机器学习学术速递[11.5]

AI 总结：针对推测解码中草稿器不确定的 token 被目标模型拒绝的问题，提出并行重考虑方法，通过并行额外前向传播优化草稿，在接受长度和加速比上均有显著提升。

链接：https://arxiv.org/abs/2611.05724

机构：Rice University（莱斯大学）； Google（谷歌公司）； Google Research（谷歌研究院）； Google DeepMind（谷歌DeepMind）

作者：Hoang Anh Duy Le, Huibing Dong, Alireza Khadem, Ziteng Sun, Xiang Song, Yizheng Huang, Soroush Radpour, Anshumali Shrivastava, Ye He

英文摘要：Speculative decoding accelerates large language model inference with a small drafter, whose per-guess acceptance rate determines the achievable speedup. Feature-level drafters share a design choice: every token, easy or hard, is drafted under a single forward pass. Many of the tokens the target rejects are ones the drafter is itself unsure about yet the target answers decisively. To address this, we introduce Parallel Reconsideration: guided by its own uncertainty, the drafter reconsiders its hesitant guesses with extra passes that run in parallel with the one-pass draft, adding negligible latency. A co-designed training recipe and serving-time drafting algorithm append these reconsidered guesses to the original draft tree as refined candidates. Across models and benchmarks, on average, Parallel Reconsideration improves over the baselines by 5.3% to 10.2% on acceptance length and by 3.5% to 9.2% on speedup.

AI 总结：该研究针对基础模型扰动预测的可靠性问题，发现ST解码器的低秩注意力瓶颈限制预测可靠性，提出predicted-NN-distance置信度评分，其与PDC相关性达0.81，可跨编码器和细胞系迁移。

链接：https://arxiv.org/abs/2611.05849

机构：University of Michigan（密歇根大学）； Computer Science and Engineering Division（计算机科学与工程系）； Gilbert S. Omenn Department of Computational Medicine and Bioinformatics（吉尔伯特·S·奥门计算医学与生物信息学系）

作者：Zheyu Zhang, Yuanhao Huang, Fan Feng, Jie Liu

英文摘要：Foundation-model predictors of perturbation response are evaluated primarily by aggregate accuracy. Using their predictions to prioritize experiments requires assessing each prediction's reliability. We audit the state-transition (ST) decoder used in STATE across three encoders (SE-600M, scGPT, and Tahoe-x1) and four cell lines under CRISPRi. The decoder's first attention layer has a low-rank output. Projecting this output onto 4-32 fixed directions estimated from training activations preserves mean per-perturbation Pearson delta correlation (PDC). This low-rank attention bottleneck bounds the decoder's reliability by distance to training responses: PDC declines as the cosine distance from a perturbation's observed response to its nearest training response increases. We propose predicted-NN-distance, the cosine distance from a predicted response to its nearest training response, as a per-prediction confidence score. The score has a mean absolute Spearman correlation of 0.81 with PDC, close to the 0.88 obtained with the observed response. Thresholds calibrated on this score transfer across encoders and cell lines, and the score remains predictive of PDC for drug perturbations.

AI 总结：本研究针对超连接（HC）架构的训练开销问题，提出SimpleHC简化设计，移除残差混合并采用共享内存访问，在15B至30B MoE模型上性能与mHC相当，训练时间仅比基线多2.4%，梯度更平稳且稳定性更强。

链接：https://arxiv.org/abs/2611.05984

机构：School of AI, Shanghai Jiao Tong University（上海交通大学人工智能学院）； Dots Studio, Xiaohongshu Inc.（小红书公司Dots工作室）； School of CS, Peking University（北京大学计算机学院）； Nanyang Technological University（南洋理工大学）

作者：Xiangdong Zhang, Xiaohan Qin, Sunan Zou, Tuo Dai, Bizhao Shi, Zhuo Xia, Yebin Yang, Yizhou Chen, Yuliang Liu, Yu Cheng, Junchi Yan

英文摘要： Hyper-Connections (HC) architectures are increasingly adopted in frontier language models, yet their components remain insufficiently understood and introduce non-negligible training overhead. In this work, we systematically study the HC architecture to understand its components and identify simpler, more efficient designs. We find that learned read/write gating without mixing and unconstrained mixing with fixed gates form two effective interaction groups: either alone achieves performance comparable to mHC. We also identify repeated access to the expanded residual state as a major source of training overhead, even without mixing. These findings lead to SimpleHC, which simplifies the architecture by removing residual mixing and simplifies execution through shared memory access, using the resulting additive structure and shared gate inputs. Layer-specific gates share a group-entry state, enabling batched reads and deferred writes; scalar corrections exactly preserve preceding sublayer output contributions. Our analyses support both the retained residual structure and the design of shared memory access. SimpleHC incurs only 2.4% extra end-to-end training time over a vanilla residual baseline at the largest tested scale. It maintains performance comparable to mHC when scaling from 15B to 30B MoE models, exhibits smoother gradient dynamics during standard training, and remains stable under stress. We hope SimpleHC will encourage a broader pursuit of simplicity in foundation model design, making performance gains more accessible and practical at scale.

AI 总结：该研究针对混合语言模型的记忆冗余问题，提出SEED方法，通过浅层预填充与状态压缩降低计算开销，在多参数规模模型上几乎无损失，将Qwen3.8-Flash-Next的GPU吞吐量提升1.2-1.4倍。

链接：https://arxiv.org/abs/2611.06084

机构：NVIDIA（英伟达）

作者：Mingyuan Ma, Yangmin Li, Harris Nover, Zheyu Fu, Po-Han Huang, Alex Settle, Ashwin Nanjappa

英文摘要：Hybrid language models interleave a few softmax-attention layers with many linear-attention or state-space layers, so every layer keeps a memory of earlier tokens: a key-value cache or a fixed-size recurrent state. Serving them spends more compute and high-bandwidth memory on these memories than the output requires. The prefill runs every layer for every prompt token, and every decode step loads and stores whole recurrent states, which on Qwen3.8-Flash-Next take as many bytes per request as the attention cache of thousands of tokens. We find two properties of trained hybrids that locate this excess. First, writes are easier than reads: writing a deep layer's memory of a token needs little of the deep computation that predicting the next token needs. Trained copies of the deep layers' write paths, which we call emitters, therefore write a prompt's deep memories from the residual stream of a shallower anchor layer, and all prompt tokens but the last run as few as 63% of the layers. Second, recurrent states have a state sink: most of their energy lies in a rank-one term along a fixed direction of each head, and the rest is a low-rank working memory. SEED builds on both: it makes the prefill shallow in depth and the decode state shallow in representation. Each recurrent state is stored as its sink plus low-rank factors, which the Gated DeltaNet, Kimi Delta Attention and Mamba-2 updates extend exactly; we prove that the open-loop error of the periodic compressions back to low rank is bounded independently of their number. SEED keeps the original weights frozen, compresses no attention cache, and runs all layers for every generated token. On six checkpoints of five production hybrids of 30B to over 300B parameters, it is nearly lossless on eleven official exams. Served with SGLang on NVIDIA GB300 GPUs on an agentic-coding trace, it raises the per-GPU throughput of Qwen3.8-Flash-Next 1.2-1.4x.

AI 总结：该研究针对TPU架构设计SLA，通过重构门控增量注意力解决GPU适配层在TPU上的效率问题，在TPU v7x上实现训练速度提升与更优困惑度表现。

链接：https://arxiv.org/abs/2611.06828

机构：Google（谷歌）

作者：Gagik Amirkhanyan, Nuojin Cheng

英文摘要：Gated-delta linear attention layers such as Gated DeltaNet (GDN), Kimi Delta Attention (KDA), and GDN-2 are tuned for GPUs, where small tensor-core tiles and fast vector registers make per-channel decay rescaling and sequential in-block solves cheap. On Google TPUs, which concentrate compute in wide $256 \times 256$ systolic matrix units backed by a slower vector unit, porting these layers directly leaves half of each matrix tile empty and bottlenecks on vector operations. We present \textbf{Systolic Linear Attention (SLA)}, which reshapes gated delta attention around the matrix unit by widening keys to $256$ with grouped value heads, grouping decay gates into four $64$-channel tiles, and computing the chunk solve entirely with block matrix products in a single fused kernel. On TPU v7x, a full \texttt{SLA-R4} layer runs $1.6\times$--$2.5\times$ faster than the open-source Tokamax KDA kernel ($1.4\times$--$2.4\times$ with exact chunk inversion), driven primarily by head sharing and kernel fusion. Across a $2{,}048$-chip TPU v7x pre-training study of sparse Mixture-of-Experts (MoE) models from $6.5\text{B}$ to $63.1\text{B}$ parameters, SLA trains $11\%$--$24\%$ faster than KDA at similar perplexity, and reaches slightly lower perplexity than KDA in smaller matched-seed runs.

AI 总结：本文提出将表示学习与算子学习解耦，预训练函数自编码器得到通用潜在空间，在28类物理任务上精度超端到端架构，吞吐量与内存效率显著提升。

链接：https://arxiv.org/abs/2611.05735

机构：ETH Zurich（苏黎世联邦理工学院）； ETH AI Center（ETH人工智能中心）

作者：Sepehr Mousavi, Levi Lingsch, Siddhartha Mishra, Laura De Lorenzis

英文摘要：Neural operators fit their representation of functions to the task they are trained on, so every new task rebuilds a reduced space from labeled data. Following the self-supervised pre-training that made representations shareable in natural language processing and computer vision, we decouple representation learning from operator learning. A functional autoencoder is pre-trained on analytic functions synthesized on the fly, with no solver and no labels, to represent functions on arbitrary point clouds, and the latent space it induces is frozen and reused across tasks. The representation is exactly equivariant to translation, dilation, and amplitude, and reconstructs solutions of partial differential equations (PDEs) and natural images on geometries it never saw. Being task-agnostic, the latent space can be interpreted in its own right. The whole space is used and decodes to valid fields, it preserves the ordering of distances between physical fields, its geodesics interpolate physical evolutions, and edit directions transfer across fields. With frozen representation maps, a dataset is encoded once and a neural operator is trained entirely on cached embeddings, which saves memory and training time. On 28 tasks in fluid dynamics, solid mechanics, acoustics, and the Helmholtz and Poisson equations, this paradigm can exceed the accuracy of state-of-the-art end-to-end architectures, with up to an order of magnitude higher throughput and memory savings that fit models nearly two orders of magnitude larger on consumer-grade GPUs. At inference, latent rollouts run up to an order of magnitude faster than regular rollouts, and chunked inference processes functions of over half a billion points on a single consumer-grade GPU.

AI 总结：提出RIDGE单智能体架构，通过状态条件融合器与多头评论家解决深度强化学习中奖励切换的稳定性问题，在Crafter环境中实现多角色行为覆盖。

链接：https://arxiv.org/abs/2611.05601

机构：Ontario Tech University（安大略理工大学）

作者：AL Muqshith Mohammed Shifan, Kevin Christopher Chua, Ali Neshati, Loutfouz Zaman, Cristiano Politowski

英文摘要：A deep RL agent trained on a fixed persona reward plays one way: an Explorer keeps exploring while starving, a Survivor hoards on an empty map. Since each persona fixes its own reward, automated playtesting trains N agents for N styles. Changing reward composition mid-episode runs into the switching-stability dilemma: PPO degrades under non-stationary rewards. We present RIDGE, a single-agent architecture that sidesteps the dilemma with two design choices: a state-conditioned blender that computes softmax-normalized sigmoid weights from slow-moving state variables, and a multi-head critic that trains one value head per persona on its own stationary signal. On Crafter with four personas, one \ridge agent acquires both branches of the wood-tier crafting/combat split that no fixed-persona baseline solves jointly, and a sharpness ablation confirms state-conditioning drives the gain. We position these results as demonstrations of the blending mechanism's dynamics across n=3 seeds per condition; larger multi-seed sweeps remain future work.

