (HLSF), a weighted anomaly fusion framework integrating CP-APR structural anomaly scores with latent-space density scores derived from normalizing flows. In our experiments, we show that the HLSF framework improves anomaly detection performance on a dataset of real-world compromised user credentials collected from the large enterprise network of Los Alamos National Laboratory (LANL) during a red-teaming exercise, compared with using CP-APR or normalizing flows alone.

AI 总结：研究针对1型糖尿病血糖预测问题，提出多模态深度学习架构SCGP，基于观测数据和个体表征预测血糖，通过分离特征与建模避免早期融合，实验证明其能提高预测性能，利于个性化糖尿病管理。

链接：https://arxiv.org/abs/2607.19006

作者：Giorgia Rigamonti, Mirko Paolo Barbato, Davide Marelli, Paolo Napoletano

英文摘要：Accurate forecasting of blood glucose concentration is key in the management of Type 1 Diabetes, facilitating early detection of adverse glycemic events and supporting timely therapeutic interventions. Despite recent advances in glucose prediction, most existing approaches rely on population-level representations or implicit personalization strategies that fail to deliver effective subject-specific forecasts. In this work, we propose Subject-Conditioned Glucose Prediction (SCGP), a novel multimodal deep learning architecture conceived for personalized blood glucose prediction. SCGP conditions glucose predictions based on observed glucose data and a compact subject-specific representation learned from contextual information. By explicitly separating subject characterization from glucose dynamics modeling and avoiding early fusion of heterogeneous inputs, the proposed framework effectively captures inter-subject variability while preserving robust and reliable temporal modeling. Experiments on two state-of-the-art benchmark datasets demonstrate that SCGP consistently improves forecasting performance, enabling reliable detection of adverse glycemic events across multiple prediction horizons, highlighting the benefits of explicit subject conditioning for personalized diabetes management.

AI 总结：针对财务报表欺诈检测中现有方法性能估计不现实的问题，提出利用大语言模型整合结构化与非结构化数据的框架，通过新基准任务评估，构建公开数据集，该方法在新任务中性能最佳，凸显文本数据和稳健评估的价值。

链接：https://arxiv.org/abs/2607.19259

作者：Guy Stephane Waffo Dzuyo (1 and 2), Gaël Guibon (2 and 3), Christophe Cerisara (2), Luis Belmar-Letelier (1) ((1) Forvis Mazars, (2) LORIA CNRS Université de Lorraine, (3) LIPN CNRS Université Sorbonne Paris Nord)

英文摘要：Financial statement fraud detection (FSFD) is crucial for market integrity but faces challenges from increasingly sophisticated schemes and under-utilized textual data in financial reports. Existing methods often rely on random data splits, leading to overoptimistic performance estimates that do not reflect real-world generalization to new companies or future periods. To address this recurring problem with the state of the art, we propose a robust FSFD framework leveraging Large Language Models (LLMs) to integrate both structured financial data and unstructured textual information from financial reports. We provide a more realistic evaluation through a novel and challenging benchmark task called Company-Isolated FSFD (CI-FSFD). We construct and make publicly available a comprehensive U.S. company dataset combining financial statements, summarized MD&A text, and fraud labels. Our approach achieves the best performance on the challenging CI-FSFD task, demonstrating the critical value of textual data and robust evaluation for reliable financial fraud detection.

AI 总结：研究欺诈检测系统，通过分层管道结合多种方法，包括梯度提升分类器等。在PaySim数据集上实验，各组件在特定条件下起作用，如校正后图特征等未提升全测试集平均精度，但在部分子集有作用，调查代理表现不佳，还标记了其错误。

链接：https://arxiv.org/abs/2607.19266

机构：Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）

作者：Rahil Sharma

英文摘要：Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable. We study a layered pipeline on the PaySim dataset that combines a gradient-boosted classifier, graph-derived structural features, an autoencoder-based anomaly signal, TreeSHAP explanations, and a bounded LLM investigation agent applied to cases the classifier scores uncertainly. Before any model comparison, we identify and remove a simulator-specific balance shortcut that would otherwise inflate baseline performance. After this correction, neither the graph features nor the anomaly signal improves Average Precision on the full test set. Both, however, rank fraud better within the subset of cases receiving intermediate baseline scores. In a controlled experiment with injected multi-account fraud rings, engineered structural features recover all injected test transactions, while the tabular baseline misses roughly a quarter of them. The investigation agent underperforms direct thresholding of the classifier it relies on, reaching 65.0% accuracy against 71.7% on a balanced 60-case sample, despite having access to model explanations, graph context, and retrieved reference cases. Of the eight decisions the agent changed, six replaced correct classifier outputs with errors, and it produced a coherent written rationale in each case. An exploratory disagreement-based escalation rule flagged two of these agent errors for human review without flagging any correct decision. We conclude that each component of a layered fraud system contributes only under specific conditions, and that a plausible rationale from an investigation agent is not evidence of a better decision.

AI 总结：研究校准中错误置信集中问题，引入FALCON-Discover框架，利用多种差异信号排序预测。通过多数据集和强学习器实验发现，错误置信集中依赖模式，基于差异排序在强模式下表现优，最佳检测器因数据集而异，推动针对特定区域的校准策略。

链接：https://arxiv.org/abs/2607.18278

机构：Macquarie University（麦考瑞大学）

作者：Filippo Cenacchi, Longbing Cao, Runze Yang

英文摘要：Calibration is usually evaluated in aggregate, but the most dangerous failures are often local: predictions that remain highly confident despite being wrong. We study this failure mode as false-confidence concentration, the extent to which confident errors occupy compact, discoverable regions of prediction space. We introduce FALCON-Discover, a post-hoc, model-agnostic framework that ranks predictions using discrepancy signals from confidence, local support, neighborhood agreement, and perturbation stability. Across seven binary tabular datasets, four seeds, five-fold cross-fitting, and strong learners including XGBoost and CatBoost, we find that false-confidence concentration is recurrent but regime-dependent. At the main confidence threshold, discrepancy-based ranking substantially outperforms the strongest validation-selected calibration or trust-scoring baseline in the strongest regimes, while raw confidence recovers little dangerous-error mass. The best detector varies across datasets: learned discrepancy is strongest when multiple cues must be combined, whereas stability-centered ranking works best when local decisional fragility dominates. These results show that dangerous overconfidence is better treated as a family-level discovery problem than as a single-score calibration problem, and motivate calibration strategies that explicitly target regions where confidence, support, and stability diverge.

AI 总结：研究针对时间序列分类可靠性问题，提出验证门控固定标签可靠性策略，结合输出端线索与频谱描述符形成可靠性估计，经实验在多个数据集和骨干家族上提升了选择性可靠性指标，验证门控进一步优化了结果。

链接：https://arxiv.org/abs/2607.18279

机构：Macquarie University（麦考瑞大学）

作者：Filippo Cenacchi, Longbing Cao, Runze Yang

英文摘要： Post-hoc calibration for time-series classification usually remaps output scores, but deployment decisions such as trust, abstention, and review depend on whether a confident prediction is supported by the current temporal signal. We address three time-series reliability gaps: identical confidence values can hide different temporal support, average calibration can miss false high-confidence errors, and output-space recalibration offers limited input-linked auditability. We introduce a validation-gated fixed-label reliability policy that keeps the backbone prediction unchanged while estimating whether it should be trusted. The method combines output-side cues with whole-sample spectral descriptors, including band energy, entropy, peak dominance, period support, and phase stability, to form a scalar reliability estimate and diagnostic band-level evidence. A validation gate enables spectral conditioning only when correctness ranking improves without breaching FalseConf@0.9 or AURC tolerances; otherwise it reverts to the safer output-space baseline. Across eight heterogeneous UCR/UEA datasets, eight time-series backbone families, and standard recalibrators, the unconstrained method improves fixed-label selective-reliability metrics on the matched evaluation subset, raising Corr-AURC from 0.693 to 0.779. The validation-gated policy further improves Corr-AURC to 0.786 and reduces FalseConf@0.9 to 0.094. These results suggest that reliability estimation for time-series classifiers benefits from bundling output confidence with spectral evidence, while validation gating prevents unsupported spectral conditioning.

AI 总结：研究大语言模型压缩，提出复合稀疏框架，结合静态参数剪枝和动态令牌级计算，通过低秩近似、通道剪枝及引入轻量级路由器实现。实验表明其在相同总稀疏度下优于单机制压缩，揭示跨维度干扰及有效分配方式，为改进压缩提供实用途径。

链接：https://arxiv.org/abs/2607.18280

机构：Ningbo Institute of Digital Twin, Eastern Institute of Technology（宁波数字孪生研究院，东方理工大学）

作者：Chao Han, Haozhe Hu, Xiaoyu Shen

英文摘要：Large language models (LLMs) are often compressed through static parameter pruning or dynamic token-level computation, yet aggressive sparsification can trigger rapid performance degradation beyond an essential sparsity boundary. This work asks \emph{whether combining these two mechanisms can delay such degradation by distributing the compression burden}. We study a minimalist compound sparsity framework that first applies low-rank approximation and channel pruning to obtain a statically compressed backbone, and then introduces lightweight routers for per-token dynamic layer skipping. This design enables independent control of parameter sparsity and token-level computation sparsity. Experiments across language understanding and modeling benchmarks show that compound sparsity consistently outperforms single-mechanism compression under the same total sparsity, delaying the decay point on understanding tasks and preserving stronger modeling performance. Further analysis reveals cross-dimensional interference between parameter pruning and token skipping, and shows that near-balanced allocation is most effective under a fixed sparsity budget. These results demonstrate that compound compression provides a practical way to improve LLM compression, while revealing a broader cross-dimensional sparsity boundary that ultimately limits further compression. Code will be available at this https URL.

AI 总结：研究语言模型压缩问题，结合神经元重要性和数据感知低秩近似方法，提出动态压缩率分配算法，实验表明该方法在高压缩率下性能与或优于现有技术。

链接：https://arxiv.org/abs/2607.18284

机构：Information Technologies Institute (ITI), Centre for Research and Technology HELLAS (CERTH)（信息技术研究所（ITI），希腊研究与技术中心（CERTH））

作者：Athanasios Ntovas, Alexandros Doumanoglou, Petros Drakoulis, Dimitris Zarpalas

英文摘要：To excel at their domain large language models are comprised of billions of parameters. Yet this comes at the cost of huge memory requirements restricting their applicability in resource-constrained environments. To address the problem of neural network (NN) compression Singular Value Decomposition (SVD) has played a key role as a fundamental component for matrix compression through decomposition. To minimize compression error and to maximize the efficacy of the compressed model on the downstream tasks previous works focused on low-rank approximation of the NN's weight matrices either from the perspective of parameter importance or per-layer functional equivalence. While previous works studied the aforementioned perspectives in isolation in this work we are investigating the effectiveness of an approach that combines ideas from these two perspectives in a single objective. In parallel to this an important aspect that affects the compression quality is the distribution of the compression rate across layers and NN parameters. Earlier works mostly considered distributing the compression rate uniformly across layers and network weights or relied on computationally expensive heuristic search. Contrary to them in this work we propose an enhanced and computationally efficient algorithm for dynamic compression rate allocation. Experimental results support the efficacy of the proposed approach which performs on par or substantially better than the previous state-of-the-art especially under high compression ratios.