AI 总结：本文研究强化学习后整合中在线策略蒸馏（OPD）的泛化局限，发现其分布外性能不足，提出几何修补与基础保留重放方法，为相关算法优化提供方向。

链接：https://arxiv.org/abs/2611.05609

作者：Yichen Wang, Yufa Zhou, Siqi Zhu, Haozheng Luo, Jingzhi Bao, Zhichao Liu, Zijun Wei

英文摘要：Multi-teacher on-policy distillation (MOPD) extends on-policy distillation (OPD) to consolidate capabilities from multiple teachers. Although prior work reports near-perfect in-distribution (IND) distillation performance, we find that this can mask incomplete capability consolidation beyond the supervised distribution. A preliminary study on synthetic reasoning shows that OPD does not fully transfer teachers' generalization capabilities. We then extend this investigation to real-world tasks, evaluating extrapolative and compositional generalization across mathematical reasoning, coding, agent, and long-context capabilities using Qwen3 models. Across extrapolative settings, OPD and MOPD reach 96.04-102.73% of their teachers' IND scores. Yet their out-of-distribution (OOD) scores remain 2.45-16.25% below the base model's. Across three compositional settings, MOPD improves IND performance over the base model, but its performance on composed tasks as OOD remains 18.19-24.34% below that of the best teachers. To understand these gaps, we examine teacher capability and data coverage as two potential bottlenecks. Jointly alleviating these bottlenecks eliminates OOD forgetting in our experiments and suggests the potential for generalization beyond the teacher. (M)OPD also reduces OOD forgetting on average relative to its reinforcement learning (RL) baselines. Analysis of the associated update geometry reveals smaller and more spectrally concentrated updates on average than those of the RL baselines, along with greater shared update structure under MOPD. Motivated by these findings, we propose geometry-based patching and base-preserving replay to improve OOD performance while largely preserving IND performance. Our findings identify a blind spot in capability consolidation via (M)OPD, highlighting the need for algorithms that explicitly support OOD retention and generalization.

AI 总结：本研究针对辅助脑机机器人控制的解码器评估问题，提出基于信息论的遗憾预测器U_id，可预测超出准确率解释范围的规划遗憾，解决了传统准确率指标的缺陷。

链接：https://arxiv.org/abs/2611.05658

机构：University of Oxford（牛津大学）

作者：Tasha Kim, Benjamin Ballyk, Anson Lei, Ingmar Posner, Oiwi Parker Jones

英文摘要：A central challenge in assistive brain-to-robot control is decoding a user's latent intent from complex and noisy brain signals. Prior approaches evaluate decoders as single-trial classifiers with accuracy-based metrics, but this neglects the inherently sequential nature of assistive control. Since a planner must integrate intermittent, noisy decoder outputs over time, single-step accuracy can only be a proxy when predicting downstream task performance. We formalize brain-to-robot control as a partially observable Markov decision process (POMDP), and prove that there is a sharp phase transition: regret is uniformly bounded across the horizon as long as pairs of goals are all decoder-distinguishable, but grows linearly otherwise. We show that a single channel statistic, the worst-pair Chernoff information $\Gamma_{\min}$ sets this boundary. Then, we derive a closed-form regret predictor $U_{\mathrm{id}}(C)$, that can be computed from any confusion matrix in $O(K^2)$ time. Across two domains spanning orthogonal information channels, we show that $U_{\mathrm{id}}$ predicts cumulative planning regret beyond what accuracy can explain. We then highlight a pathological accuracy trap, in which two decoders have identical validation accuracy 35.1% on real EEG data (BNCI2014001 subject 5) but result in a difference of $9.6\times$ in held-out regret, which $U_{\mathrm{id}}$ predicts. Same-signal control confirms $U_{\mathrm{id}}$'s advantage with decoders that span independent feature channels. Ultimately, our work provides a drop-in diagnostic to evaluate and select sensors and decoders in sequential assistive control.

AI 总结：本研究重新审视SAC的三种评论家设计选择，通过14个任务的超参数搜索评估六种变体，发现从重放轨迹训练λ-回报评论家可提升连续控制性能，相关思想可扩展至其他强化学习算法。

链接：https://arxiv.org/abs/2611.05666

机构：Leiden Institute of Advanced Computer Science (LIACS)（莱顿高级计算机科学研究所）； Leiden University（莱顿大学）

作者：Jan Felix Kleuker, Koen Ponse, Aske Plaat, Thomas M. Moerland

英文摘要： Soft Actor-Critic (SAC) is a widely used deep reinforcement learning algorithm that combines entropy regularization with an actor-critic architecture. Standard implementations rely on one-step temporal-difference learning of Q-functions, yet alternative critic design choices remain underexplored. In this work, we revisit three such choices: learning soft advantages instead of soft Q-values, the use of multi-step return targets, and the training of multi-step critics from replay buffer data. Keeping the policy objective fixed, we compare the six critic variants these choices generate: (i) advantage and (ii) dueling-advantage critics learned from one-step transitions, and $\lambda$-return and soft Generalized Advantage Estimation (GAE) critics learned either (iii, iv) on-policy or (v, vi) from replayed trajectory segments. Each variant is evaluated across an entire random hyperparameter search on fourteen tasks, treating the hyperparameters as part of the algorithm. Learning soft advantages instead of soft Q-values does not improve performance, and the soft GAE critic adds nothing over the simpler $\lambda$-return critic. What does improve performance, most clearly on continuous control, is training a $\lambda$-return critic on replayed trajectory segments, even without importance weighting or trace cutting. This extends earlier evidence that uncorrected multi-step targets work well under replay to the full soft $\lambda$-return, entropy terms included. By design, the underlying ideas studied here extend beyond SAC, and our empirical findings aim to encourage their application to a broader class of reinforcement learning algorithms.

AI 总结：该研究针对固定预算下大语言模型调用的任务分配问题，提出ADP-HT策略，通过K位尾部代价学习优化路由，在RouterBench、LiveCodeBench等基准上表现优于基线和商业系统。

链接：https://arxiv.org/abs/2611.05835

机构：National University of Singapore（新加坡国立大学）； University of Pennsylvania（宾夕法尼亚大学）； Tsinghua University（清华大学）

作者：Hanzhang Qin, Sophie H. Yu, Yuewei Zhang, Zhi-Hai Zhang

英文摘要：Firms using large language models under fixed budgets must decide which model to call and whether to retry a failed task. Because a call's charge is revealed only after completion, one long response can exhaust the remaining budget and leave later tasks unserved. Our objective is to maximize the number of tasks solved before service stops. We develop a contextual bandit policy, ADP-HT, which learns each model's task-specific success probability and constructs conservative cost estimates from coarsened response-length labels on a quantile grid with $2^K$ intervals. The policy weighs predicted success against these costs, adjusting the cost penalty to realized spending and the budget available per remaining task. Under calibration, local regularity, and grid-coverage conditions, we bound the excess allowance in the policy's conservative cost estimates. At a fixed sample size, increasing $K$ reduces the discretization term but increases the estimation allowance. At the tightest tested budgets, ADP-HT solves more than twice as many MATH500 and GPQA tasks as the best implemented baseline. It achieves the highest mean cumulative score at ten of fourteen RouterBench budgets. On LiveCodeBench, it outperforms a deployed commercial router in both default and cost-saving modes at the three lowest test budgets.

AI 总结：该研究针对部分偏好漂移问题提出PEARL方法，通过反转出错特征方向的Gram更新实现选择性遗忘，在PRISM基准上较基线方法表现更优，且在偏好稳定时性能与贪心策略相当。

链接：https://arxiv.org/abs/2611.05842

机构：AWS（亚马逊网络服务）

作者：Rashmi Gangadharaiah, Balakrishnan Narayanaswamy

英文摘要：User preferences shift as needs, context, and tastes evolve. This matters for personalized recommendation, LLM response generation, and RLHF-trained reward models, which can become miscalibrated as preferences drift. Existing non-stationary bandit methods forget uniformly across all dimensions, but real preference shifts are typically partial: some dimensions go stale while others remain valid. Neural contextual bandits accumulate confidence monotonically via the Gram matrix, with no mechanism to direct uncertainty where predictions have gone wrong. We propose PEARL (Prediction Error-Adaptive Regularized Learning), which reverses the Gram update along feature directions where predictions are surprisingly wrong, while leaving confidence intact elsewhere. We prove that (i) any non-adaptive policy suffers linear regret after a preference shift, (ii) no fixed exploration rate achieves low regret in both stationary and non-stationary regimes, and (iii) PEARL's cumulative regret scales with the number of shifted users N_s, not the total population N. On PRISM, a personalized LLM response selection benchmark (1,500 users, 21 LLMs), PEARL beats greedy by +4.0 points and the best neural bandit baseline by +2.5 points (p<0.001), while performing on par with greedy when preferences are stable. It beats sliding-window and discounted approaches by 3 to 10 points, showing that selective forgetting outperforms uniform forgetting. The selection policy is a ~100K-parameter MLP that runs in milliseconds on CPU, leaves LLM weights frozen, and works with any API-based model.

AI 总结：本研究针对LLM推理RL的过早收敛问题，提出奖励预算方法，在Qwen3-8B等模型上使AMC23等基准性能提升最高达12.5，无需显式保留熵即可改善性能。

链接：https://arxiv.org/abs/2611.05942

机构：University of Cambridge（剑桥大学）； Qwen Business Unit, Alibaba（阿里巴巴通义千问业务部）

作者：Mengni Jia, Mengyu Zhou, Xiaoxi Jiang, Guanjun Jiang

英文摘要： RL for LLM reasoning often reaches an early performance plateau as the policy distribution becomes increasingly sharp. Recent work has studied policy entropy as both a diagnostic and a control signal for this behavior. In our experiments, however, increasing entropy alone does not reliably change the eventual plateau. We analyze RL through its implicit target policy and derive a sharpness factor $\kappa$, together with an effective counterpart $\kappa_{\mathrm{eff}}$, that describes how strongly reward or advantage gaps are translated into probability-ratio gaps. Motivated by this view, we introduce \emph{reward budgeting}, a plug-in advantage construction that assigns a fixed total reward budget to each prompt. For binary rewards and independent on-policy rollouts, its unclipped score-function estimator follows the gradient of an exact finite-$G$ concave objective with diminishing returns, while its advantage gap decreases as more rollouts become correct. For general rewards, a shifted log-mean interpretation holds asymptotically under the stated regularity conditions. These results motivate, rather than establish exact objective equivalence for, the clipped implementations. On Qwen3-8B and Llama3.2-3B-Instruct, reward budgeting consistently improves a range of RL baselines, with gains up to +12.50 on AMC23 and +10.00 on AIME24 and AIME25. Overall, our results support an update-dynamics view of premature convergence and show that strong gains can be obtained without explicitly preserving entropy.