AI 总结：针对时变可靠性分析中传统方法不足，提出双域融合长短期记忆（DDF-LSTM）模型，通过新颖架构联合处理时变和非时变域信息，设计改进损失函数，有效捕捉相关依赖关系，经案例验证有更高计算效率和预测准确性。

链接：https://arxiv.org/abs/2607.18291

作者：Yixin Zhang, Mingyang Li, Zichao Jiang

英文摘要： Time-dependent reliability analysis is crucial for ensuring the long-term safety and performance of engineering systems under uncertainties. However, traditional surrogate model methods often struggle to incorporate time-independent random variables and capture their complex interactions with time-dependent stochastic processes. To overcome this limitation, this paper proposes a dual-domain fused long short-term memory (DDF-LSTM) model for efficient and accurate time-dependent reliability analysis. A novel network architecture is developed to jointly process information from both time-dependent and time-independent domains. Specifically, the time-independent variables are embedded into the initial hidden states, and a fully connected layer is introduced to map both LSTM outputs and time-independent variables into the final output space. Furthermore, an improved loss function is designed to emphasize the model's sensitivity to minimum responses, thereby improving the precision of failure probability estimation. The proposed method effectively captures the dependencies among random variables, stochastic processes, and the temporal behavior of limit state functions. Once trained, the DDF-LSTM model enables efficient Monte Carlo simulation to estimate time-dependent failure probabilities with minimal computational cost. Four case studies validate the proposed method's enhanced computational efficiency and predictive accuracy.

AI 总结：研究发现随着语言模型规模扩大，答案虽更接近真实但退化更快。通过逐位置分歧追踪自回归风险残余，揭示了知识差距下降、知识退化增长等四个发现，指出更大模型会通过特定风险机制更快加剧错误，该机制因果且模型自身难以察觉。

链接：https://arxiv.org/abs/2607.18292

机构：Obviously Wrong, LLC（明显错误有限责任公司）

作者：Kushal Chakrabarti

英文摘要：As language models scale, answers start truer but degrade faster: scaling buys capability but erodes reliability. The knowledge-gap account - more data, retrieval, or scale - misses an auto-regressive risk residual that scale sharpens: the model commits to a low-probability token, conditions on it as established, and snowballs. We track this through per-position disagreement $\delta = \log p_M - \log p_O$ against a stronger same-family oracle, whose second moment splits exactly into bias$^2$ $\mathrm{KL}(p_M \,\|\, p_O)^2$ and risk $\mathrm{Var}[\delta]$. We present four findings: (i) under scaling, the knowledge gap falls $\approx$$6\times$ while knowledge degradation grows $11$-$39\times$; (ii) at a fabrication, felt uncertainty $H(p_M)$ relaxes quickly while oracle-referenced risk persists up to $17\times$ longer, leaving a confident-but-precarious risk regime that bridges consecutive fabrications ($+69\%$ at $14$B); (iii) this regime is causal - an on-policy, fixed-$\mathrm{KL}$ variance contraction cuts web-verified hallucination by $35$-$74\%$ across three model families; and, (iv) it structurally evades self-monitoring, with $p_M$-only detectors (e.g. semantic entropy) firing $\approx$$30\%$ less ($p<10^{-16}$) on the risky branch holding nearly $4\times$ more fabrications. Bigger models snowball mistakes faster, through a failure mode that is dominant, self-perpetuating, causal and invisible to the model itself.

AI 总结：研究提出一种多任务在线策略蒸馏方法，教师与学生仅通过可学习软提示区分，在骨干冻结时训练特定任务教师。单任务变体训练参数少且效果好，多任务变体保持通用能力基准同时实现最佳总体平均水平。

链接：https://arxiv.org/abs/2607.18293

机构：University of Wisconsin–Madison（威斯康星大学麦迪逊分校）； Johns Hopkins University（约翰霍普金斯大学）； Nanyang Technological University（南洋理工大学）

作者：Yingzi Ma, Zichen Zhu, Ming Jiang, Chaowei Xiao

英文摘要：On-policy self-distillation (OPSD) teaches large language models new skills through a teacher that shares the student's backbone and supervises its own rollouts. Existing teachers either inject privileged context at the input -- inducing post-hoc rationalization -- or fine-tune weights, accumulating drift and forgetting across tasks. We propose \method, whose teacher differs from the student only by a learnable soft prompt: trained on $(x, y_\text{gold})$ pairs with the backbone frozen, the prompt yields a task-specific teacher that preserves the student's exact representational geometry. \method\ extends naturally to multi-task settings by routing each example in a merged corpus to its corresponding soft-prompt teacher, allowing a single student to absorb knowledge from $K$ teachers in parallel; at inference, all prompts are discarded. On Qwen3-1.7B-Base and Phi-4-mini-instruct across four tasks (Science, Tool Use, Biology, Math), the single-task variant (OPD with a PT teacher) matches or exceeds full fine-tuning while training orders of magnitude fewer parameters, and the multi-task variant achieves the best overall average ($56.2$ on Qwen3-1.7B-Base) while preserving general-capability benchmarks -- in contrast to sequential SFT, which degrades both.

AI 总结：研究在大语言模型中，重塑基础模型输出分布的对齐方案与有界安全过滤器结合能否消除有害行为。通过形式化设置并分析，发现有界过滤可能无法消除所有有害输出，实证评估显示有害输出率始终高于零。

链接：https://arxiv.org/abs/2607.18295

作者：Aryan Dutt, Rui Mao, Anupam Chattopadhyay

英文摘要：We study whether alignment schemes that reshape a base model's output distribution, combined with bounded safety filters, can drive the probability of harmful behavior to zero in modern large language models. Recent research suggests that harmful behaviors can persist under preference-based alignment and that external filtering can be computationally hard in the worst case, but it remains unclear whether practical alignment pipelines that largely preserve internal representations can eliminate harmful behavior entirely rather than merely suppressing its most visible forms. We formalize this setting using support-preserving alignment operators together with bounded filtering algorithms under black-box, white-box, and statistical-query access, and analyze their ability to approximate an ideal eliminator that removes all harmful mass. Building on this framework, we provide computational and information-theoretic arguments indicating that, under these constraints, bounded filtering may fail to eliminate all harmful outputs supported by the base model's distribution. To evaluate these limits empirically, we analyze a range of state-of-the-art open-weight and hosted LLMs accessed via OpenRouter under bounded black-box, white-box, and statistical-query filters on adversarial prompts drawn from curated cybersecurity scenarios and PKU-SafeRLHF. Across models, filter classes, and query budgets, the estimated harmful-output rate decreases with additional filtering compute but consistently plateaus above zero, suggesting a persistent empirical harm floor.

AI 总结：研究针对大语言模型在自闭症干预对话中的谄媚问题，提出最小编辑数据增强策略及令牌级差异直接偏好优化方法，通过加权差异令牌、降权共享令牌抑制背景漂移，经实验验证该方法能在减轻谄媚与保留干预能力间达较好平衡。

链接：https://arxiv.org/abs/2607.18304

机构： Nanhu Brain-Computer Interface Institute（南湖脑机接口研究所）； MOE Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University（浙江大学脑与脑机融合教育部前沿科学中心）； College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）； Children’s Hospital Zhejiang University School of Medicine（浙江大学医学院附属儿童医院）； State Key Laboratory of Brain-Machine Intelligence（脑机智能技术国家重点实验室）； Department of Neurobiology, Affiliated Mental Health Center and Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine（浙江大学医学院附属精神卫生中心和杭州市第七人民医院神经生物学系）

作者：Shuzhong Lai, Junhong Lai, Chenxi Li, Qing Zhou, Haifeng Li, Gang Pan, Lin Yao, Yueming Wang

英文摘要：The sycophancy of large language models can increase the safety risk in intervention dialogue for autistic children. Supervised fine-tuning can somewhat reduce sycophancy, but relying solely on positive examples is often insufficient to identify and correct failure patterns. We observe that sycophancy behaviors can often be localized to a limited span within the model response. In this regime, sequence-level preference optimization can over-update preference-irrelevant tokens and degrade intervention ability. To address this, we propose the \textbf{M}inimal \textbf{E}dit \textbf{D}ata \textbf{A}ugmentation (MEDA) strategy to construct controlled, stable, minimal edit preference pairs and \textbf{T}oken-level \textbf{D}ifference \textbf{D}irect \textbf{P}reference \textbf{O}ptimization (TD-DPO), which upweights difference tokens between chosen and rejected responses while downweighting shared tokens to suppress background drift. Extensive experiments across multiple backbones and evaluators show that TD-DPO achieves a better trade-off between sycophancy mitigation and intervention ability retention in our offline settings, highlighting its potential as a practical alignment approach for autism intervention.

AI 总结：研究神经网络计算图穷举干预成本，提出在反应式图引擎上的成本核算方法，包括扫描加速比、变异成本及反向局部性等，通过NeuroDSL验证相关恒等式。

链接：https://arxiv.org/abs/2607.18323

机构：ISITCOM, University of Sousse（苏塞大学ISITCOM）

作者：Abdallah Khemais (ISITCOM, University of Sousse)

英文摘要：Exhaustive site-by-site interventions on a neural network's computational graph -- activation-patching sweeps, circuit-discovery searches, systematic ablation studies -- mutate the graph at every candidate site, and their cost is dominated by recomputation after each mutation. On a reactive graph engine whose invalidation provably touches exactly the downstream cone of a mutated node, we give a complete cost accounting for such workloads. First, the aggregate speedup of an exhaustive sweep over independent full recomputations is not a universal constant: if per-layer weight varies regularly with depth at Karamata index q, the ratio converges to (q+2)/(q+1) when weight concentrates near the output and to q+2 near the input, recovering 2 only in the depth-uniform case; a wall-clock corollary predicts a ceiling of about 1.79, below 2, until interpreter overhead is compiled away. Second, we prove the exact cost of a sequence of persistent mutations, never undone between insertions: the interleaved cost exceeds the isolated sum by an exact overcount summed over comparable site pairs, with closed-form extremes over insertion orders, while batched application is order-independent and sub-additive, costing exactly the union of the sites' cones plus the fresh nodes. Third, we prove the exact mirror of forward locality for the backward pass, showing it collapses the aggregate speedup to 1 under backpropagation on architectures without long skip connections. Every identity is validated on NeuroDSL, a reactive graph engine in Julia: measured sweep ratios converge to the predicted limits under four cost profiles; the training-mode ratio collapses to 1 at the predicted rate; and all 18 per-graft sequential costs and the batched total match the closed forms at zero tolerance across three insertion orders.