AI 总结：本文提出重要性引导的混合推理框架 VITAL，通过动态选择显式 token 生成与潜在状态反馈，在数学推理等任务上优于现有方法，在多款模型上实现准确率提升且推理轨迹更短。

链接：https://arxiv.org/abs/2611.06108

机构：Beijing Institute of Technology（北京理工大学）； Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）； Kuaishou Technology（快手科技）

作者：Zhehao Zhou, Tongrui Su, Shengyu Zhu, Da Li, Zeyu Liu, Jun Zhu, Xueqi Cheng

英文摘要：Latent Chain-of-Thought (CoT) reasoning improves efficiency by shifting intermediate computation into continuous hidden states. However, fully latent methods often struggle on precision-critical tasks such as mathematical reasoning, while existing hybrid approaches retain substantial token-generation overhead. We introduce VITAL, an importance-guided hybrid reasoning framework that selectively verbalizes informative tokens as reasoning anchors while keeping most computation latent. A lightweight gating head dynamically selects between explicit token generation and direct hidden-state feedback. VITAL constructs hybrid trajectories from explicit CoT using token- importance estimation and trains the model through curriculum-based supervised training, followed by reinforcement learning with a reward that balances correctness and conciseness. Experiments show that VITAL consistently out- performs existing fully latent reasoning methods, achieving relative average accuracy gains of 9.6% on GPT-2 and 5.1% on Llama-3.2-1B at comparable reasoning lengths. Compared with hybrid baselines, VITAL achieves higher reasoning accuracy with substantially shorter reasoning trajectories on Qwen3-1.7B.

AI 总结：该研究提出RcRL方法，利用生成-验证差距，结合成对偏好与规则课程，在创意写作和HealthBench任务上均优于基线模型，实现了持续改进。

链接：https://arxiv.org/abs/2611.06244

机构：University of Oxford（牛津大学）； Microsoft Research（微软研究院）

作者：Sumeet Ramesh Motwani, Tejas Krishnan, Alistair Letcher, Charles London, Suhaas Bhat, Huitian Jiao, Philip Torr, Riashat Islam, Christopher Summerfield, Jakob Foerster, Christian Schroeder de Witt, Qilong Gu, Shital Shah

英文摘要：Reinforcement learning with verifiable rewards (RLVR) has led to significant gains in math and code generation. Extending these gains to open-ended domains remains challenging: ground-truth verification is unavailable, human annotation is expensive, and learned reward models are prone to reward hacking. The generation-verification gap, where judging good outputs is easier than producing them, offers a potential path to improvement. However, rubric-based judges are often imprecise and stationary, so policies can overfit or reward hack them. We introduce Rubric Curriculum RL (RcRL), which exploits this gap without new data, human annotations, or stronger teacher models. We combine pairwise preferences over rollouts with a curriculum over rubric criteria, yielding a more discriminative signal than absolute scoring and a non-stationary objective. Whereas baselines plateau within a few hundred steps, RcRL preserves output entropy and keeps improving past 1000 steps. On creative writing, RcRL achieves a 70.5% human win rate against the base model, with gains across benchmarks and judges. On HealthBench, RcRL improves over the base model by 14.6% (full set) and 25.4% (consensus subset), outperforming all training baselines, including instance-specific rubrics.

AI 总结：本研究针对独立开发的LLM智能体的零样本协作问题，提出协作友好型定义（CFDs），实现了无需人工工程的自动发现，解决了其协作能力不足的问题。

链接：https://arxiv.org/abs/2611.06409

机构：University of Oxford（牛津大学）

作者：Adrian Hayler, Shashank Reddy Chirra, Andrei Lupu, Johannes Forkel, Bidipta Sarkar, Siheng Feng, Jakob Foerster

英文摘要：We study zero-shot coordination (ZSC), where independently developed agents must coordinate at test time. While ZSC has been well studied in the RL literature, far less is known about the performance of LLM agents despite their increasing deployment in such settings. Existing work on LLM coordination often relies on specialised scaffolding that independently developed agents are unlikely to share in practice. Moreover, evaluations on complex environments (e.g., Hanabi) make it difficult to pinpoint the sources of coordination failure. In contrast, we focus on simple, general-purpose scaffolds in minimal environments designed to isolate specific coordination challenges. Our results show that even in these controlled settings, frontier LLM agents struggle to coordinate, largely due to limited understanding of the coordination problem and weak reasoning about their partner's beliefs. While LLMs introduce semantic information as an additional axis for coordination, they nonetheless fail to exploit this structure effectively. Towards this, we propose Coordination-friendly definitions (CFDs) as a principled approach for enabling robust coordination among LLM agents. Finally, we show that CFDs can be discovered automatically, removing the need for manual engineering. Code is available at this https URL.

AI 总结：该研究探讨多轮RL在LLM智能体中的性能依赖，发现其优势随交互预算收紧而减弱，核心方法是提出BudgetMix RL，可在训练中调整交互预算以提升跨场景鲁棒性。

链接：https://arxiv.org/abs/2611.06483

作者：Kejian Zhu, Zhuoran Jin, Shangqing Tu, Tianyi Men, Yupu Hao, Jiachun Li, Kang Liu, Jun Zhao

英文摘要： While multi-turn agentic reinforcement learning (RL) excels on long-horizon tasks, its performance gains remain highly sensitive to interaction constraints. Evaluating across sampling breadth $K$ and interaction depth $T$ via $Pass@(K,T)$, we show that trajectory-level RL advantages depend on sufficient turn budgets, diminishing significantly when budgets tighten. Besides, agentic RL does not uniformly elevate single-step decision quality; rather, its strength concentrates in error recovery and long-horizon resilience. To mitigate the deployment risks caused by this budget dependence, we provide a preliminary discussion on the benefits and limitations of process supervision signals and introduce BudgetMix RL as an alternative that varies interaction budgets during training. Experiments demonstrate that BudgetMix RL achieves robust performance across diverse execution settings. Project Page: this https URL

AI 总结：该研究针对基于Qwen3.5-9B的吃豆人视觉语言智能体，通过感知检查与策略强化学习研究，发现图像转录消除可提升迷宫完成率，提出训练后需评估感知、区分训练与验证的经验。

链接：https://arxiv.org/abs/2611.06704

作者：Xinglu Wang, Edward Wang, Yong Zhang, Zhenan Fan

英文摘要：Training an agent to play Pac-Man with only up, down, left and right actions raises a basic question: when performance is poor, what should be improved? Difficulty can arise from reading the screen or from choosing actions over a long sequence. We examine these problems in a Qwen3.5-9B-based agent through separate perception checks and primitive-policy reinforcement-learning studies. In the policy study, which uses textual boards to remove image transcription from the decision interface, completion on eight held-out mazes increased from 0% before this optimization to 80.2% at a selected checkpoint, with three extra lives. The perception checks show that accurate static screenshot reading can coexist with errors during interaction. A separate policy comparison also shows that further optimization can accompany worse action choices. Together, these findings motivate concrete checks for post-training: evaluate perception in use, distinguish training success from full-game validation, and examine decisions and later performance rather than relying on one selected score. The study provides empirical lessons for improving future agent training and evaluation.

AI 总结：本研究探究通用机器人策略能否实现多任务通用化改进，通过开发结构化评估方法并系统评估算法设计选择，证明基于价值的强化学习结合合适方案可实现机器人基础模型的在线通用化改进。

链接：https://arxiv.org/abs/2611.06905

机构：ETH Zurich（苏黎世联邦理工学院）； MPI for Intelligent Systems, Tübingen（图宾根智能系统马克斯·普朗克研究所）； Technical University of Munich（慕尼黑工业大学）； University of Warsaw（华沙大学）； Nomagic（诺马吉克公司）； Carnegie Mellon University（卡内基梅隆大学）； UC Berkeley（加州大学伯克利分校）； Microsoft（微软公司）； Amazon FAR（亚马逊FAR部门）； Physical Intelligence（物理智能研究所）

作者：Michal Korniak, Marco Bagatella, Ralf Römer, Michal Nauman, Yarden As, Pranav Vignesh, Manan Agarwal, Leander Diaz-Bone, Piotr Zalewski, Mikołaj Czarnecki, Lenart Treven, Shashwat Saxena, Sreyas Venkataraman, Sarvesh Patil, Zhiyuan Zhou, Daniel Marta, Oier Mees, Nur Muhammad Mahi Shafiullah, Carmelo Sferrazza, Bhavya Sukhija, Angela P. Schoellig, Andreas Krause, Max Simchowitz, Andrew Wagenmaker

英文摘要：The introduction of generalist robot policies has enabled a single, large model to learn to perform a variety of tasks from a vast pre-training corpus. While these models often exhibit powerful zero-shot generalization capabilities, they typically require an adaptation phase to achieve sufficient performance in deployment. Iterated self-improvement via reinforcement learning (RL) has demonstrated promise in enabling this adaptation, yet such approaches have been largely explored in single-task settings - where the goal is to simply improve on a narrow deployment task - which lies in stark contrast to the multi-task capabilities of the base policy. In this work we ask whether generalist robot policies are capable of multi-task, generalist improvement in deployment - targeting many diverse tasks simultaneously - and what algorithmic recipe enables this. To answer this question, we first develop a set of structured evaluations to test generalist improvement capabilities, and systematically evaluate algorithmic and practical design choices regarding value estimation, policy extraction and resource allocation. We show that not only can generalist policies be improved online across diverse tasks, but that the right recipe can enable generalization and increase performance on tasks not considered during online training. We characterize when this generalization occurs and show that both the training task distribution and the use of value-based RL are critical. To the best of our knowledge, this is among the first evidence for online generalist improvement for robot foundation models.

AI 总结：提出拉普拉斯约束施蒂费尔流匹配（LaSFM），在谱图生成中同时保留拉普拉斯零空间约束等特性，扩展至属性引导生成后，在相关指标及目标属性对齐上表现更优。

链接：https://arxiv.org/abs/2611.05436

机构：The University of Tokyo（东京大学）； National Institute of Informatics（情报信息学研究所）； Jeonbuk National University（全北国立大学）

作者：Anita Yang, Ken-ichi Kawarabayashi, Yohan Jung

英文摘要：Spectral graph generation models graphs through the eigenvalues and eigenvectors of their Laplacians, providing a compact and continuous alternative to direct graph construction. However, these eigensystems must retain the structural properties of graph Laplacians. Existing approaches preserve eigenvector orthonormality but do not enforce the Laplacian nullspace constraint, potentially producing eigensystems inconsistent with Laplacian structure. To address this, we introduce Laplacian-Constrained Stiefel Flow Matching (LaSFM), which preserves the nullspace condition alongside eigenvector orthonormality and positive eigenvalues during generation. We characterize the underlying constrained Stiefel manifold and formulate flow matching on it. We further extend LaSFM to property-guided generation, steering samples toward desired graph properties while maintaining these constraints. Experiments demonstrate improved performance on both spectral and graph-level metrics, along with better alignment with target properties.

AI 总结：该研究提出结合注意力、VAE与DP的表格数据模型SAVANT，在公共数据集上与PrivBayes、MST对比，发现相同DP下数据越多效用越高，且未检测到聚合成员信号。

链接：https://arxiv.org/abs/2611.05582

机构：Foresight, Intuit（Intuit公司Foresight部门）

作者：Rohit Sadavarte, Dhruvi Kothari, Sparsh Gupta, Eric Van Oeveren

英文摘要：Data tends to be most useful when it is shared, and the data most worth sharing is often the data that privacy obligations restrict. Synthetic data from a generator trained under differential privacy (DP) is one way to reconcile the two, but its utility depends on the privacy budget $\varepsilon$ and on how much data the generator sees - a parameter that most data sharing applications of synthetic data control. We present SAVANT, a self-attentive variational autoencoder for tabular data trained with DP-SGD that, to our knowledge, is the first to combine attention, a VAE, and DP in one tabular model. We evaluate it against two marginal mechanisms, PrivBayes and MST, jointly over the privacy budget and the dataset size on two public datasets, measuring the utility a downstream classifier loses by training on synthetic rather than real data. We find that, under the same DP guarantee, more data generally means more utility. For the marginal mechanisms it absorbs most of what a strict budget costs, and a noise-scaling argument suggests DP-SGD exchanges data fo budget at a worse rate. Two independent membership-inference attacks, judged against a measured null, found no aggregate membership signal.

AI 总结：针对MIMO-OFDM系统单步信道估计的高推理成本问题，提出CMT等方法，在降低模型规模和计算量的同时，显著提升了符号错误率性能。

链接：https://arxiv.org/abs/2611.06271

机构：KTH Royal Institute of Technology（KTH皇家理工学院）

作者：Fredrik Nguyen, Qiwen Wang

英文摘要：Iterative neural channel estimators can improve recovery under pilot contamination, but repeated network evaluations increase inference cost. This paper develops observation-anchored consistency and Consistency Mid-Training (CMT) estimators for one-step channel estimation in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Both estimators refine pilot-derived least-squares channel estimates in the delay-beam domain. Consistency learning couples endpoint predictions along noisy-to-clean paths. CMT then refines a compact consistency-trained student to predict a flow-matching teacher's endpoint from intermediate trajectory states. Across the evaluated SNR range, the compact CMT student reduces uncoded symbol error rate (SER) by 9.6-20.2% relative to the one-step flow-matching baseline (0.44-0.98 dB in logarithmic SER reduction), while using 74.5% fewer parameters and 73.0% fewer network multiply-accumulates.

AI 总结：该研究提出能量引导传输框架，将物理约束转化为平滑势能，通过连续耗散力引导生成状态贴合物理流形，在保持流动力学的同时提升生成稳定性与推理效率。

链接：https://arxiv.org/abs/2611.06711

机构：Sapienza University of Rome（罗马第一大学）

作者：Giuliano Galadini, Aurelio Uncini, Danilo Comminiello

英文摘要：Deep generative models, particularly Flow Matching frameworks, have emerged as powerful tools for synthesizing complex physical systems. However, standard architectures remain purely statistically driven and agnostic to the underlying governing equations, often violating fundamental physical constraints during inference. Existing zero-shot approaches designed to enforce these constraints typically rely on rigid algebraic projections. While effective for sparse boundary conditions, applying such discrete geometric corrections to dense, full-domain PDEs may disrupt the probability path, \rev{potentially deviating from the learned manifold and introducing substantial computational overhead}. To address these challenges, we introduce Energy-Guided Transport, a novel training-free framework that formulates non-linear physical constraints as a smooth potential energy landscape. Rather than interrupting the ODE integration, our method applies a continuous dissipative force directly to the intermediate generative states, steering them toward the physical manifold. By evaluating this force implicitly via reverse-mode automatic differentiation, our framework preserves the natural dynamics of the flow while maintaining an efficient $\mathcal{O}(N)$ algorithmic complexity. We extensively evaluate our framework across various 1D and 2D physical systems, including the chaotic Navier-Stokes equations. Our method achieves stable generation without structural divergence, significantly improves the stability-accuracy trade-off compared to state-of-the-art methods, and substantially accelerates inference with respect to exact projection strategies. Code available at: this https URL.

AI 总结：本文将相关性传播建模为Filippov神经网络的熵最优传输问题，提出Sinkhorn相关性传播规则，在VGG-16和ConvNeXt-Tiny上验证了规则的性能与特性。

链接：https://arxiv.org/abs/2611.05594

机构：Arts et Métiers Institute of Technology（国立 Arts et Métiers 理工学院）

作者：Olivier Gibaru, Eric Nyiri

英文摘要： Relevance propagation, which redistributes a network's prediction backward through the layers, is usually treated as a heuristic. We show that it is the solution of a single variational problem: Layer-wise Relevance Propagation (LRP) is the divergence-free flow on the active computational graph of zero relative entropy to the backward transition law of the forward activation flow; for a non-negative reference, this is a relative entropy between Markov measures on backward paths. The rules LRP$_0$, LRP$_{z^+}$ and LRP$_{\alpha,\beta}$ follow from this principle by the choice of a reference, and are instances of one rule, LRPN$_{\alpha,\beta}$, whose weights may vary from node to node, for instance with the node's activity, in the spirit of Hebb's principle. The principle rests on a measure-theoretic formalization of feed-forward networks as directed acyclic graphs of Filippov operators, which covers piecewise-smooth and discontinuous architectures (e.g. hard-routed Mixture-of-Experts) with a Clarke-Filippov generalized-gradient calculus; it also characterizes backpropagation as the adjoint equation of a Lagrangian relaxation. On networks of ReLU and max-pooling nodes, LRP$_0$ is Gradient$\times$Input, with an explicit jump across a kink where LRP$_{z^+}$ stays continuous. Taking the entropy edge by edge yields a new rule, Sinkhorn Relevance Propagation. Aggregated over an input region, the flow of a non-negative reference tells, with exact accounting, which hidden units carry its evidence, the conditioned flow being a Schrödinger bridge. On VGG-16, the conservation identities hold to round-off, and the rules are told apart by the sign of their maps, the share of the output left to the bias nodes, faithfulness, localization and class dependence; on ConvNeXt-Tiny, LRPN$_{\alpha,\beta}$ conserves the output and its Hebbian weights give the highest ABPC of the rules tested.

AI 总结：本研究针对稀疏自编码器（SAEs）的特征对应问题，开发了能量加权最优传输框架，推导了联合渐近推断与置信带，在四个语言模型实验中定位了跨种子差异并分离了上下文依赖变化。

链接：https://arxiv.org/abs/2611.05655

机构：MSU Institute for Artificial Intelligence（莫斯科国立大学人工智能研究所）

作者：Alexandra Suvorikova, Ilgam Latypov, Anton Trukhan, Yuriy Dorn

英文摘要：Comparing sparse autoencoders (SAEs) requires connecting feature correspondence, differences between whole dictionaries, and uncertainty from finite evaluation data. We develop a framework that connects these tasks through an energy-weighted optimal-transport plan under a specified similarity geometry. The same plan defines a dictionary-level discrepancy, decomposes it into latent contributions, and quantifies how each latent's mass is distributed across counterparts. We characterize relationships between decoder, activation, and first-order causal similarities; idealized models clarify the interpretation of splitting and absorption. For fixed trained SAEs, we derive joint asymptotic inference for transport costs and fractions, accounting for activation similarities and both energy marginals estimated from the same documents. A joint document bootstrap gives asymptotically valid simultaneous confidence bands. Experiments across four language models locate cross-seed discrepancy and separate context-dependent changes.

AI 总结：本研究针对公平分类问题，推导了公平贝叶斯误差率的显式表达式，构造了仅用软标签和群体信息的一致估计器，可稳健估计最优公平性-准确性权衡曲线。

链接：https://arxiv.org/abs/2611.06027

机构：IIIT Delhi（德里印度信息技术学院）； RIKEN-AIP（理化学研究所人工智能项目）； Microsoft Research（微软研究院）； The University of Tokyo（东京大学）； IIT Kanpur（坎普尔印度理工学院）

作者：Mohit Sharma, Okan Koç, Amit Deshpande, Takashi Ishida, Gang Niu, Rajiv Ratn Shah, Masashi Sugiyama

英文摘要：There is a fundamental limit to the best prediction error of any model for a given data distribution. In classification, the Bayes error quantifies this limit and serves as both a benchmark for trained models and a criterion to detect overfitting. The state-of-the-art Bayes error estimation techniques for binary classification use only soft labels (positive class probabilities) and require no input features or auxiliary training. We extend such notions to constrained classification problems, such as fairness. Fair classification is an important case of constrained or multi-objective classification. It requires a model not only to be accurate but also to yield fair outcomes (equal or near-equal true positive rates) across different demographic groups (e.g., race and gender). This leads to an inherent fairness-accuracy tradeoff or a Pareto frontier that is used to compare fair classifiers against each other. The focus of our work is to estimate the fundamental limit for this fairness-accuracy tradeoff. We derive an explicit expression for the fair Bayes error rate (i.e., best achievable error rate subject to given fairness constraints), and construct consistent, instance-free estimators for it using only soft labels and group information. We provably bound the bias and the sample complexity of our estimators. Empirically, our method produces a robust, low-variance estimate of the optimal fairness-accuracy tradeoff curve, even when the soft labels are noisy and derived from a classification model on hard labels.

AI 总结：提出S³GLC框架，结合谱签名与空间表示，引入基于最优传输的平衡聚类目标，在10个数据集上优于15个基线，缓解深度图级聚类的有效性不足与多类坍塌问题。

链接：https://arxiv.org/abs/2611.06052

机构：Hong Kong Baptist University（香港浸会大学）

作者：Xiaoyang Lin, Renchi Yang, Yuqi Chen, Xin Huang

英文摘要：Given a collection of $N$ graphs, graph-level clustering (GLC) seeks to partition the $N$ graphs into semantically meaningful groups, which serves as a fundamental task in analyzing graph-structured data. While Graph Isomorphism Networks (GINs) have become the de facto encoders for GLC, they still fail to distinguish graphs due to their neglect of global topology. On top of that, existing deep clustering frameworks suffer from severe "clustering collapse" issues, particularly in imbalanced multi-class scenarios. To address these challenges, we propose S$^3$GLC, a novel framework that integrates global topological characteristics with local neighborhood features for balanced GLC. Theoretically, we leverage the Hoffman-Wielandt inequality to construct Spectral Signature as a rigorous lower bound for graph edit distance (GED), providing a computationally efficient proxy for global topological features. We additionally propose a joint manifold-preserving sparse regression module to fuse Spectral Signature with Spatial Representation, eliminating noise while preserving the complementary information. Furthermore, to mitigate clustering collapse, we introduce a balanced clustering objective based on optimal transport, harnessing the Sinkhorn-Knopp normalization to enforce equipartition constraints on cluster assignments. Extensive experiments on 10 real-world datasets demonstrate that S$^3$GLC significantly outperforms 15 state-of-the-art baselines, achieving remarkable improvements on benchmark datasets such as REDDIT-12K and OGBG-PPA.