AI 总结：研究锂离子电池SOH和RUL联合预测受任务异方差性阻碍的问题，提出RoSIP-Batt框架，通过贝叶斯多任务目标、同方差不确定性加权等机制平衡任务，在多数据集上显著优于基线，是通用高效适用于实时BMS部署的方案。

链接：https://arxiv.org/abs/2607.18329

作者：Shuhao Chen, Tianyu Shi, Yiwen Huang, Chengyi Tu

英文摘要：The deployment of reliable lithium-ion battery management systems is crucial for accelerating electrification, yet the joint prognosis of State of Health (SOH) and Remaining Useful Life (RUL) remains severely hindered by task heteroscedasticity. Conventional multi-task learning frameworks fail to balance the bounded, low-variance noise of SOH estimation with the unbounded, nonlinearly expanding uncertainty of long-term RUL predictions. Here, we present the Rotary SOH-Injected Prior Battery Transformer (RoSIP-Batt), a unified co-estimation framework that resolves these optimization conflicts. By formulating joint prediction as a Bayesian multi-task objective, RoSIP-Batt introduces a homoscedastic uncertainty weighting mechanism to dynamically scale task-specific gradients based on learned residual noise levels. The architecture leverages decoupled dual classification tokens and a per-dimension gated fusion mechanism, secured by a gradient-detachment operator to prevent high-variance RUL updates from corrupting the stable SOH representation space. To capture electrochemical degradation patterns without relying on absolute cycle steps, Rotary Position Embedding (RoPE) is incorporated into a shared Transformer backbone to model translation-invariant relative temporal profiles. Crucially, the intermediate SOH estimate is directly injected into the RUL regression head as a physical degradation prior. Evaluations across the NASA, MIT-Stanford, and HUST datasets show that RoSIP-Batt significantly outperforms state-of-the-art baselines, reducing SOH estimation error to 1.994% MAE on NASA and restricting RUL prediction error to 62.85 cycles on Stanford. These findings establish RoSIP-Batt as a highly generalizable, computationally efficient solution suitable for real-time embedded BMS deployment.

AI 总结：研究锂离子电池SOH和RUL联合预测受任务异方差性阻碍的问题，提出RoSIP-Batt框架，通过贝叶斯多任务目标、同方差不确定性加权等机制，结合RoPE等，有效平衡任务，在多数据集上显著优于基线，是高效通用的实时BMS解决方案。

链接：https://arxiv.org/abs/2607.18330

作者：Shuhao Chen, Tianyu Shi, Chengyi Tu

英文摘要： The deployment of reliable lithium-ion battery management systems is crucial for accelerating electrification, yet the joint prognosis of State of Health (SOH) and Remaining Useful Life (RUL) remains severely hindered by task heteroscedasticity. Conventional multi-task learning frameworks fail to balance the bounded, low-variance noise of SOH estimation with the unbounded, nonlinearly expanding uncertainty of long-term RUL predictions. Here, we present the Rotary SOH-Injected Prior Battery Transformer (RoSIP-Batt), a unified co-estimation framework that resolves these optimization conflicts. By formulating joint prediction as a Bayesian multi-task objective, RoSIP-Batt introduces a homoscedastic uncertainty weighting mechanism to dynamically scale task-specific gradients based on learned residual noise levels. The architecture leverages decoupled dual classification tokens and a per-dimension gated fusion mechanism, secured by a gradient-detachment operator to prevent high-variance RUL updates from corrupting the stable SOH representation space. To capture electrochemical degradation patterns without relying on absolute cycle steps, Rotary Position Embedding (RoPE) is incorporated into a shared Transformer backbone to model translation-invariant relative temporal profiles. Crucially, the intermediate SOH estimate is directly injected into the RUL regression head as a physical degradation prior. Evaluations across the NASA, MIT-Stanford, and HUST datasets show that RoSIP-Batt significantly outperforms state-of-the-art baselines, reducing SOH estimation error to 1.994% MAE on NASA and restricting RUL prediction error to 62.85 cycles on Stanford. These findings establish RoSIP-Batt as a highly generalizable, computationally efficient solution suitable for real-time embedded BMS deployment.

AI 总结：研究联邦微调通信瓶颈问题，提出基于映射网络的方法，通过低秩分解和增量公式等改进，实现低带宽通信，在CIFAR - 网络上大幅减少通信量，准确率与全权重FedAvg相近，在带宽 - 准确率方面表现优异。

链接：https://arxiv.org/abs/2607.18343

机构：Cisco Systems Inc.（思科系统公司）

作者：Radhakrishna Achanta, Will Reed

英文摘要：Federated fine-tuning is bottlenecked by communication: FedAvg and pseudo-gradient schemes transmit a payload that scales with the model, and gradient compression shrinks it by only a constant factor. We take a different lever. Mapping networks generate a network's weights from a small trainable latent through a frozen affine projection; because the map is shared and affine, averaging latents is exactly averaging the generated weights. We turn this into a practical low-bandwidth federated channel with two changes: a low-rank, seed-regenerable factorisation of the projection (cutting generator memory from ~80 GB to ~10 MB), and a delta formulation $\theta = \theta^{\mathrm{pre}} + U V^{\top} z$ that learns an additive correction around a shared centrally-pretrained base -- federated fine-tuning, which is what makes the method work at scale. A frozen orthogonal classifier head further removes the head from the payload while improving accuracy. On CIFAR-100 with ResNet-18+GroupNorm, our method (FLITE, Federated Low-rank Iterative Training Engine) communicates 1,280 floats (~5 KB) per client per round -- an 8718x reduction -- and reaches 74.67%, within ~0.5 pp of full-weight FedAvg. The averaging identity holds to floating-point precision ($6 \times 10^{-8}$); the method sits one to two orders of magnitude below PowerSGD and top-k on the bandwidth-accuracy Pareto; it matches or exceeds full-weight FedAvg under strong non-IID skew. int4 latents reach 648 bytes per round at unchanged accuracy, whereas int4 full-weight FedAvg collapses to chance.

AI 总结：研究跨Transformer深度的残差流几何规律，提出以转换为中心的分析方法，通过相对位移等测量揭示规律，如相对位移与层有关、旋转幅度恒定等，构建了测量框架，表明深度曲线依赖模型且条件稳定。

链接：https://arxiv.org/abs/2607.18348

机构：ProRata AI（ProRata人工智能公司）

作者：Sunit Bhattacharya, Ravi Shankar Kolli

英文摘要：We propose a transition-centred geometric analysis of transformer residual streams. Relative displacement measures how \emph{far} representations move between consecutive layers, and orthogonal Procrustes analysis separates each transition into a rigid rotation and a non-rigid residual. Across six instruction-tuned models, on code generation and cross-lingual translation, these measurements reveal reproducible depth regularities. Relative displacement is strongly layer-dependent; typically larger early and late, with a quieter middle third; and nearly invariant across conditions within each model. Rotation magnitude is nearly constant across depth, while Procrustes residual and angle concentration remain depth-modulated, with residual peaking at the final transition. During generation, non-English targets show larger final-layer displacement and residual than English targets. We present these as descriptive geometric regularities, not as measures of computational effort or causal explanations. The contribution is a measurement framework for residual-stream transitions and evidence that, in the settings studied here, depth curves are model-dependent and largely condition-stable.

AI 总结：研究针对前馈网络占Transformer多数非嵌入参数却缺相关必要性测试的情况，预训练仅注意力机制的解码器Transformer并与标准Transformer对照，发现删除前馈层成本高，重分配预算可缩小差距，还定位了剩余差距原因，预注册测试证实相关解释。

链接：https://arxiv.org/abs/2607.18363

作者：Henry Ndubuaku, Karen Mosoyan, Jakub Mroz, Noah Cylich, Satyajit Kumar, Parkirat Sandhu, Roman Shemet, Justin H Lee

英文摘要：Feed-forward networks hold two thirds of a transformer's non-embedding parameters, yet the architecture has not received a necessity test that controls parameters, compute, and depth at once. We pretrain attention-only decoder transformers (Simple Attention Networks, SANs) against standard transformers matched separately for parameter count, training FLOPs, and depth (2 to 48 layers), for up to 105B tokens at 6M to 87M parameters. Deleting feed-forward layers in place is costly: the standard transformer leads by 0.47 nats at matched depth and 0.26 nats at matched FLOPs. Reallocating the freed budget into attention depth closes the gap: at matched parameters the difference is 0.006 nats (0.27 percent of loss), reproducible to one part in ten thousand across seed pairs, shrinking across 5B, 30B, and 105B budgets, and holding near 0.02 nats across a 29x size range. Three measurements localize the remaining gap to parametric recall: attention-only models are better on context-grounded answers and worse where knowledge must come from weights. Weight spectra show why: routing matrices (Q/K) crystallize early, content matrices accumulate rank slowly, and removing feed-forward layers relocates this accumulation to the attention output projection. QK-normalization, not feed-forward layers or residual gating, keeps 48-layer attention-only stacks trainable. The deficit concentrates on low-context query prediction and localizes there entirely by the largest budget. A pre-registered test confirms the account: it predicts a 0.02 to 0.05 nat gap on knowledge-dense web text; a matched pair trained on fineweb-edu measures 0.040. Within the tested regime, attention does the rest.

AI 总结：研究探讨冻结基础编码器问题，引入CANDOR度量，其等规模库对称使机会水平固定为二分之一。经多编码器、多数据集和大量图像实验，校正改变结论，发现各编码器虽不盲目但都弱，CANDOR可提前标记支持不佳的发现。

链接：https://arxiv.org/abs/2607.18451

作者：Soroosh Tayebi Arasteh, Sven Nebelung, Daniel Truhn

英文摘要：Frozen encoders are chosen by how well a lightweight head reads a finding from their features, not whether the geometry separates it. Nearest-neighbor discordance does, but with unequal banks the opposite-label neighbor wins on density, not geometry, so prevalence alone makes an uninformed encoder look blind. We introduce CANDOR, a discordance measure whose equal-size banks are symmetric under a label swap, fixing its chance level at exactly one half. Across 22 encoders, 20 datasets from 7 domains, and 605,443 images, this correction reverses the conclusion. Collapse falls below chance almost everywhere, so no encoder is blind, yet all are weak: the best chest model reads pneumothorax at 84.5 AUROC and still places 18.4% of those positives nearer an opposite-label film than its own kind in the same hospital. The same encoder that resolves bird species at 4.5 leaves chest findings at 42.8 and glaucoma at 49.8, at chance and worse than random weights. Such a case caps the normalized margin of any Lipschitz head, yet some head among eleven is correct on all but 2.8% of cases where one head misses 35.9%: the deficit is selection, not information. Erasure retention is associated with collapse; we detect no association with the objective, scale, recency, or size of the finding. Because the chance level is fixed, CANDOR can be read before any head is trained, flagging which findings a frozen encoder supports poorly.

AI 总结：针对运营决策中预测模型训练的数据不对称问题，提出决策感知弱到强（W2S）框架，利用有标签和无标签数据训练模型，建立相关风险上下界得出性能改善条件，通过实验验证理论。

链接：https://arxiv.org/abs/2607.18467

机构：Stanford University（斯坦福大学）

作者：Jingwei Ji, Renyuan Xu

英文摘要：Many operational decisions rely on predictive models that estimate uncertain outcomes conditional on observable contexts. Training such models, however, often faces a fundamental data asymmetry: labeled outcomes are scarce or costly to obtain, while contextual covariates are abundant. Motivated by this data asymmetry, we develop a decision-aware weak-to-strong (W2S) framework that leverages both labeled and unlabeled data to improve contextual stochastic optimization. Specifically, we first train a weak model using limited labeled data and then use it to generate predicted outcome distributions on unlabeled contexts. These distributions provide soft supervision for training a strong model. We establish a non-asymptotic upper bound on the excess decision risk of W2S and a complementary lower bound for a strong-only benchmark. Their comparison yields explicit sufficient conditions under which W2S improves downstream decision performance. The key quantity is the correlation dimension between the weak and strong feature representations: when it is small, abundant unlabeled data reduce the effect of teacher errors along non-overlapping directions. A synthetic newsvendor experiment and a comment moderation experiment based on real-world data provide empirical evidence consistent with the theory.