AI 总结：本研究提出利用静态协变量的全局TFT模型，在48小时风电预测中优于局部模型和线性基线，支持从单站点向可扩展多站点预测的转变。

链接：https://arxiv.org/abs/2611.06130

机构：Karlsruhe University of Applied Sciences（卡尔斯鲁厄应用技术大学）； Fraunhofer Institute for Industrial Mathematics（弗劳恩霍夫工业数学研究所）

作者：Viktor Walter, Andreas Wagner

英文摘要：Accurate short-term wind power forecasting is critical for grid stability and electricity trading. While transformer-based deep learning is state of the art, industry practice relies mostly on local models trained for individual assets. This study investigates whether a global temporal fusion transformer (TFT), trained jointly on a heterogeneous portfolio of wind farms across Germany, outperforms site-specific local models in 48-hour-ahead forecasting. Using numerical weather prediction (NWP) data from the regional ICON-D2 and the global ECMWF HRES models, we compare the global TFT against local models and a linear regression baseline. The global model leverages static covariates, such as farm age, altitude above sea level, hub height, rotor diameter and power curve parameters, to learn shared representations across sites. We evaluate on 100 synthetic wind farm profiles and a real portfolio of 11 German wind farms, across two seasonal test periods with intermediate retraining. The global TFT outperforms the local models for 87% of the synthetic and 77% of the real wind farms and clearly surpasses the linear baseline. Wilcoxon signed-rank tests confirm that these gains are significant across both seasons and datasets. Crucially, this superiority depends on static covariates: without asset-specific context, the global model fails to generalize. The TFT's variable selection network (VSN) reveals the learned feature selection. Altitude emerges as a relevant static discriminator on both datasets, while hub height is decisive on the real portfolio. For dynamic inputs, the encoder prioritizes historical power, whereas the decoder combines upper-level wind speeds from both NWP models, consistent with averaging out model-specific errors. These findings support a shift from single-site to scalable multi-site forecasting, reducing operational complexity while improving accuracy in large portfolios.

AI 总结：该研究针对节气门无模型控制的迭代反馈调谐存在的梯度有偏问题，分析误差来源，在数字孪生和硬件节气门上测试不同更新规则，发现梯度噪声结构决定最佳规则，数字孪生可重现机制但无法复现硬件上的性能排名。

链接：https://arxiv.org/abs/2611.06338

作者：Félix Martinez-Jiménez (GIPSA-INFINITY), Sander Miller-Murphy (GIPSA-INFINITY), Tobie Ondoa-Essomba (GIPSA-INFINITY), Emmanuel Witrant (GIPSA-INFINITY)

英文摘要：Iterative feedback tuning (IFT) learns the parameters of a fixed-structure controller by stochastic gradient descent. At each iteration, two closed-loop experiments are run on the plant: the first one measures the tracking error, and the second one, driven by this error, provides the sensitivities from which the gradient of the cost is estimated. This estimate is unbiased for linear plants only. We study the biased case on automotive electronic throttle valves, whose friction and stiction vary between valves and runs, controlled by an intelligent proportional (model-free) controller, which is an incremental PI controller. We show that the mismatch of the controller state at the start of the two experiments, which stiction makes unavoidable, adds an error inversely proportional to the amplitude of the second experiment. On digital twins of ten valves, the IFT gradient is a descent direction in only 69\% of the low-cost region, friction being the cause, and all update rules plateau and then drift. On two hardware valves, the best update rule depends on the noise structure of the gradient. On the first valve, where one gradient component is buried in noise, Adam outperforms Gauss--Newton, its normalisation and its momentum each improving one parameter. On the second valve, where both components are consistent, the same normalisation amplifies a direction that Gauss--Newton rightly discounts, and Adam drifts to a controller worse than the initial one. The twins reproduce the mechanism but not this ranking.

AI 总结：该研究证明未知协方差时差分隐私高斯均值估计无法实现无维度保证，给出两种样本量场景下的误差下界，表明需依赖维度。

链接：https://arxiv.org/abs/2611.06615

机构：Center of Applied Mathematics, École polytechnique（巴黎综合理工学院应用数学中心）； UC Berkeley（加州大学伯克利分校）； Department of Computer Science, Boston University（波士顿大学计算机科学系）； Khoury College of Computer Sciences, Northeastern University（东北大学库尔计算机科学学院）； Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）； The Simons Institute for the Theory of Computing at UC Berkeley（加州大学伯克利分校西蒙斯计算理论研究所）

作者：Renaud Gaucher, Ephraim Linder, Adam Smith, Jonathan Ullman, Lydia Zakynthinou

英文摘要：We study the limits of differentially private Gaussian mean estimation when the covariance matrix is unknown. Prior work shows that when the covariance $\Sigma$ is known, the optimal sample complexity can depend only on its spectrum, rather than on the ambient dimension $d$. We establish new lower bounds for Gaussian mean estimation with Euclidean error which show that private estimators cannot achieve similar guarantees when the covariance is unknown. First, when the sample size $n$ is smaller than the effective dimension $\mathrm{trace}(\Sigma)/\|\Sigma\|_{\text{op}}$, the optimal error when the covariance is unknown can be larger than the optimal error when it is known by a factor of $\sqrt{d/n}$. Second, when the sample size is larger than the effective dimension, even for diagonal covariance matrices with effective dimension bounded by $\log d$, the optimal error when the covariance is unknown can be larger than the optimal error when it is known by a factor of $\sqrt{d}/n^{3/2}$. Together, these results show that in the unknown covariance setting, exploiting anisotropic structure under differential privacy can require a dimension-dependent error or sample complexity.

AI 总结：该研究基于中心流动力学分析稳定性边界处的对称性破缺，探究梯度流守恒定律的演化，通过三类模型明确了稳定性边界处守恒定律的保留与破缺情况。

链接：https://arxiv.org/abs/2611.06718

机构：Technical University of Denmark（丹麦技术大学）

作者：Naima Elosegui Borras, Georgios Arvanitidis

英文摘要： Continuous symmetries in neural networks lead to conserved quantities that are invariant under gradient flow, such as layer balancedness in linear and ReLU networks. Finite-step gradient descent need not preserve these laws, particularly at the edge of stability (EOS), where the iterates oscillate around a smoother trajectory, which can be described by the central flow dynamics (Cohen et al., 2025). We study how gradient flow conservation laws evolve under the central flow description of this regime. We show that the curvature correction induces a finite-dimensional drift determined by the oscillation covariance along critical parameter directions. We thus take the first steps to analyse which conservation laws remain and are broken at the EOS according to the central flow dynamics by analysing a scalar two-layer model, two-layer matrix factorisation, and a two-layer ReLU network, where the known conservation laws are shown to be complete (Marcotte et al., 2023).

AI 总结：针对单次和少轮联邦学习中模型漂移、参数重要性差异导致的性能下降问题，提出FedFiOT框架，利用Fisher信息指导神经元对齐与模型聚合，实验验证其性能优于基线。

链接：https://arxiv.org/abs/2611.06783

机构：University of Florida（佛罗里达大学）； Seagate Technology（希捷科技）

作者：Quan Minh Nguyen, Hoang M. Ngo, Bhakti Chowkwale, Yasaman Keshtkarjahromi, My T. Thai

英文摘要：One-shot and few-round federated learning (FL) reduce communication burden by constructing a global model within one or a few communication rounds, but often achieve lower performance than conventional multi-round FL. Without frequent synchronization, client models may drift apart, causing neurons that learn similar features to occupy different positions across clients. At the same time, local data heterogeneity can cause corresponding parameters to differ in their importance to each client's predictions. Together, these challenges cause direct averaging to combine mismatched neurons and assign equal weight to parameters despite differences in their importance. We propose FedFiOT, a Fisher-weighted optimal transport framework for one-shot and few-round FL that leverages Fisher information to quantify parameter importance and guide neuron alignment and model aggregation. FedFiOT jointly optimizes neuron correspondences and global model parameters through alternating minimization of a shared Fisher-weighted objective. Theoretically, we show that FedFiOT's optimization procedure monotonically decreases the layer-wise merging objective and establish a stationarity bound that quantifies the effects of client heterogeneity and nonuniform Fisher this http URL across multiple datasets demonstrate consistent improvements over competing baselines in both one-shot and few-round federated learning settings.

AI 总结：针对噪声信道下去中心化联邦学习的拜占庭攻击与信道噪声问题，提出FedSharp框架，通过筛选候选模型、降低噪声方差及正则化等方法，在MNIST和CIFAR-10上显著优于现有基准。

链接：https://arxiv.org/abs/2611.05546

机构：School of EECS, Ohio University（俄亥俄大学电子工程与计算机科学学院）； School of EECS, University of Ottawa（渥太华大学电子工程与计算机科学学院）

作者：Ojaswi Sinha, Amin Farajzadeh, Animesh Yadav, Melike Erol-Kantarci

英文摘要：Decentralized federated learning (DFL) eliminates the central aggregation server but remains vulnerable to data heterogeneity, Byzantine attacks, and channel noise. We show that noise accumulates as models propagate across nodes, becoming increasingly significant as the learning rate decays. Moreover, accumulated noise enables Byzantine attackers to exploit loss-based model selection by mimicking honest model averages. To address these challenges, we propose screened, hop-aware, ranked, proximal averaging, a robust DFL framework (FedSharp). In this approach, each node maintains models from its 2b predecessors, ensuring at least b honest candidates when at most b nodes are Byzantine. FedSharp screens suspicious models using coordinate-wise median deviations, averages the b lowest-loss candidates to reduce fresh channel-noise variance by a factor of b, and applies noise-adaptive gradient-norm regularization and proximal training to mitigate accumulated noise and non-IID drift. Experiments on a 100-node ring with 33 Byzantine nodes show that FedSharp achieves 93.5-94.9% accuracy on MNIST, compared with 29.4-38.1% for a state-of-the-art single-model-selection benchmark, and 32.8-48.8% on CIFAR-10, compared with 10.0-30.5%, across Gaussian and hidden attacks under IID and non-IID data. On MNIST, FedSharp also achieves accuracy within approximately one percentage point of the benchmark operating without channel noise.

AI 总结：本研究提出IDSISA方法，通过影响力函数集中易遗忘样本并结合跨分片蒸馏恢复效用，在多数据集上相比SISA实现了更好的选择性遗忘与效用保留。

链接：https://arxiv.org/abs/2611.06519

机构：Sardar Vallabhbhai National Institute of Technology Surat（萨达尔·瓦拉巴伊国家技术学院苏拉特分校）

作者：Deeraj SK, Sadhana Devarajan, Tanmoy Hazra, Anupam Shukla

英文摘要：Machine unlearning removes the influence of specific training examples to comply with privacy regulations. Full retraining gives strong guarantees but is computationally prohibitive at scale; approximate methods are efficient but lack formal guarantees. SISA (Sharded, Isolated, Sliced, and Aggregated training) achieves certified deletion through data sharding and independent retraining, but random partitioning scatters forget requests across most shards, and isolated retraining degrades utility. We introduce IDSISA (Influence-Distilled SISA), which uses influence functions to concentrate forget-prone samples into few shards and distillation from unaffected shards to recover utility lost to isolation. The deletion guarantee inherited from SISA's retraining step remains exact and requires no probabilistic qualification; the distillation-based utility recovery is a separately quantified, empirically bounded approximation ($\epsilon_{\text{cert}} \leq 0.015$ in expectation). We further formalize certification under sequences of repeated deletion requests, where cross-shard distillation can create dependencies between shards; we prove that a closure-based cascading retraining procedure restores the deletion guarantee to the full aggregated model under any deletion sequence. Experiments on ResNet-18/CIFAR-10, ResNet-18/CIFAR-100, BiLSTM/IMDB, TCN/IMDB, and a fully connected network on Fashion-MNIST show $14-91\%$ better selective forgetting, up to $50\%$ better utility retention in relative terms (with consistent percentage-point gains on the other architectures, where a relative baseline is undefined), and unlearning-time changes ranging from $-6.7\%$ to $+6.4\%$ depending on architecture, relative to SISA.

AI 总结：该研究针对机器遗忘的评估问题，证明遗忘模型与重新训练模型存在特征编码差异，引入特征恢复度量，发现标准指标表现好的模型常无法恢复重新训练特征，纠正该差异可降低后续偏差。

链接：https://arxiv.org/abs/2611.06681

机构：CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）； Saarland University（萨尔大学）； University of Copenhagen（哥本哈根大学）

作者：Polina Dolgova, Amartya Sanyal, Sebastian U. Stich

英文摘要：Machine unlearning is commonly evaluated with standard post-deletion metrics that compare an unlearned model to one retrained on the remaining data. Such snapshot evaluations can conceal consequential differences in the features the two models encode. We prove that if an unlearned and a retrained model agree on their outputs but encode different features, a single gradient step on their prediction heads can break this agreement. We further show that deleting an arbitrarily small fraction of the data can cause retraining to select qualitatively different features, so matching retraining can require learning features that the full model never had. We then introduce a measure of feature recovery that separates missing from extraneous features. Across eight unlearning methods, models that perform well on standard metrics often fail to recover the retrained features. This mismatch predicts deviations from retraining under subsequent relearning and downstream adaptation, and directly correcting it systematically reduces these deviations.

AI 总结：本研究针对对比激活加法（CAA），通过行为-能力边界框架，在4个指令微调模型上验证其干预效果，明确其可用范围受多因素约束，为激活引导的实用化提供了量化依据。

链接：https://arxiv.org/abs/2611.06001

机构：The University of Chicago（芝加哥大学）

作者：Deepanshu Raj

英文摘要：Activation steering can modify language-model behavior without parameter updates, but behavioral efficacy alone does not determine whether an intervention is usable. Contrastive Activation Addition (CAA) steers targeted behaviors using activation directions derived from contrasting examples, potentially degrading unrelated model capabilities in the process. We therefore study CAA through a behavior-capability frontier, selecting interventions that maximize behavioral gain subject to an explicit bound on capability degradation. We evaluate this framework across four instruction-tuned models, seven behaviors, and capability-loss budgets of 1%, 3%, and 5% (absolute percentage points). Validation-selected behavioral effects generalize strongly to held-out data ($\rho = 0.945$), while joint behavior-capability survival among nonzero operating points rises from 37.5% at the 1% budget to 72.7% at 3%, with limited additional improvement at 5%. Control experiments indicate that these effects depend on learned behavior directions rather than generic activation perturbations. Representational geometry is significantly but only modestly predictive of causal cross-behavior transfer. Finally, secondary capability, out-of-distribution, and open-ended evaluations show that interventions satisfying protected benchmarks can still degrade untested capabilities or transfer unevenly to free-form generation. These results characterize activation steering as a constrained intervention whose usable range depends on model, behavior, direction, layer, and evaluation budget, rather than a universal control knob.

AI 总结：该研究以类条件哈密顿动力学为测试平台，发现分布外检测中存在吸引-排名不匹配，通过分析揭示其对AUROC的影响，实验验证分数对齐规则的迁移性能缺陷，为分布外检测提供了新的研究方向。

链接：https://arxiv.org/abs/2611.06021

机构：School of Information Science and Technology, Jinan University（暨南大学信息科学与技术学院）

作者：Zilin Wang, Lianghai Xiao

英文摘要：Attracting features toward learned in-distribution (ID) regions is an intuitive strategy for out-of-distribution (OOD) detection, but does such attraction improve detection? We investigate this question using class-conditioned Hamiltonian dynamics as a controlled testbed and identify an attraction-ranking mismatch: improving individual class-affinity scores need not improve the relative ordering of ID and OOD samples. Our analysis separates misalignment between the driving field and the detection score from ranking failure that persists even when they are aligned. An exact AUROC decomposition and a local boundary analysis identify relative score changes at the ID-OOD ranking boundary as the source of first-order AUROC change under regularity conditions. Experiments demonstrate that a score-aligned rule can improve performance on a held-out source validation split yet consistently degrade it after transfer, even without a single winning-class change. A separate perturbation study finds that near-boundary relative score changes track the direction of small AUROC changes in the tested settings. Finally, centroid approximation and shared scales connect the zero-step score to a static centroid family that includes the existing CTM detector. Together, these results explain why class attraction is insufficient to justify feature refinement and motivate evaluating its effect on the ranking boundary.

AI 总结：该研究发现快速对抗训练的灾难性过拟合是训练损失无法察觉的权重漂移，提出将权重重置为指数移动平均的修复方案，消除了灾难性过拟合，在基准数据集上取得了快速方法中最强的AutoAttack准确率。

链接：https://arxiv.org/abs/2611.06082

机构：Southern University of Science and Technology（南方科技大学）

作者：Chao Pan

英文摘要： Fast adversarial training (FAT) makes robust training affordable by replacing the multi-step attack with a single-step one, but it suffers from catastrophic overfitting (CO): partway through training, robustness to multi-step attacks collapses to near zero while the single-step attack used for training keeps being repelled. We show that CO is a quiet escape of the weights. Across the event the network's clean behavior quietly improves, and moving from the weights just before CO to those just after it changes the training objective only marginally while destroying most of the robustness. The training objective is therefore nearly blind to the displacement it causes, so it exerts no force to undo it. The exponential moving average (EMA) of the weights, which FAT protocols already maintain, does not accumulate this drift and stays robust through the entire event. This observation suggests a nearly free fix: when validation robustness drops, reset the weights to their EMA. We build this guard on top of a coupling-based base method designed so that its average stays inside the robust region. The resulting training removes CO in every one of our runs and attains the strongest AutoAttack accuracy among fast methods on all three benchmark datasets, including a clear margin on CIFAR-100, where it also clears the multi-step reference at roughly half its cost. Code is available at this https URL.

AI 总结：本文针对无目标故障数据的数据稀缺资产早期故障检测问题，提出仅需目标正常数据的ADANN框架，在轴承数据集上取得最优迁移性能。

链接：https://arxiv.org/abs/2611.06194

作者：Zekai Wang, Yuxuan Li, Zhangyue Shi

英文摘要：Reliability management of industrial fleets depends on early fault warnings, yet lifecycle data across similar assets are highly imbalanced. A few assets accumulate run-to-failure histories, while newly installed or rarely failing assets provide only normal-condition data. This paper asks how diagnostic knowledge can be transferred from a data-rich source asset to a data-scarce target asset when no fault data have been observed on the target. We formulate cross-asset early fault detection as a zero-shot domain adaptation problem, in which zero-shot refers to the absence of target fault samples, and propose a framework that requires only normal data from the target. The framework comprises (1) a statistical process control step that labels the source asset run-to-failure data with an exponentially weighted moving average chart, reducing manual annotation to verifying flagged segments, (2) an Adapted Domain-Adversarial Neural Network (ADANN) that combines domain-adversarial training and a Maximum Mean Discrepancy penalty with an Adapted Classification-Domain (ACD) loss, which anchors the classifier normal region to the target own normal data, and (3) an ahead-of-time labeling scheme with a prediction horizon that targets incipient faults. Under an exact-classification assumption, a theoretical analysis characterizes the domain-adversarial term for a normal-only target and the alignment state in which source fault features remain separated from target normal features. On a public run-to-failure bearing dataset, ADANN attains the highest mean F1-score among four transfer baselines, with a nominally significant precision gain over the strongest baseline, and its robustness is examined under synthetic noise.

AI 总结：本研究针对LLMs的CoT推理，将其视为组合式OOD泛化问题，通过理论分析与实验验证揭示其泛化机制，证明其可实现短到长及组合式泛化。

链接：https://arxiv.org/abs/2611.06438

机构：School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）； Singapore Management University（新加坡管理大学）

作者：Nuo-Jing Liang, Xiao-Tong Yuan, Pan Zhou

英文摘要：Chain-of-Thought (CoT) reasoning substantially improves Large Language Models (LLMs), yet its out-of-distribution (OOD) generalization mechanism remains theoretically underexplored. We study CoT as compositional OOD generalization: the target task lacks complete reasoning trajectories or direct input-output pairs, while training provides only in-distribution (ID) atomic step data. We show that CoT generalizes by repeatedly applying a shared single-step predictor to compose reusable ID transitions. Theoretically, let $T$ denote the number of reasoning steps in the target task, $n$ the number of training samples per sub-task, and $k$ the number of output tokens per step; then the OOD task risk is controlled by the sum of the ID sub-task risks, scaled by a coefficient of order $\mathcal{O}(k)$ that is independent of $T$, although the summed risk itself may still grow with $T$. A Rademacher-complexity analysis further shows that the estimation term is of order $\mathcal{O}(\sqrt{T/n})$, rather than linear in $T$, due to predictor sharing under a uniform capacity control on the shared class. For autoregressive Next-Token Prediction, the final-task risk is bounded by the best-in-class step-level risk scaled by the same coefficient of order $\mathcal{O}(k)$, plus an estimation term of order $\widetilde{\mathcal{O}}\big(k\sqrt{T/n}\big)$. A parity-coupled construction further shows that, for the sub-task empirical risk minimizer, the expected final-task risk is at least the best in-class risk plus $\Omega(\sqrt{T/n})$ when $T\le n$. Experiments on Atom-Task and GSM8K support the theory: CoT enables short-to-long and compositional generalization, while explicit state tracking is crucial. Our code is available at this https URL.

AI 总结：该研究提出一种与模型无关的信任层，为快速基座稳定性替代模型提供校准误差棒、分布外警报与少样本重新校准，经多类等离子体数据集测试，可提升替代模型在分布偏移场景下的可靠性与效率。

链接：https://arxiv.org/abs/2611.06597

机构：dataquanta LLC（达昆塔有限责任公司）

作者：Amin Vakhshouri

英文摘要： Machine-learning surrogates of linear peeling-ballooning stability now run in milliseconds, but they are mostly deployed as point estimators: they do not say how wrong they may be, or when a query lies outside what they were trained on. For a fusion company designing a new device, every equilibrium of interest is outside the training data of existing surrogates, so this is the case that matters. We describe a model-agnostic trust layer for stability surrogates, made of conformal error bars, out-of-distribution alarms that route doubtful cases to the physics code, and recalibration from a few simulations of a new design, and we test it with pre-registered hypotheses. On a large public dataset of JET-like equilibria with ideal-MHD growth rates, the error bars hold their nominal coverage in distribution and fall short under a shift in plasma shape; a few dozen simulations of the new shape, or routing the cases the alarms flag, restore it. On resistive BOUT++ surrogates, the bars stay calibrated on held-out runs, the alarm flags tokamaks absent from training, and a scenario scan is screened in seconds instead of hours. Across equilibria from the SPARC library and the DIII-D coil set, a surrogate trained on our own graded BOUT++ labels beats the baseline on unseen equilibria, while a surrogate trained elsewhere ranks the same cases backwards and its alarm declines them. Building our own neural-operator surrogate, the harness exposed two input defects whose fix closed its gap to a simple network. For every surrogate tested, the trust layer keeps coverage near target, with a bar width set by that surrogate's own error. Not every pre-registered hypothesis held, and we report those results and the limits of all of them.