AI 总结：研究从数据中进行符号化控制方程发现的可识别性极限，表明吸引子几何中的$\lambda_{\min}(M)$为SINDy和PySR设定上限，混沌对二者影响不同，还引入Soft F1，指出发现首要问题是吸引子允许什么，而非算法。

链接：https://arxiv.org/abs/2607.18490

机构：University of Trieste（的里雅斯特大学）； National Institute of Oceanography and Applied Geophysics - OGS（国家海洋学与应用地球物理研究所 - OGS）； National Biodiversity Future Center（国家生物多样性未来中心）

作者：Matteo Gallo, Fabio Anselmi, Paolo Lazzari

英文摘要：Symbolic discovery of governing equations from data is limited not only by algorithm design and data volume, but by the geometry of the attractor: what the long-run dynamics allow to be recovered. Using a within-system design on Lorenz-84, where one forcing parameter drives fixed-point, limit-cycle, and chaotic regimes while the governing equations and library stay fixed, we show that a single number, $\lambda_{\min}(M)$, the smallest eigenvalue of the invariant-measure moment matrix, sets the identifiability ceiling for both sparse regression (SINDy) and evolutionary symbolic regression (PySR). Derived from the Birkhoff ergodic theorem and obtained from a short reference trajectory before any run, $\lambda_{\min}(M)$ measures how fully the attractor covers function space: where it vanishes, recovery is impossible for any algorithm, sparse or combinatorial alike; as it grows, both algorithms improve. Chaos raises $\lambda_{\min}(M)$ by spreading the attractor, but also enlarges it and amplifies noise; because noise enters SINDy's regression bottleneck linearly and PySR's discrimination channel superlinearly, the same transition can push the two methods in opposite directions, so deeper chaos is not uniformly better. Parameter-free mechanistic scores from this framework transfer without refitting to a held-out Lorenz-96 system, confirming mechanism rather than curve-fitting; a criterion read from the equations predicts when added chaos will not improve conditioning. We also introduce Soft F1, a coefficient-weighted structural metric that resolves performance differences invisible to binary-success and predictive scores. The first question of discovery is then not which algorithm, but what the attractor permits.

AI 总结：通过对TerraMind和THOR两个地理空间基础模型对比，研究其在补丁大小、解码器复杂性等方面的差异轴，发现架构设计选择对性能差异影响更大，体现互补投资策略，还得出假设和诊断消融方法，有望推广到未来模型。

链接：https://arxiv.org/abs/2607.18504

机构：University of Münster（明斯特大学）； IBM Research（IBM研究院）； Norwegian Computing Center（挪威计算中心）

作者：Frederick Schindlegger, Kenzo Bounegta, Eva Gmelich Meijling, Johannes Jakubik, Arnt-Børre Salberg, Theodor Forgaard, Nicolas Longepe, Valerio Marsocci

英文摘要： Benchmarks for Geospatial Foundation Models (GFMs) increasingly rank models by aggregate score, but such rankings obscure why models differ: how much of the gap is architecture, how much is decoder capacity, and how much is a use-case-specific artefact? This study addresses that gap through a controlled comparison of two GFMs developed under European Space Agency's $\Phi$-lab with contrasting design philosophies: THOR, which introduces a compute-adaptive architecture supporting variable patch sizes and unifies Sentinel-1, -2, and -3 data at their native resolutions; and TerraMind, a multimodal generative GFM pretrained with a dual-scale token/pixel objective that enables any-to-any cross-modal generation (Thinking-in-Modalities) to infer missing sensors at inference time. Rather than reporting a single leaderboard, we investigate the axes along which the two architectures actually differ - patch size, decoder complexity, finetuning regime, input modality, and model scale - across ten use cases spanning segmentation and regression in diverse domains, including climate disaster response, methane leak detection, snow monitoring, or sea ice mapping. We find that architectural design choices - patch size and decoder type in particular - explain more performance variance than model identity itself, that the two models embody complementary investment strategies (pretraining-time scale for TerraMind versus inference-time tokenisation for THOR), and that correctly interpreting results requires dataset-level characterisation. The resulting picture is not a single winner but a set of hypotheses and a diagnostic ablation methodology that we expect to generalise to future GFMs beyond THOR and TerraMind.

AI 总结：研究提出带符号整流流（Signed RF），通过推广整流流，基于带符号测度构建生成模型，能将概率集中在正区域并排除负区域。分析其连续性方程并给出解释，推动实用算法，在多应用中提升性能，改进了保真度 - 多样性权衡等。

链接：https://arxiv.org/abs/2607.18516

机构：UT Austin（德克萨斯大学奥斯汀分校）

作者：Runlong Liao, Baiyu Su, Lizhang Chen, Qiang Liu

英文摘要：We introduce Signed Rectified Flow (Signed RF), a generalization of Rectified Flow that targets the signed measure $\pi^{sign} = (1+\alpha)\pi^+ - \alpha\pi^-$, where $\alpha>0$, $\pi^+$ is the distribution to promote, and $\pi^-$ is the distribution to suppress. Although direct sampling from a signed measure is not well-defined, Signed RF induces a valid generative process that concentrates probability in regions where the signed measure is positive while provably excluding regions dominated by its negative component. It therefore provides a principled framework for incorporating negative information and exclusion constraints into generative modeling. We analyze the signed continuity equation underlying Signed RF and use a charged-particle interpretation to explain how negative mass forms exclusion barriers. This theory further motivates practical adaptive guidance algorithms. Across several applications, Signed RF improves the fidelity-diversity trade-off on ImageNet, reduces nearest-neighbor similarity in anti-memorization experiments, and reduces nudity induced by adversarial prompts in Stable Diffusion 3.5 while preserving CLIP and aesthetic scores.

AI 总结：针对移动核心网络中基于流量的故障检测难题，提出两阶段在线学习框架。第一阶段用带时间感知特征的轻量级回归建模正常流量动态，第二阶段结合上下文指标分析预测残差。该框架在线运行，在多种模型中实现最佳精确率-召回率权衡，凸显残差分解对故障检测的重要性。

链接：https://arxiv.org/abs/2607.18522

机构：Vodacom Group Limited（沃达康集团有限公司）

作者：J. du Toit, G. Fita, J. Salzwedel, A. Stoltz, R. Wolhuter

英文摘要：Mobile network operators monitor aggregated traffic volumes to assess the operational health of core network infrastructure. Reliable failure detection is challenging due to strong temporal structure, non-stationarity, measurement artefacts, and extreme class imbalance, which limit static threshold-based monitoring. This paper proposes a two-stage online learning framework for traffic-based failure detection in mobile core networks. Stage I incrementally models normal traffic dynamics using lightweight regression with time-aware features. Stage II analyses prediction residuals together with contextual indicators to detect genuine service-affecting network failures. The framework operates fully online under a prequential evaluation protocol, enabling continuous adaptation with low computational overhead. Across linear and non-linear models, the proposed two-stage architecture achieves the best precision-recall trade-off, attaining the highest recall, F1-score, and AUC at acceptable false positive rates. These results demonstrate the importance of explicit residual decomposition for reliable failure detection in streaming mobile core network data.

AI 总结：研究针对AMICA算法依赖单一Fortran实现、限制其在非MATLAB生态系统中应用的问题，提出AMICA-Python这一Python实现，通过符合scikit-learn的API集成，引入安德森加速方案，经测试其数值精度高、运行时具竞争力。

链接：https://arxiv.org/abs/2607.18568

机构：University of Southern California（南加州大学）； University of South Carolina（南卡罗来纳大学）

作者：Scott Huberty, Christian O'Reilly

英文摘要：Adaptive Mixture Independent Component Analysis (AMICA) is widely used in EEG research and has long been associated with strong empirical performance for blind source separation. Despite its impact, practical use has historically depended on a single Fortran implementation, accessed via the EEGLAB toolbox for MATLAB, limiting its accessibility for analytical pipelines not designed within the MATLAB ecosystem. Here we present AMICA-Python, a Python implementation of the AMICA algorithm, with a scikit-learn-conformant API designed for integration with existing scientific Python pipelines. The implementation follows the reference algorithm closely while adopting modern software engineering practices and an interface familiar to Python users. Additionally, we introduce an optional Anderson acceleration scheme that can dramatically reduce the time to convergence for this relatively slow algorithm. To evaluate numerical agreement and practical performance, we benchmarked AMICA-Python against the reference Fortran implementation on 14 open EEG recordings. After averaging 3 runs of each implementation on all 14 recordings, AMICA-Python closely matched the reference, with a median final normalized log-likelihoods of 11.572 for both the Fortran and Python implementations, and a negligible median relative absolute difference of only $1.07\times10^{-8}$ when normalized by the absolute Fortran value. Runtime was also competitive. Relative to the reference implementation, AMICA-Python was 17.7\% faster, while the Anderson-accelerated variant was 34.1\% faster. AMICA-Python reproduces the reference implementation to high numerical precision with competitive runtime, while making AMICA available through a more accessible and extensible Python interface.

AI 总结：研究直接反馈对齐（DFA）训练失败模式，通过分析各向异性进入局部权重更新的方式，提出条件DFA，经实验验证其在不同模型上有效，还介绍了归一化DFA家族及相关计算动机，强调是对局部外积规则失败的因子级研究。

链接：https://arxiv.org/abs/2607.18574

机构：Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University（哈佛大学坎普纳自然与人工智能研究所）； Howard Hughes Medical Institute, Harvard Medical School（哈佛医学院霍华德·休斯医学研究所）

作者：Houman Safaai, Varun Reddy, Bernardo L. Sabatini

英文摘要：Direct feedback alignment (DFA) trains hidden layers with fixed random projections of the output error, avoiding the transposed-weight backward pass of backpropagation (BP). We study a failure mode of DFA training that is distinct from feedback quality: the local weight update is calculated by an outer product, so anisotropy can enter through either its presynaptic-activity factor or its local-error factor. Our analyses with controlled synthetic regimes isolate the first failure mode and show an approximately 40-percentage-point activity-conditioning gain when high-variance directions contain task-irrelevant nuisance. Three clean confirmations isolate a different regime: error conditioning improves raw DFA by 1.77--7.53 percentage points, and combining independently selected activity and error factors adds 0.40--0.90 points over activity conditioning. The signs hold for tanh/one-vs-rest MNIST and preregistered Fashion-MNIST, and replicate on eight fresh seeds in a ReLU/softmax MNIST model. This factorization yields a symmetric block-local family of normalized DFA (nDFA): activity nDFA right-preconditions by an inverse activity second moment, error nDFA left-preconditions by an inverse local-error second moment, and K-nDFA applies both factors with separately tuned damping. A linearized post-alignment calculation gives an exact input-side spectral identity and a Kronecker-factor motivation for the two-sided rule, whereas norm matching rules out a scalar step-size explanation. The error factor is fragile when under-damped, BatchNorm is a strong activity-side alternative, and convnet gains remain partial. We therefore frame conditioned DFA as a factor-level study of when local outer-product rules fail, not as a general replacement for BP or a solution to all-layer convolutional credit assignment.

AI 总结：研究针对协同基因调控依赖联合作用调控因子组，但多数基因调控网络推断方法输出两两调控因子-靶点排名的问题，引入BRIDGE框架及TRACE诊断套件，介绍其核心机制，指出该方法提升了多项指标，区分了相关不同目标。

链接：https://arxiv.org/abs/2607.18602

机构：University of Central Florida（中佛罗里达大学）

作者：Maryam Rahimimovassagh, Clayton Thomas Barham, Ivan Garibay, Niloofar Yousefi

英文摘要：Cooperative gene regulation often depends on groups of regulators acting jointly, but most gene regulatory network (GRN) inference methods output pairwise regulator-target rankings. We introduce Bottleneck-Aware Regulator-Set Inference and Diagnosis (BRIDGE), a framework for complete regulator-set recovery, and Targeted Recovery Attribution for Cooperative Evaluation (TRACE), a diagnostic suite that attributes failures to retrieval, set-level scoring, decoding, and evaluation bottlenecks. TRACE includes a leak-free mechanism-mismatch cooperativity stress test in which cooperative targets are generated by random nonlinear mechanisms rather than product interactions. This design avoids feature-mechanism circularity: Residual higher-order set scoring (Residual HOS2) operates on raw expression vectors without handcrafted product-correlation features. Across 30 matched seed-cooperativity settings, Residual HOS2 improves Jaccard similarity from 0.382 to 0.460, recall from 0.522 to 0.597, and exact recovery from 0.053 to 0.113 over a decomposable pairwise set scorer (PairS2), although exact recovery remains low. On SERGIO DS3, oracle retrieval and TRACE show that candidate coverage is necessary but insufficient because set-level misranking remains the dominant source of exact-recovery failure. PairS2 proposal followed by Residual HOS2 reranking reduces HOS2-scored candidate sets by 94-97% while largely preserving exact-recovery behavior. These results distinguish edge ranking, candidate retrieval, set-level scoring, and exact cooperative regulator-set recovery as separate objectives.

AI 总结：研究针对大语言模型两用知识的安全干预问题，提出“令牌接种”方法，通过绑定和分支操作，在保留有害知识的同时实现选择性拒绝，在有害领域降低准确率，保持良性领域性能，取得较好安全效用权衡，且拒绝选择性可控。

链接：https://arxiv.org/abs/2607.18639

机构：NAVER AI Lab（NAVER人工智能实验室）

作者：Seunghyun Lee, Dongyoon Han, Sangdoo Yun

英文摘要：Safety interventions on dual-use knowledge typically choose between destroying hazardous content (e.g., unlearning, filtering) and suppressing it at the output layer (e.g., refusal training); both pay a tax in adjacent-domain competence or over-refusal. We argue that the right operation is conditioning, not reduction: we show that hazardous knowledge can be retained in the model and behaviorally gated by a privileged control token. Our method, Token Inoculation, introduces a binding-and-branching approach. First, during continued pre-training, we mark hazardous content by inserting a special token alongside dual-use documents, so the model binds the marker to the underlying semantics of the hazardous domain. Second, during supervised fine-tuning, we teach the model to answer hazardous queries correctly when the special token is present and to refuse them when it is absent, thereby enabling selective refusal without removing dual-use knowledge. On hazardous domain (e.g., WMDP-Bio), Token Inoculation reduces accuracy from 79% to 18% while retaining 93% of the base-model's benign-domain performance (e.g., MMLU), achieving the best safety-utility trade-off against unlearning and refusal-tuning baselines across 1B-14B model scales. We further show that refusal selectivity is controllable through the quality of the conditioning signal and that domain-specific semantic binding during pre-training is critical for the conditional behavior to generalize beyond memorized triggers. Our results suggest that safety alignment is better cast as a conditioning problem than a forgetting one: behavioral control is more precise when sensitive knowledge is retained under controlled access than when it is destroyed.

AI 总结：研究两个因子均量化时\(C = AB\)的低精度计算，利用乘积保持等价关系制定收缩规范预处理，推导可计算选择统计量，给出上界用于排序候选者，并通过实验验证该方法在降低乘积误差等方面的有效性。

链接：https://arxiv.org/abs/2607.18745

机构：UT-Battelle, LLC（UT-巴特尔有限责任公司）； Oak Ridge National Laboratory（橡树岭国家实验室）

作者：Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara, Keita Teranishi, Sudip Seal

英文摘要：We study low-precision computation of C=AB with both factors quantized. We derive an exact finite-dimensional identity for the expected squared product error under independent, zero-mean entrywise errors with known variance fields; it holds exactly for non-overloading subtractive dither and for independent stochastic rounding, and we empirically assess deterministic round-to-nearest (RTN). Using the product-preserving equivalence AB=(AT)(T^{-1}B), we formulate contraction-gauge preconditioning: jointly choosing a factor representation and its sharing pattern before quantization. Preconditioning can reduce product error but may require extra transformed, quantized copies of the opposite operand: a shared transform needs one copy, a block-specific transform up to one per block. Within the bounded family of positive diagonal gauges (folds), a geometric program computes a globally optimal shared fold and a linear program decides whether the identity fold is already optimal. For other families we derive computable selection statistics -- tail index for scaling, profile spread for partitioning, coherence and weighted-Gram energy for rotations, slice-energy covariance for hierarchy depth -- with upper bounds for ranking heuristic candidates. Across twelve linear products from a trained three-block image classifier, median within-product rank correlations between dither-model predictions and deterministic-RTN errors are 0.937 at 8 bits and 0.918 at 4 bits. The GP fold cuts held-out product error over the identity fold by 18.0% (8-bit) and 20.5% (4-bit) in geometric mean, beats a SmoothQuant-style grid baseline at both precisions and on ten of twelve products, and lowers composed logit MSE by 15.4% and 26.4%. We thus provide exact stochastic product-error accounting, certified selection within the diagonal family, and a common objective for evaluating reusable transform candidates under RTN.

AI 总结：研究针对时间序列可解释性问题，提出ConceptCF方法，通过时间序列分解构建概念，利用遗传算法生成反事实，经与五种先进方法对比评估，该方法在多指标上表现出色，能有效提供基于概念的反事实解释。

链接：https://arxiv.org/abs/2607.18748

机构：Saxion University of Applied Sciences（萨克逊应用科学大学）； University of Twente（特温特大学）

作者：Annemarie Jutte, Faizan Ahmed, Jeroen Linssen, Maurice van Keulen

英文摘要：This paper proposes ConceptCF, a method for counterfactual generation that operates on human-interpretable concepts. In high-stakes domains such as healthcare and predictive maintenance, artificial intelligence models can increase efficiency and safety. Explainability is key to ensure these models rely on causal relationships rather than spurious correlations. Counterfactual explanations identify minimal modifications that would change a model's predictions. Existing methods for time series operate on individual points or subsequences without ensuring interpretability of the mutations. ConceptCF instead modifies meaningful concepts. As a result we can provide explanations in terms of these concepts, for example ``the model's prediction would be `Sit' instead of `Walk' if you increase the scale of the movement''. In this paper, the concepts are constructed through time series decomposition, resulting in concepts such as scale, and frequency bands. Counterfactuals are generated using a genetic algorithm that optimizes the concept mutations. Evaluation against five state-of-the-art approaches demonstrates that ConceptCF consistently achieves top-tier performance across validity, confidence, proximity, sparsity and plausibility metrics.

AI 总结：研究针对基于流的模型潜在嵌入难解释及生成因素纠缠问题，提出基于李群的新型条件生成器，通过对抗损失使替代潜在空间与潜在流空间对齐，实现可解释条件生成，在图像和分子生成任务中性能强大。

链接：https://arxiv.org/abs/2607.18755

机构：Aalto University（阿尔托大学）； Orion Pharma（奥立安制药公司）； ARF（未提及具体中文译名，保留英文缩写）； University of Manchester（曼彻斯特大学）； Yai Yai Ltd（未提及具体中文译名，保留英文缩写）

作者：Anirudh jain, Sakshi Varshney, Samuel Kaski, Vikas Garg

英文摘要：Flow-based models have established state-of-the-art performance in generative modeling across domains, but are hard to interpret due to their complex latent embeddings. In particular, the entanglement of generative factors in the latent space hinders controlled generation. We circumvent this issue by appealing to a novel conditional generator based on Lie groups that disentangles an alternative latent space, which is aligned closely with the latent flow space using an adversarial loss. Our approach facilitates interpretable conditional generation while obviating the need to expand the dimensionality of the flow space (owing to its invertibility requirements). The proposed model demonstrates strong performance across conditional image (including, outperforming StyleGAN on MNIST, dSprites) and molecule (using standard QM9, ZINC and MOSES) generation tasks

AI 总结：研究利用潜在后验模型提出后验前缀调整（PPT）方法，在贝叶斯滤波变压器中，给定效用函数找高预期效用提示。通过预测蒙特卡洛抽取先验样本，用重要性采样估计梯度，无需前向传递和反向传播，一组样本可驱动针对多种效用的启发并验证。

链接：https://arxiv.org/abs/2607.18804

机构：Monash University（莫纳什大学）； Timaeus（提马尤斯）

作者：Garrett Baker, Vinayak Pathak, Daniel Murfet, Susan Wei

英文摘要：In the \emph{latent posterior model} of transformer behavior, the next-token distribution arises from a posterior over latent predictive models conditioned on the context, mixed to generate continuations. We exploit this model in settings where it is exact, namely Bayes-filtered transformers (BFTs) meta-learned on sequences from a hierarchical prior, to introduce \textbf{Posterior Prefix Tuning (PPT)}, a new method for \emph{eliciting} behavior from a transformer: given a utility function on continuations, find a prompt under which the transformer generates continuations of high expected utility. For a BFT, the elicitation objective factors through the latent posterior, and the gradient of this objective can be estimated from samples of the prior alone. PPT optimizes the parameters of a distribution over hard prompts: it draws prior samples once from the BFT via predictive Monte Carlo (PMC), then estimates the gradient by importance sampling against them. The optimization performs no transformer forward passes and no backpropagation through the transformer, and the prior samples are utility-independent, so a single set of samples drives elicitation against any number of utilities at negligible marginal cost. We validate PPT on Beta--Bernoulli and reinforced urn BFTs across three utility families (reverse cross-entropy, frequency matching, Dyck validity).

AI 总结：研究针对抗体CDR设计，提出基于策略蒸馏的ABOPD框架，利用天然几何结构监督模型去噪轨迹，提升了RAbD CDR-H3生成的结构恢复，降低均方根偏差，优于其他控制方法，为高保真蛋白质设计开辟道路。

链接：https://arxiv.org/abs/2607.18835

作者：Zhuo Yang, Jiaying He, Jiaqing Xie, Daolang Wang, Xipeng Qiu, Yuxin Wang, Tianfan Fu, Beilun Wang

英文摘要：Antibodies are essential therapeutic molecules, and their complementarity-determining regions (CDRs) form the primary antigen-recognition interface. Recent protein generative models have demonstrated broad capabilities in biomolecular design, yet post-training strategies for downstream objectives remain limited. Standard denoising training operates on noisy states obtained by perturbing native structures, whereas recursive generation proceeds through model-generated intermediate states. For flexible antibody CDR loops such as CDR-H3, this mismatch can allow backbone deviations to accumulate along the denoising trajectory and compromise antigen-facing loop geometry. We introduce ABOPD, an antibody design framework based on on-policy distillation that leverages privileged native geometry during training to supervise states visited along the model's own denoising trajectories. With this fine-grained structural supervision, ABOPD substantially improves structural recovery on RAbD CDR-H3 generation, reducing RMSD by 0.42 Å (from 2.37 Å to 1.95 Å) and outperforming supervised fine-tuning and offline distillation controls, offering a path to higher-fidelity protein design.