AI 总结：本研究针对近分布外检测难题，提出与检测器无关的PLUS子空间信号，将其与基础分数融合，可稳定提升近分布外检测性能，同时基本保持远分布外性能。

链接：https://arxiv.org/abs/2611.06688

机构：Yonsei University（延世大学）

作者：Wonseok Shin, Songkuk Kim

英文摘要：Post-hoc out-of-distribution (OOD) detection is mature on far-OOD yet remains weak on near-OOD. These two settings differ in their use of class-characteristic feature directions: far-OOD inputs largely fail to activate those of their predicted class, whereas near-OOD inputs activate them much as in-distribution samples do. Because the axis separating far-OOD leaves near-OOD almost indistinguishable from ID, a score optimized for one setting is misaligned with the other; where a detector already saturates far-OOD, added near-OOD sensitivity is bought partly against it. This divergence motivates a targeted near-OOD signal designed to complement existing scores. We propose PLUS (Per-class Low-dimensional sUbspace Signal). For each class, PLUS selects the $m$ most class-active neurons from a sparse autoencoder, orthonormalizes the corresponding decoder directions to form a low-dimensional subspace, and scores an input by its per-class whitened squared Mahalanobis distance $z$ within the subspace of its predicted class. Designed as a dedicated complement rather than a standalone detector, the signal $z$ is fused additively with any calibrated base score at a single, fixed weight. This weight is a single global constant: no value is adjusted per backbone, per OOD dataset, or per deployment, and all calibration statistics are estimated from ID validation data alone. When applied to 18 existing detectors spanning diverse method families, $z$ consistently improves near-OOD AUROC in all 126 detector--backbone combinations. Far-OOD performance is broadly maintained or improved, with only marginal declines where the base score is near-optimal, making $z$ a robust, detector-agnostic complement across backbones.

AI 总结：本研究对比了四种重要性归因方法在关系数据库深度学习模型中的表现，发现其效果受预测类型影响，单属性影响可替代夏普利值以降低计算成本。

链接：https://arxiv.org/abs/2611.05986

机构：Technion（以色列理工学院）； Northeastern University（东北大学）

作者：Hila Levi-Yosefi, Shunit Agmon, Ester Livshits, Agapi Rissaki, Benny Kimelfeld

英文摘要：Deep learning over relational databases has led to increasingly effective prediction models, motivating a growing need to explain their behavior. We study explanation methods that assign importance scores to individual database attributes, focusing on a fundamental question: to what extent does the choice of importance-attribution method matter? We develop a methodology to systematically compare importance-attribution methods and analyze their similarities and differences. In particular, we study two aspects of the explanations: which attributes they deem important, captured by differences in attribute rankings, and how importance scores vary across attributes, captured by differences in score distributions. We further investigate whether these differences are reflected in the conventional measure of explanation quality, fidelity. We apply our methodology in an extensive empirical study of four methods: Monte Carlo Shapley, Kernel SHAP, single-attribute impact, and mask learning. We experiment with multiple datasets and tasks using two RDL architectures: heterogeneous GraphSAGE and Relational Graph Transformers. Our experiments show that explanation methods behave differently depending on the prediction type: regression or classification. In regression, most methods exhibit high agreement in attribute rankings, whereas classification yields considerably less consensus. Notably, while mask learning frequently diverges in its importance distributions, Shapley-based methods and single-attribute impact often converge to similar explanations. This suggests that, in many settings, single-attribute impact can replace Shapley at substantially lower computational cost. Overall, our study provides methodological tools for comparing importance-attribution methods, and empirical evidence that their choice has important consequences for interpreting relational deep learning models.

AI 总结：该研究针对异质图中传统图神经网络的正定几何先验不匹配问题，提出图签名指数衡量不匹配程度，构建伪黎曼注意力网络（PRAT），在15个节点分类基准上取得优异性能。

链接：https://arxiv.org/abs/2611.06265

机构：SolverX（SolverX公司）； Max Planck Institute for Mathematics（马克斯·普朗克数学研究所）

作者：Yun Young Choi, Asung Kil, Sun Woo Park, Minho Lee, Seokhwan Kim

英文摘要：Several widely used families of Graph Neural Networks carry a positive-definite geometric prior. Graph convolution read as heat diffusion needs a positive semi-definite operator, hyperbolic models place features on positive-definite Riemannian manifolds, and dot-product and GAT-style attention scores use positive-definite inner products. On heterophilic graphs, where edges often signify dissimilarity or structural repulsion, that prior is a mismatch: no positive-definite form can assign a negative squared distance to a direction the labels separate. To measure the mismatch, we introduce the Graph Signature Index ($s_{\mathcal{G}}$), the inertia of a class-compatibility matrix built from label topology alone, and we prove that no value of the edge or node homophily index in $(0,1)$ rules out a negative eigenvalue. We then propose the Pseudo-Riemannian Attention Network (PRAT), whose attention logit is a signed squared pseudo-distance under a learnable diagonal metric initialized from $s_{\mathcal{G}}$. The design is diagnose-then-learn: the signature supplies a sign structure that unconstrained training keeps outside near-zero directions, and the projection learns the rest of the metric around it. Across fifteen node-classification benchmarks PRAT outperforms or matches the strongest heterophily-adapted models. A controlled ablation ties that performance to the sign structure itself: with architecture and protocol fixed, forbidding the metric to be indefinite costs accuracy on the graphs $s_{\mathcal{G}}$ marks as indefinite and leaves the others unchanged.

AI 总结：该研究提出消息传递图持久性（MGP），将消息传递融入图过滤，证明其表达能力强于现有基于PH的描述符，且在多数图分类、回归数据集上表现更优。

链接：https://arxiv.org/abs/2611.06801

机构：Institute of Mathematics and Computer Sciences (ICMC), University of São Paulo（圣保罗大学数学与计算机科学研究所）； Mercado Libre（美客多）； Avra（阿夫拉）； Federal Institute of Ceará (IFCE)（塞阿拉联邦学院）

作者：Jorge Luiz Franco, Arthur Queiroz Moura, Moacir Ponti, Amauri Souza

英文摘要：Message-passing graph neural networks (GNNs) are limited in expressive power by the one-dimensional Weisfeiler-Leman test. Persistent homology (PH) provides topological descriptors that can complement GNN representations, but existing approaches typically compute persistence separately from the representation learning process. In this paper, we introduce Message-Passing Graph Persistence (MGP), a family of topological descriptors that incorporates message passing into graph filtrations. At each filtration step, node embeddings are updated using the current subgraph and associated with persistence events. These embeddings also determine the representatives of connected components during persistence computation. We prove that MGP is invariant under graph isomorphism and that it is strictly more expressive than the PH-based descriptors it extends. We evaluate MGP on an expressivity benchmark and on several graph classification and regression datasets. Experiments show that MGP outperforms other PH-based descriptors on most of these datasets.

AI 总结：本研究将通用视觉语言模型（VLM）应用于临床神经诊断，证明未经过大规模EEG预训练的VLM在相关任务中表现有竞争力，少样本适应性优于专用基础模型，且随模型规模扩大性能提升更显著。

链接：https://arxiv.org/abs/2611.05538

机构：Massachusetts General Hospital（麻省总医院）； Binghamton University, State University of New York（纽约州立大学宾汉姆顿大学）； Harvard Medical School（哈佛医学院）； Brigham and Women’s Hospital（布莱根妇女医院）

作者：Xin Zhou, Elena Sergeeva, Nan Bi, Wenyu Song, Lijun Yin, Hao Deng

英文摘要：Historically automated electroencephalography (EEG) systems was done using task-specific supervised models trained on purposely annotated data. A more recent modeling paradigm relies on creating a unified representation, or a 'foundation model', for multiple modality-specific tasks through large-scale domain-specific pretraining on the unannotated EEG signal. The quality of this unified representation directly depends on the availability of the modality-specific datasets. At the same time, generalist Vision Language Models (VLMs) exhibit broad few-shot learning capacities as well as good fine-tuning adaptability to different visual and language tasks. Can VLMs learn how to read and interpret EEG visually, emulating to a degree the way human clinicians inspect the EEG patterns? In this work we make a case that they can: we show that generalist VLMs without large-scale EEG pretraining perform competitively across a collection of clinical neuro-diagnostic tasks, can adapt to different tasks in a few-shot learning setting better than specialist foundation models, and exhibit better performance with model scaling when modality-specific models fail to exhibit.

AI 总结：本研究针对低资源豪萨语医疗ASR，采用MMS-1B-FL102声学模型，通过浅层融合与密度比率重评分的纯文本领域自适应方法，有效降低了词错误率，无需额外收集医疗语音数据。

链接：https://arxiv.org/abs/2611.05744

机构：RUZIVO Research Lab, African Leadership University（鲁齐沃研究实验室，非洲领导力大学）； Stanford University（斯坦福大学）； Mila – Quebec AI Institute and McGill University（米拉-魁北克人工智能研究所与麦吉尔大学）

作者：Oche David Ankeli, Hassan Adelani Luqman, Theodora Ngozi Egbunike, Joshua Gottschalk, Bonaventure F. P. Dossou

英文摘要：Automatic speech recognition (ASR) systems often struggle with specialised medical vocabulary, especially in low-resource languages where domain-specific speech data are scarce. We investigate text-only domain adaptation for Hausa medical ASR using the fixed MMS-1B-FL102 acoustic model and two decoding-time language-model fusion methods: Shallow Fusion (SF) and the Density Ratio Approach (DRA). A 4-gram KenLM trained on 19,421 Hausa health sentences serves as the medical target model, while a 75,372-sentence general-domain model provides the source prior for DRA. Evaluation uses 250 recordings of 25 physician-reviewed prompts from 10 speakers across six northern Nigerian states. Under prompt-disjoint five-fold cross-validation, SF and DRA reduce WER from 29.56% to 23.55% and 23.33%, respectively. Both significantly outperform greedy decoding (p < 0.001), while their difference is not significant (p = 0.423). However, general-LM-only SF achieves 23.96% WER, indicating that the medical LM's additional improvement over a general LM is modest. Medical Term Error Rate also improves under fusion, but does not establish a distinct medical-domain advantage. These findings demonstrate that lightweight, text-only language-model fusion can improve Hausa medical ASR without additional domain-specific speech collection.

AI 总结：本文提出PRISM-AMC无重放持续学习框架，用于自动调制分类，可缓解灾难性遗忘，在两类增量设置下实验显示其遗忘程度低，性能优于或相当于对比方法。

链接：https://arxiv.org/abs/2611.06066

机构：University of Texas at Austin（德克萨斯大学奥斯汀分校）； Applied Research Laboratories（应用研究实验室）

作者：Usman Akram, Matthew Graham, Haris Vikalo

英文摘要： Automatic modulation classification (AMC) is a key component of cognitive wireless systems, enabling spectrum awareness and adaptive communication. Most deep learning approaches to AMC assume a fixed set of modulation classes and access to previously observed training data. In practice, modulation classes and channel conditions may evolve over time, while storage or data-retention constraints may preclude replay of raw baseband I/Q samples. We propose PRISM-AMC, a replay-free continual learning framework that mitigates catastrophic forgetting by preserving task-relevant subspaces of intermediate representations via gradient projection memory. We further analyze projected stochastic-gradient updates under an SNR-dependent gradient-noise model. PRISM-AMC is evaluated in both class-incremental and domain-incremental settings. Experiments on the MIGOU and DeepSig 2018.01A datasets show substantially reduced forgetting relative to sequential fine-tuning and elastic weight consolidation, while remaining competitive with or outperforming replay-based RSD-AMC in class-incremental settings, without storing raw I/Q samples.