AI 总结：研究锂离子电池热失控早期预警，提出状态感知物理引导框架，整合多测量信号，经卷积分类器和因果时间卷积主干联合学习，在多条件测试中取得良好效果，支持该融合策略用于更可靠预警。

链接：https://arxiv.org/abs/2607.18860

作者：Syed Sajid Ullah, Muhammad Zunair Zamir, Salman Khan

英文摘要：Thermal runaway in lithium-ion batteries poses a major safety risk to electric vehicles and energy storage systems. Current early-warning methods depend mainly on temperature and may therefore miss mechanical precursors that emerge before rapid heating. We introduce a regime-aware, physics-guided framework that integrates temperature, voltage, force, deformation, and state-of-charge measurements for early warning under controlled mechanical abuse. A lightweight convolutional classifier first infers safe, warning, or danger regimes from mechanical signals. These regime estimates then condition a causal temporal convolutional backbone through feature-wise linear modulation, physics-biased attention, and regime-dependent gating. Joint learning unifies regime identification, thermal-runaway detection, and time-to-disaster estimation. We evaluate the framework using leave-one-experiment-out cross-validation on 30 mechanical-abuse tests across state-of-charge levels of 10%, 50%, and 90% and two loading protocols. The method achieves an F1 score of 0.89, a high-temperature prediction root-mean-square error of 12.3 °C, a mean warning lead time of 15.6 s, a detection success rate of 0.92, and an experiment-level false alarm rate of 2.7%. Its lead time exceeds that of the strongest baseline by 69.6%. Removing force reduces the lead time by 60.3%, highlighting the value of mechanical precursors. These results support regime-aware thermo-mechanical fusion as a promising strategy for earlier and more reliable thermal-runaway warning under controlled abuse conditions.

AI 总结：针对全球变暖下大气数据超分辨率问题，提出基于原始方程的PISR方法及NPC度量，通过约束SR模型遵循物理方程，实验表明该方法能提升重建保真度，增强极端事件检测能力。

链接：https://arxiv.org/abs/2607.18877

机构：EPFL（洛桑联邦理工学院）； University of Cambridge（剑桥大学）

作者：Chang Xu, Gencer Sumbul, Hugo Porta, Manon Béchaz, Sebastian Schemm, Devis Tuia

英文摘要：In the context of global warming, extreme events have become more frequent and intense, making their trustworthy detection and forecasting more important than ever. Yet, atmospheric observations lack sufficient spatial resolution, motivating atmospheric data downscaling as a way to reconstruct high-resolution data from coarse observations. This task is now being formulated as a super-resolution (SR) problem with machine learning methods featuring high efficiency. Nevertheless, it remains unclear whether the super-resolved atmospheric data still satisfies fundamental physics governing the Earth system, raising concerns about their trustworthiness in climate-related applications. In this work, we address this challenge by constraining SR models to respect hydrostatic primitive equations that represent multivariate atmospheric physics. First, we propose a Physics-Informed Super-Resolution (PISR) method involving multi-scale physics-informed objectives based on primitive equations. PISR favors the SR outputs to respect these equations and therefore naturally encodes inter-variable relationships. In addition, we propose a metric called Normalized Physical Consistency (NPC) derived from said primitive equations to measure the physical consistency of super-resolved data. Experiments on ERA5, CERRA, and COSMO demonstrate that PISR enhances the reconstruction fidelity by improving physical consistency, SR accuracy, and downstream detection of extreme events, as demonstrated by case studies in heatwaves and extreme winds.

AI 总结：无监督学习旨在揭示潜在因素，概率模型有局限。本文基于新范式提出RAMP方法，通过学习摊销消息传递框架隐式定义潜在结构，能在复杂高维数据的非线性模型中有效基于似然恢复潜在变量分布。

链接：https://arxiv.org/abs/2607.18883

机构：Gatsby Computational Neuroscience Unit（盖茨比计算神经科学单元）； Centre for Artificial Intelligence（人工智能中心）

作者：Lior Fox, Kai Biegun, James Heald, Samo Hromadka, Arielle Rosinski, Maneesh Sahani

英文摘要： A central aim of unsupervised learning is to uncover latent factors that explain dependencies among observations. Probabilistic models typically achieve this by introducing multiple latent variables linked through a graph of conditional relationships, with distributional parameters and their dependence learnt from data. Learning relies either on distributional choices that allow tractable belief propagation, or on approximations that scale poorly with model size and complexity. We build on the recently developed recognition-parametrised modelling paradigm to propose an alternative approach: RAMP, a method that implicitly defines latent structure by learning a flexible, nonlinear, amortised message-passing framework. We show that RAMP enables efficient likelihood-based recovery of latent-variable distributions within expressive nonlinear models acting on complex high-dimensional data.

AI 总结：研究在网络规模数据上CLIP与DINOv2对齐问题，引入KALE损失均衡控制器，自适应调整对齐权重，无需数据集微调，在CC12M子集实验中提升了模型图像-文本检索及线性探测性能，零样本性能显著提高。

链接：https://arxiv.org/abs/2607.18885

作者：Michał Pawłowicz

英文摘要：Kernel-based alignment of CLIP toward a vision centric teacher such as DINOv2 (KUEA) improves CLIP's visual representations while preserving text-encoder compatibility, using a fixed trade-off weight tuned on curated ImageNet-1K. We ask whether this transfers to noisy, web-scale data (CC12M) and find that it does not: the alignment term's weighted contribution falls to about 0.2% of the clean term, so under any fixed weight its gradient is effectively inert. We introduce KALE, a loss-equilibration controller that tracks both losses and adaptively rescales the alignment weight toward a target ratio, restoring the signal with no per-dataset tuning; reaching balance requires increasing the weight by roughly four orders of magnitude, and the required value is configuration-dependent, so no fixed scalar suffices. We characterize the resulting regime: a bounded high learning rate and a decaying schedule with a moderate floor are needed for stability, and the controller equilibrates rather than diverging. On a 3.3M-image CC12M subset, the aligned model preserves image-text retrieval and reproducibly improves SVHN linear probing; zero-shot improves by +2.00 over CLIP on the standard 11-dataset average, exceeding KUEA's +1.29. We report all results with explicit run-to-run variance and base our conclusions on the metrics that are stable across runs.

AI 总结：研究利用端到端深度学习框架对视频刺激的脑电信号进行视觉语义解码，在每个视觉类别训练样本少的情况下评估多种方法，分析最佳方法依赖的信息，所选系统有良好性能且模型行为可解释，与神经科学知识相符。

链接：https://arxiv.org/abs/2607.18923

机构：The University of Melbourne（墨尔本大学）； Bionics Institute（仿生研究所）； The University of Osaka（大阪大学）

作者：Stella Ho, Joel Villalobos, Joseph West, Jingyang Liu, Weijie Qi, Haruhiko Kishima, Ryohei Fukuma, Takufumi Yanagisawa, Sam E. John, David B. Grayden

英文摘要：ECoG-based visual semantic decoding enables inference of semantic interpretation of visual perception from complex, noisy brain activity. This study examines the feasibility of visual semantic decoding using an end-to-end deep learning framework using electrocorticography (ECoG). Specifically, the decoding task is to predict visual categories from video stimuli using time-series neural inputs. A previously collected ECoG dataset from participants ($n=17$) with drug-resistant epilepsy is used for analysis. With fewer than 50 training samples per visual category, this study evaluates multiple deep learning approaches, artificial neural network architectures, and frequency-band filtered inputs. The best-performing approach is analyzed to shed light on the discriminative information it relies on across spectral, temporal, and cortical dimensions. The selected decoding system uses mixup augmentation, a Transformer-based encoder, and high-gamma (80-150 Hz) inputs with a 900 ms post-stimulus window. Further analysis shows that early visual cortex (V2-V4), ventral stream visual cortex, MT+ complex with neighbouring visual areas, and lateral temporal cortex contributed substantially to decoding performance. This study demonstrates that an end-to-end deep learning framework can yield promising decoding performance from dynamic visual stimuli without handcrafted features, while the model behavior remains interpretable through spectral, temporal, and cortical dimensions, which are broadly consistent with established neuroscience knowledge.

AI 总结：研究如何采购监督微调数据，提出统计优先门控架构SFGA，将采购视为成本感知路由问题，经实验在准确率、F1值和成本上取得良好平衡，还报告辩论路径负面诊断，为测量和校准构建合成基准。

链接：https://arxiv.org/abs/2607.18960

机构：DGrid AI（DGrid人工智能）

作者：Arther Tian, Alex Ding, Simon Wu, Aaron Chan

英文摘要：Procuring supervised fine-tuning (SFT) data forces a buyer to decide, before any downstream training, whether a candidate corpus is worth acquiring. We present \sys{}, a statistics-first gating architecture that treats procurement as a cost-aware routing problem over three intrinsic quality axes -- diversity, utility, and redundancy. Cheap blind measurements are summarised into per-axis estimates with confidence intervals; a gate accepts a decision only when intervals are tight, sample sizes are adequate, and the axes agree, otherwise it escalates the case to an adjudicative debate between a buy-advocate and a reject-advocate judge, resolved by a presiding verdict. On a controlled benchmark of 12 datasets ($2{\times}3{\times}2$ grid over the three axes) with 5 seeds, the gate reaches 0.90 accuracy and 0.83 $F_1$ at \$0.017 per unit, sitting between an always-verify baseline (0.75) and an oracle upper bound (0.98) while spending less than always-escalate (\$0.020). We further report honest negative diagnostics of the debate path: a con-side win rate of 0.80 ($p\approx3{\times}10^{-6}$) and a 52\% position-flip rate under advocate swapping expose negativity and positional biases that a naive LLM-judge would hide. We frame the injected-knob evaluation explicitly as a controlled synthetic benchmark for measurement fidelity and routing calibration, and delimit external validity as future work.

AI 总结：研究重症监护室时间序列预测中临床决策支持系统退化问题，提出自适应临床智能架构，通过解耦生理与治疗表征、限制参数更新等实现漂移自适应，实验验证其有效性，为高风险临床环境中部署自适应模型提供模板。

链接：https://arxiv.org/abs/2607.19020

机构：Patuakhali Science and Technology University（帕图阿卡利科学技术大学）； University of Dhaka（达卡大学）

作者：Fatema Ferdous Tamanna, K. M. Merajul Arefin, Md. Abdul Masud

英文摘要：Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models as monolithic blocks, unable to distinguish stable patient physiology from shifting institutional practice. Methods: We propose an adaptive clinical intelligence architecture for ICU intervention prediction that structurally decouples physiological from treatment representations, confining parameter updates to the treatment stream upon a dual distributional and accuracy trigger. Automated audit logs record which treatment features drove each adaptation event and how their importance shifted. At inference, an attribution-driven Temporal RAG module grounds each prediction in patient-specific, era-matched PubMed evidence anchored to the patient's dominant physiological features. Experiments used 84,792 MIMIC-IV stays (2008-2022) under strict chronological split. Results: Drift localised entirely to the treatment stream, validating the structural prior. Selective adaptation improved vasopressor and septic shock discrimination and calibration over the static source model. A fully retrained baseline yielded marginally higher aggregate discrimination but missed 26 septic shock cases the framework correctly identified, with none in the reverse direction; retrieval consistency with the pre-adaptation source model was preserved by the framework but degraded substantially in the retrained baseline. Conclusions: Structurally constraining adaptation to drifting components while preserving stable physiological representations enables clinical AI to evolve with practice without distorting learned patient biology. This architecture offers a template for governable, interpretable deployment of adaptive models in high-stakes clinical environments.

AI 总结：研究黑盒优化中自动算法选择问题，提出基于多核聚类的无监督方法，通过多核k均值公式联合学习，在差分进化和粒子群优化任务中表现良好，能对核权重进行任务特定解释。

链接：https://arxiv.org/abs/2607.19031

机构：Sorbonne Université, CNRS, INRAE, IRD, iEES（索邦大学、法国国家科学研究中心、法国农业食品环境研究院、法国发展研究所、环境与社会科学研究所）； LIP6, Sorbonne Université, CNRS（巴黎第六大学信息处理实验室、索邦大学、法国国家科学研究中心）； Jožef Stefan Institute（约瑟夫·施特凡研究所）

作者：Yihang Lu, Tome Eftimov, Carola Doerr

英文摘要：Automated algorithm selection in black-box optimization typically relies on supervised models that map landscape features to algorithm performance labels. Such models are costly to train, benchmark-dependent, and often fail to generalize to unseen problem classes. We study an unsupervised alternative: multi-kernel clustering over heterogeneous landscape representations, in which problem instances are grouped without using performance labels in the clustering stage, and the resulting clusters are mapped post hoc to solver recommendations through a strictly separated three-stage evaluation protocol. Drawing on two decades of advances in multiple kernel learning, we adopt a multi-kernel k-means formulation that jointly learns cluster assignments and kernel weights over four heterogeneous landscape views: ELA, DeepELA, DoE2Vec, and TransOptAS. On affine BBOB-derived selector tasks for Differential Evolution (DE) and Particle Swarm Optimization (PSO) at a fixed evaluation budget, we report mean plus or minus standard deviation selector profiles over 50 independent random seeds for stochastic configurations. Multi-kernel clustering obtains the strongest mean profile on the DE portfolio and remains competitive with, and nominally ahead of, the leading baselines on the more compressed PSO portfolio, where differences among the best methods are small relative to stochastic variation. In representative median-seed runs used for visualization, the learned kernel weights retain ELA and TransOptAS while assigning zero weight to DeepELA and DoE2Vec, providing a task-specific interpretation of which representations are retained by the multi-kernel model for selector-oriented grouping.

AI 总结：研究谱高阶神经网络的表达能力界限，提出利用谱属性的新参数化方法，通过权重共享降低计算成本，在N位奇偶校验任务上评估该框架，证明其具有通用且可调的假设空间。

链接：https://arxiv.org/abs/2607.19042

作者：Gianluca Peri, Diego Febbe, Duccio Fanelli

英文摘要：Neural hypergraphs are a natural generalization of neural networks, the reference models in modern machine learning. Yet, their deployment has proven demanding: the number of weighted hyperedges required leads to an intractable parameter explosion. However, a novel parametrization that leverages spectral attributes for neural hypergraphs has been recently proposed, that enables to recycle parameters via a weight sharing scheme and consequently yields a significant reduction of the associated computational cost. Preliminary tests carried out on spectral higher-order architectures pointed to meaningful improvements in both performance and interpretability. Building on these results, we advance the benchmarking efforts by evaluating the spectral higher order framework on N-bit parity tasks, a well-established testbed known to be particularly challenging. As we will convincingly argue, Spectral Higher-Order Neural Networks (SHONNs) possess a versatile and highly tunable hypothesis space.

AI 总结：研究电动卡车能耗预测，通过考虑能量损失模型将物理原理融入数据驱动方法，用贝叶斯线性回归等模型预测能耗并估计不确定性，复杂模型能提高预测准确性和可靠性。

链接：https://arxiv.org/abs/2607.19054

作者：Hannes Nilsson, Rafael Basso, Balázs Kulcsár, Morteza Haghir Chehreghani

英文摘要：In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses during vehicle operations. Our results show that Bayesian linear regression based on this physics-aware model can improve the reliability of the expected energy consumption, as compared with standard linear regression. Further, it is shown that more complex machine learning models such as neural networks and gradient boosted regression trees, based on the same physical model, can further improve the accuracy in energy forecasting and significantly outperform standard versions of the same machine learning models. In addition to point predictions of the energy consumption, we develop a framework for estimating the corresponding uncertainty in the form of predicted standard deviation. Our results show that all of the models learn to estimate the uncertainty reasonably well.

AI 总结：研究针对软机器人等领域中快速预测粘弹性接触响应的挑战，训练标量条件、序列到序列的深度学习模型，引入FMS表示，经多种架构训练比较，最佳模型有LSTM架构，能快速预测力轨迹，为数值评估提供替代，可用于控制应用。

链接：https://arxiv.org/abs/2607.19060

机构：Chair of Cyber-Physical Systems in Mechanical Engineering, Technische Universität Berlin（柏林工业大学机械工程系网络物理系统主席）； TriboDynamics Lab, Department of Mechanics, Mathematics and Management, Polytechnic University of Bari（巴里理工大学力学、数学与管理系摩擦动力学实验室）

作者：Ali Maghami, Merten Stender, Michele Ciavarella, Antonio Papangelo

英文摘要：Fast prediction of the response of adhesive soft viscoelastic contacts represents a current challenge in soft robotics and for gripping and manipulation tasks. Determining the complete time-resolved force trajectory requires full numerical simulations, whose computational cost is strongly parameter-dependent, making them impractical for real-time application or design-optimization loops. In this work, we overcome this limitation by training a scalar-conditioned, stateful, sequence-to-sequence deep learning model to predict the full force evolution from a prescribed displacement history for both short- and long-range adhesion regimes. The data set spans four orders of magnitude in loading and unloading rates and includes varied dwell times, with the Tabor parameter ranging from $0.2$ to $3.2$. To enable learning across these heterogeneous time scales, we introduce a fixed-measurement-step (FMS) representation that converts variable-length trajectories into fixed-length sequences while preserving their physical-time information. Different architectures were trained, including long short-term memory (LSTM) networks, temporal convolutional neural (TCN) networks, and time-distributed dense layers with three different Tabor-conditioning mechanisms. The models were compared using global waveform and error metrics. We found that the best-performing model has an LSTM architecture with concatenated conditioning, which achieves a held-out mean-squared error of $5.0\times10^{-4}$, a median pull-off-force error of $\approx2.2\%$, and a median hysteresis error of $\approx1.1\%$. For the held-out protocols, the model predicts a complete force trajectory with a median inference time of $0.16$ s. The model is tested across unseen parameter combinations and against analytical limiting cases, providing a rapid surrogate for repeated numerical evaluations with potential use in control-oriented applications.

AI 总结：研究利用电子健康记录中的乳腺癌数据，先采用DBSCAN密度聚类法，又通过UMAP降维增强效果，用三个统计指标评估聚类结果，证实了UMAP与DBSCAN结合用于该数据聚类的有效性，为医学解读患者组提供了支持。

链接：https://arxiv.org/abs/2607.19089

机构：Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca（信息系统与通信系，米兰比可卡大学）； Institute of Health Policy Management and Evaluation, University of Toronto（卫生政策管理与评估研究所，多伦多大学）

作者：Davide Chicco, Nicoletta Benvenuto

英文摘要：Breast cancer is one of the most widespread types of cancer, affecting approximately 8 million women worldwide. Electronic health records of patients diagnosed with this disease can serve as valuable datasets for computational analyses, enabling the discovery of new insights about the pathology. Unsupervised clustering, in particular, can identify groups of patients with medically significant features, revealing data trends that might otherwise go unnoticed by medical doctors. In this study, we first applied the DBSCAN density-based clustering method to three independent datasets derived from electronic medical records of patients with mammary carcinoma. Subsequently, to enhance our results, we preceded the DBSCAN application with a dimensionality reduction phase using UMAP. We evaluated our clustering outcomes using three statistical indices (DBCV, DCSI, and DISCO). Our results confirm the effectiveness of combining UMAP with DBSCAN for clustering data derived from electronic health records, paving the way for the medical interpretation of the patient groups identified by our approach.

AI 总结：研究利用混合机器学习模型，结合Bromley模型与矩阵补全方法，预测水电解质溶液活度。通过对478种电解质实验数据训练，得到参数矩阵，可预测9296种电解质活度，扩展Bromley模型适用性并保持高精度。

链接：https://arxiv.org/abs/2607.19114

机构：Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern（工程热力学实验室，凯撒斯劳滕工业大学）

作者：Zeno Romero, Maximilian Kohns, Fabian Jirasek

英文摘要：Activities in aqueous electrolyte solutions, usually described by ionic activity and osmotic coefficients, are important properties for modeling many processes in industry and nature. Established activity models, such as those of Pitzer or Bromley, require fitting to experimental data for each electrolyte of interest and thus cannot predict properties for unstudied systems. While some predictive approaches exist, they are typically limited in scope and rely on additional ion-specific descriptors. In this work, we introduce a new hybrid model that combines the physics-based Bromley model with a matrix completion method (MCM) from machine learning. The MCM is employed to predict the electrolyte-specific parameters of the Bromley model, exploiting the fact that these parameters can be arranged in a matrix with cations and anions as rows and columns, respectively. Due to the lack of experimental data for many electrolytes, the initial parameter matrix is sparsely populated, making the prediction of the Bromley parameters for unstudied electrolytes a matrix completion problem. The hybrid model, Bromley-MCM, was trained end-to-end on experimental data for mean ionic activity coefficients and osmotic coefficients of aqueous solutions of 478 electrolytes at 298 K from the Dortmund Data Bank. As output, we obtain a completed matrix of Bromley parameters for 83 cations and 112 anions, enabling consistent prediction of concentration-dependent activities in aqueous solutions of 9,296 electrolytes at 298~K. This substantially extends the applicability of the Bromley model while maintaining high predictive accuracy, as demonstrated through evaluations on electrolytes excluded from model training.

AI 总结：针对神经随机微分方程现有方法的局限性，提出神经柯尔莫哥洛夫方程，基于柯尔莫哥洛夫前向方程重构，通过其算子结构学习随机强迫，利用拉格朗日伽辽金投影和算子分裂实现并行训练，在多基准测试中表现良好。

链接：https://arxiv.org/abs/2607.19173

机构：EPFL（洛桑联邦理工学院）； Imperial College（帝国理工学院）

作者：Arthur Bizzi, Olga Fink

英文摘要：Neural stochastic differential equations (SDEs) have emerged as powerful tools for learning noisy or stochastic dynamics directly from data; however, existing approaches largely assume uncoupled and continuous noise, limiting their applicability to realistic stochastic drivers, and often scale poorly in time, requiring expensive autoregressive training. To address these limitations, we propose Neural Kolmogorov Equations (NKEs), a deterministic, infinite-dimensional reformulation of Neural SDEs based on the Kolmogorov Forward equation, transforming the learning problem from modelling individual stochastic trajectories to modelling the evolution of probability densities. NKEs learn general Lévy-type stochastic forcing directly through the operator structure of the KFE, and enable parallel-in-time training via a Lagrangian Galerkin projection and operator splitting. We evaluate NKEs on several stochastic benchmarks, including systems with coupled noise and jump processes, and verify that NKEs provide flexible models that accurately recover deterministic and stochastic dynamics with competitive predictive accuracy and improved training efficiency. Code and pretrained models will be released.

AI 总结：研究针对扩散草稿器在推测解码中存在的问题，提出AdaFlash框架，包含基于策略蒸馏算法和自适应长度头，有效降低方差，实验证明该框架能提高推理速度，尤其在高并发场景中吞吐量显著提升。

链接：https://arxiv.org/abs/2607.19223

机构：State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）； School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）； Huawei Foundation Model Dept（华为基础模型部）

作者：Yu-Yang Qian, Hao-Cong Wu, Chen Chen, Jiacheng Sun, Zhenhua Dong, Peng Zhao, Zhi-Hua Zhou

英文摘要：Speculative decoding, in which a lightweight draft model first generates a draft sequence that is then verified in parallel by the target model, has become a prevalent paradigm for accelerating large language model inference. Recent work such as DFlash further boosts drafting efficiency by leveraging diffusion drafters, whose parallel denoising mechanism enables draft generation in a single forward pass. In this work, we uncover a central pitfall of diffusion drafters: bidirectional attention is a double-edged sword. On one hand, it endows the model with parallel generation and global contextual modeling capabilities; on the other hand, this inherent global dependency introduces high variance at both the domain-level and the token-level: acceptance rates fluctuate substantially across different domains, and draft token quality also varies heterogeneously at different token positions. To tackle this issue, we propose AdaFlash framework, comprising two components: (i) an on-policy distillation (OPD) algorithm with reverse-KL divergence tailored for diffusion drafters, bringing stable convergence and effectively reducing domain-level variance; and (ii) an adaptive length head that dynamically adjusts the candidate sequence length on the fly, substantially lowering the verification cost of the target model and handling token-level variance. Experiments demonstrate that AdaFlash consistently improves speedup rate during deployment, with especially significant gains in high-concurrency scenarios, achieving up to approximately 66% higher throughput than previous state-of-the-art methods.

AI 总结：研究时间序列分类，提出MASHT方法，结合MultiRocket和Hydra特征与预训练表格基础模型，绕过特定任务模型训练，在单变量任务中匹配最先进基线，多变量数据集上竞争力强。

链接：https://arxiv.org/abs/2607.19234

作者：Joscha Cüppers, Jilles Vreeken

英文摘要：Time series classification is central to domains like medical signal analysis, industrial monitoring, and sensor-based activity recognition, where class information manifests as localized shapes, specific frequencies, temporal shifts, or complex cross-channel interactions. Random convolutional transforms efficiently map these sequences to fixed-dimensional tabular features but are traditionally paired with simple linear classifiers. We investigate whether a pretrained tabular foundation model can more effectively harness these rich representations. We propose MASHT, a pipeline that marries MultiRocket and Hydra features with the power of in-context tabular foundation models. By leveraging a pretrained tabular foundation model, our approach completely bypasses task-specific model training, requiring only feature extraction and direct inference. Extensive experiments demonstrate that MASHT matches state-of-the-art time series classification baselines on univariate tasks, achieving a lower average rank than HIVE-COTE 2.0. On multivariate datasets, MASHT remains highly competitive with the strongest reference methods.

AI 总结：研究超临界燃烧模拟中真实流体热力学性质评估成本高的问题，提出TAIR策略，通过用目标匹配的热力学坐标替换原始焓坐标，降低了预测温度、密度和压缩系数的RMSE，证明收益源于热力学匹配的输入设计。

链接：https://arxiv.org/abs/2607.19241

机构：State Key Laboratory of Turbulence and Complex Systems, College of Engineering, Peking University（湍流与复杂系统国家重点实验室，北京大学工学院）； AI for Science Institute (AISI)（科学智能研究院）

作者：Haoze Zhang, Han Li, Ke Xiao, Yangchen Xu, Runze Mao, Zhi X. Chen

英文摘要： Real-fluid thermodynamic property evaluation is a major computational cost in supercritical combustion simulations. In the enthalpy-based pressure-correction formulation, the closure evaluates temperature T, density $\rho$, and compressibility coefficient $\psi$ from the solver state (h,p,Y) through enthalpy-temperature inversion and repeated real-fluid equation-of-state evaluations. Neural-network surrogates offer fixed-cost inference, but direct mapping from (h,p,Y) to $(T,\rho,\psi)$ must capture the enthalpy-temperature relation and non-ideal equation-of-state response, resulting in a complex regression problem. This work introduces a thermodynamics-informed input reparameterization strategy, termed target-aligned input reparameterization (TAIR). TAIR replaces the raw enthalpy coordinate of each property network with a target-matched thermodynamic coordinate: the temperature network uses a temperature estimate obtained by inverting a constant-$c_p$ ideal-gas mixture enthalpy approximation, whereas the density and compressibility networks use an ideal-gas density estimate. These algebraic transformations use only solver-available variables and species constants, guiding the networks to learn real-fluid departures from ideal-gas baselines rather than reconstructing the full closure from raw enthalpy. The method is assessed using supercritical methane-oxygen counterflow flame data against a raw-input baseline and target-inconsistent cross-reparameterization controls. TAIR reduces held-out RMSE by factors of about 1.5, 2.0, and 7.5 for T, $\rho$, and $\psi$, respectively. For an unseen strain-rate flame within the augmented thermodynamic envelope, the corresponding factors are 3.6, 14.5, and 6.0. The target-inconsistent controls perform worse, indicating that the gains arise from thermodynamically matched input design rather than generic preprocessing.

AI 总结：研究如何对高维动态系统进行实时最优控制，利用SHRED-ROM模型，通过专家示例训练，以有限传感器读数合成闭环控制器，减轻维度诅咒，还引入传感器预测器，经三个高维案例评估，该策略表现良好。

链接：https://arxiv.org/abs/2607.19302

作者：Matteo Tomasetto, Francesco Braghin, J. Nathan Kutz, Andrea Manzoni

英文摘要：Controlling dynamical systems in real-time across multiple scenarios is critical to enabling adaptive control strategies, ensuring stability and efficiency. However, to tailor control actions in response to varying scenarios, traditional optimal control problems typically require several system simulations, which are often computationally demanding due to the high-dimensionality of the underlying spatio-temporal dynamics. In this work, we exploit SHallow REcurrent Decoder networks-based Reduced Order Modeling (SHRED-ROM) to synthesize a real-time closed-loop controller for high-dimensional and parametric dynamics, relying solely on limited state sensor readings. After training the model on a few optimal examples given by an expert demonstrator, SHRED-ROM mimics the expert behavior with effective distributed control actions in new scenarios, alleviating the curse of dimensionality. Moreover, a sensor forecaster is synthesized and used to close the loop at the latent level, thus efficiently mitigating possible sensor failures or delays. The performance of the proposed optimal control strategy is finally assessed on three challenging high-dimensional cases dealing with either parametric density control or fluid flow control.

AI 总结：研究为黎曼深度学习构建统一框架，从可复用神经模块等三个角度出发，推广批量归一化等，开发相关神经网络及度量，经理论分析、数值实验和多领域应用验证方法的有效性。

链接：https://arxiv.org/abs/2607.19305

作者：Chen Ziheng

英文摘要：Deep neural networks on manifold-valued representations have attracted growing interest, but many basic components remain tied to specific manifolds, rely on Euclidean approximations, or require costly and numerically fragile geometric operations. This thesis develops a unified framework for Riemannian deep learning from three complementary perspectives: reusable neural modules, manifold-specific network architectures, and the design of underlying geometries. It generalizes batch normalization from Euclidean spaces and individual manifolds to broad classes of Lie groups and gyrogroups, and extends multinomial logistic regression from Euclidean space to SPD manifolds and then to general Riemannian manifolds. It further develops neural networks for several important geometric representations, including an unconstrained model of hyperbolic space, Busemann-based hyperbolic learning, and full-rank correlation matrices. Finally, it introduces adaptive and computationally efficient Riemannian metrics on SPD manifolds, including learnable Log-Euclidean geometries and fast, stable Cholesky-based geometries. The proposed methods are supported by theoretical analysis and validated through numerical experiments and applications in vision, signal processing, graph learning, and genomics.

AI 总结：介绍CircuitKIT工具包，用于机理可解释性的电路分析。它通过可序列化表示连接工作流程，提供发现算法、声明式接口、电路诊断及应用模块，为电路分析提供通用基础设施，解决了现有方法碎片化问题。

链接：https://arxiv.org/abs/2607.19317

作者：Pratinav Seth, Hem Gosalia, Aditya Kasliwal, Vinay Kumar Sankarapu

英文摘要：Circuit analysis can support not only model explanation but also downstream interventions such as pruning, editing, steering, and selective fine-tuning. However, conducting such analyses currently requires stitching together separate implementations for discovery, evaluation, and intervention, as well as hand-authoring the contrastive prompts required by many discovery methods. This fragmentation makes methods difficult to compare and limits their application beyond canonical tasks. We introduce CircuitKIT, a source-available library that connects the circuit-analysis workflow through a typed, serializable representation. CircuitKIT provides a suite of discovery algorithms, declarative interfaces for mapping structured data into discovery tasks, complementary circuit diagnostics, and downstream application modules. Together, these components provide common infrastructure for conducting and comparing circuit analyses. The library, examples, notebooks, and documentation are released at this https URL.

AI 总结：该研究针对线性逆问题，提出\pddim算法，通过对标准DDIM更新进行轻量级逐坐标修改并纳入测量模型，沿测量算子奇异方向分别采样。此算法收敛到贝叶斯后验，在图像恢复任务中性能优于现有方法，实现高效且具后验一致性。

链接：https://arxiv.org/abs/2607.19333

作者：Yuchen Jiao, Na Li, Changxiao Cai, Yuxin Chen, Gen Li

英文摘要： Diffusion-based methods have achieved remarkable empirical success in solving inverse problems. However, many existing posterior samplers either lack rigorous theoretical guarantees or incur substantial computational overhead. We propose a simple and efficient algorithm, called \pddim, for solving linear inverse problems with diffusion priors via a DDIM-type sampler. Our method requires only lightweight, coordinate-wise modifications to the standard DDIM update, while explicitly incorporating the measurement model. The key idea is to perform posterior sampling separately along each singular direction of the measurement operator: for each direction, the sampler follows the learned diffusion prior when the observation signal-to-noise ratio (SNR) is below the corresponding diffusion SNR, and switches to a calibrated measurement-based predictor otherwise. We prove that the proposed sampler converges to the Bayesian posterior conditioned on the measurements. Empirical results show that the proposed sampler performs favorably against existing diffusion-based posterior samplers across a range of image restoration tasks, achieving the best performance on the majority of evaluation metrics considered. Overall, our results convert posterior sampling for noisy linear inverse problems to simple coordinate-wise DDIM updates, yielding an efficient, easy-to-implement algorithm with provable posterior consistency.