AI 总结：该研究通过微调Qwen3.5-4B的实证研究，发现保留SFT的长度归一化DPO带来的对齐代价具有任务选择性，不同结构化任务的能力损失程度不同。

链接：https://arxiv.org/abs/2611.06476

机构：University College Dublin（都柏林大学学院）

作者：Ye Yuan

英文摘要：Setting. This is an empirical characterization study; we propose no new method and run no comparisons against alternative objectives. We fine-tune Qwen3.5-4B with sparse LoRA and length-normalized DPO plus an SFT retention term (the RPO objective) on structured financial-filing tasks, and track downstream capability as the implicit reward margin grows to $\sim 7$ $\beta$-scaled units (nf4 training; $\sim 3$ under fp16 measurement on the unquantized base). Question. Does margin growth degrade capability uniformly across tasks, or selectively? Findings. In a pre-registered study (8 seeds, 6 unseen at freeze) with artifact-aware scoring and TOST equivalence testing, the alignment tax is graded and task-selective: extraction is preserved ($\Delta = -0.55$ pts, equivalent at $\pm5$ pts), json loses little ($\Delta = -3.52$ pts, equivalence fails on a clustered item-level interval), and router degrades severely ($\Delta = -30$ pts). Router's loss concentrates in a margin 1-3 damage window containing a transient format-break spike. The margin is accounted for almost entirely ($\geq 96\%$) by suppression of rejected completions, with chosen reward approximately flat, consistent with the retention term anchoring it; we do not attribute the behavioral damage to any single gradient channel. Gold-logp deflation is task-selective (extraction $-0.15$, json $-1.04$, router $-1.47$ nats/token under fp16), with gold selectively protected relative to incorrect controls on extraction (8/8 seeds). QLoRA reward margins differ up to $3.4\times$ between nf4 training and unquantized fp16 measurement, with an axis-dependent gap. Scope. Length-normalized rewards with SFT retention, sparse LoRA on 8/32 layers, one 4B model, easily-separable synthetic preferences, three structured tasks. A no-retention control and a measured format-alignment metric are left to future work.

AI 总结：该研究指出模型合并无需统一LoRA秩，为各专家分配不同秩可提升多领域准确率，而按损失影响选择分量的合并方法能保留所有专家并稳定准确率。

链接：https://arxiv.org/abs/2611.06486

机构：Amazon（亚马逊）； Amazon Web Services（亚马逊网络服务）

作者：Zirong Xie, Zainab Afolabi, Nikita Kozodoi, Jack Butler

英文摘要：Model merging combines separately fine-tuned experts into a single model without retraining. Standard practice trains every expert at the same LoRA rank, which couples the rank choice across experts even though each domain has its own best rank. We show this coupling is a property of the merge method, not of merging. We train 36 experts on three domains (mathematics, instruction following, multilingual) at four ranks and three model scales, then merge with six methods at uniform ranks and at each domain's own best rank. Giving experts different ranks costs averaging and utility-based selection nothing, and gains them up to 1.8 pp of multi-domain accuracy, while the conflict-resolution methods are inconsistent and can lose up to 4 pp. The split has a simple cause. Merging keeps only a fixed share of the pooled spectral components, and methods that choose them by size discard many that would have reduced loss. They also tend to discard the same expert: the instruction adapter has the smallest singular values, so it is dropped almost entirely and IFEval falls by 16 to 25 pp. Choosing components by their effect on loss instead keeps all three experts, and leaves accuracy nearly flat across an 8$\times$ rank range (0.7 pp, against 6.7 pp for TIES at 4B scale). Rank can therefore be chosen per expert whenever the merge operator does not select by magnitude.

AI 总结：该研究定义了行为速率$R^\star$作为微调所需信息的度量，发现微调可压缩性是数据集-接收者对的属性，且压缩可过滤适配器的噪声，提升泛化性能。

链接：https://arxiv.org/abs/2611.06598

机构：Université Paris Dauphine-PSL（巴黎多芬大学-PSL大学）； ESPCI PSL（巴黎高等物理化工学院-PSL大学）

作者：Joey David, Paul Caillon, Alexandre Allauzen

英文摘要：Low-Rank Adaptation (LoRA) adapts a frozen language model through a compact learned update, making it a natural setting for asking how much information fine-tuning needs to write. Yet adapter rank, parameter count, or numerical reconstruction error do not directly tell us how many bits are required to preserve the acquired behavior. We therefore define an operational behavioral rate $R^\star$ as the smallest serialized size of a compressed adapter that, once reloaded into the frozen base model, retains a fixed fraction of its held-out fine-tuning gain. Across LoRA fine-tuning experiments, this rate increases with distinct training content at fixed optimization budgets, but is not determined by intuitive metrics like source description length, fine-tuning gain, or weight-space reconstruction error. Most importantly, the same datasets exhibit substantially different, and even reordered, rates across frozen base models, showing that fine-tuning compressibility is a property of the dataset--receiver pair rather than of the dataset alone. However, we show that correction spectra measured on the data are able to rank these rates within a receiver. Finally, we study a striking noise-reducing effect of compression: adapters trained on deliberately corrupted datasets fall far below the frozen model, yet after compression score above it (23.7% to 82.9% on GSM8K, against 77.6% for frozen Qwen2.5-7B), consistently across tested families and sizes. The damage is unevenly concentrated in the adapter's leading singular directions, which compression appears to filter. Away from its training task, the compressed corrupted adapter also generalizes better than a clean fine-tune: on harder math it retains 97% of base accuracy, versus 44% for the clean adapter.

AI 总结：该研究针对持续线性分类，提出加权L2正则化变体，利用对角线性网络的稀疏性偏差，在适度任务周期上提升了收敛速度与性能。

链接：https://arxiv.org/abs/2611.06759

机构：Technion（以色列理工学院）； NVIDIA（英伟达公司）

作者：Gilad Karpel, Itay Evron, Ron Meir, Daniel Soudry

英文摘要：We study logistic regression for continual learning with linear predictors in a high-dimensional statistical setting. Existing theoretical work has largely focused on continual linear regression with variants of L2 regularization, motivated by their analytical simplicity and connection to the kernel regime of neural networks. We first derive the optimal L2 regularization strength for continual linear classification and characterize its generalization performance, recovering behavior analogous to prior results for continual linear regression. Next, we examine the effect of L2 regularization in deeper models using a minimal two-layer diagonal linear network. In the single-task setting, L2 regularization on the weights of such models is known to promote sparsity in the resulting predictor, yielding a rich, sparse feature-learning regime beyond the kernel regime (a similar effect arises in two-layer ReLU networks). However, in continual learning, where L2 regularization is typically centered on previous-task weights, this sparsity bias is no longer preserved. We therefore propose a weighted variant of L2 regularization that extends this beneficial sparsity bias to continual learning. We show theoretically and empirically that, for a sufficiently sparse teacher, our variant converges substantially faster than directly learning a linear predictor with conventional continual L2 or L1 regularization, trading some long-horizon bias for markedly improved performance over moderate task horizons.

AI 总结：该研究在六个临床数据集上对比量子与经典机器学习模型，发现量子模型未显著优于经典模型，提出QML-Health-READY报告框架，明确量子临床预测的可信报告要求。

链接：https://arxiv.org/abs/2611.05442

机构：School of Engineering, St Aloysius (Deemed to be University)（圣阿洛伊修斯（被视为大学）工程学院）； School of ICT, Bahrain Polytechnic（巴林理工学院信息与通信技术学院）

作者：Mohammed Siraj B, Gousia Habib, Ruban S

英文摘要：Background: Quantum machine learning (QML) is promoted for clinical prediction, but comparisons with classical models are rarely paired or tested for equivalence. Methods: We compared a quantum-kernel support vector machine (QSVM; 4 qubits, tuned bandwidth) and a variational quantum classifier (VQC) with five tuned classical models across six public clinical datasets. All models used identical four-component inputs and 25 cross-validation splits. Paired AUROC differences were assessed using a corrected resampled t-test, Holm-adjusted equivalence tests (margins: +/-0.02 and +/-0.05), and random-effects pooling. Ablation, noise, finite-shot, and implementation-fault analyses were conducted. We propose QML-Health-READY, an eight-domain reporting framework. Results: Neither quantum model significantly outperformed any classical comparator. Pooled across datasets, QSVM differed from the kernel-matched radial basis function (RBF) SVM by -0.006 AUROC (95% CI: -0.017 to +0.004), equivalent to +/-0.02. VQC differed from the best classical model by -0.007 (95% CI: -0.019 to +0.004). QSVM was equivalent to RBF SVM within +/-0.05 in four of six datasets but was significantly worse than the best classical model on NHANES 2017-18 (-0.076). A classically trivial product-state kernel performed as well as the entangling kernel. With depolarizing noise (p = 0.01) and 1,024 shots per kernel entry, QSVM AUROC decreased by 0.054 (95% CI: 0.036 to 0.071). Untuned bandwidth produced near-chance AUROC (0.48-0.59). Conclusions: Correctly configured quantum classifiers approached but did not exceed tuned classical models. Finite-shot estimation, rather than gate noise, was their main practical cost, while configuration errors could reverse conclusions. QML-Health-READY defines reporting requirements for credible claims.

AI 总结：针对分布到分布回归场景，提出正交基空间的共形预测框架及自适应变体，解决直接应用拆分共形预测的不可解释问题，实验验证了方法的有效性。

链接：https://arxiv.org/abs/2611.06320

机构：School of Computing（计算机学院）； National University of Singapore（新加坡国立大学）； Center for AI Research（人工智能研究中心）； VinUniversity； Department of Mathematics（数学系）

作者：Trung-Khang Tran, Tuan A. Hoang, Viet-Hoang Tran, Tan M. Nguyen

英文摘要：Conformal Prediction (CP) provides a model-agnostic framework for constructing finite-sample prediction sets with coverage guarantees and has become a tool for reliable decision-making in high-risk settings. Recent advances have extended Split Conformal Prediction to increasingly complex learning problems, including multilabel, high-dimensional, and functional outputs. In this work, we study CP for Distribution-to-Distribution Regression, a setting of growing interest across many application domains. We show that a naive application of Split CP yields prediction sets that, while marginally valid, are non-interpretable, e.g., lacking a well-defined volume or a tractable sampling procedure, and of limited practical value. To overcome this, we propose a new conformal framework that operates in an orthogonal basis space, producing interpretable prediction sets while preserving coverage guarantees. We further develop an adaptive variant with asymptotic conditional coverage under mild assumptions. Empirical results on synthetic and real-world datasets validate the effectiveness of the proposed approach. The code is publicly available at this https URL.

AI 总结：该研究发布了包含77个EEG数据集的EEGDash语料库，可用于训练EEG基础模型，通过重新训练REVE-Base验证其有效性，在OpenEEGBench任务上达到优于未训练编码器的性能。

链接：https://arxiv.org/abs/2611.06356

机构： Swartz Center for Computational Neuroscience, University of California San Diego（加州大学圣地亚哥分校斯沃茨计算神经科学中心）； Université Paris-Saclay（巴黎萨克雷大学）； Yneuro

作者：Kuber Shahi, Bruno Aristimunha, Arnaud Delorme

英文摘要：Foundation models for electroencephalography (EEG) are pretrained on tens of thousands of hours of recordings, and their accuracy grows with that amount. Such data is hard to assemble. The largest sources are clinical archives that cannot be redistributed, and the public sources are spread over several archives, file formats, and licences. We release eegdash-corpus-v0, a training-ready corpus of 77 public EEG datasets, as the gated Hugging Face dataset EEGDash/eegdash-corpus-v0 with the SHA-256 of every file. REVE, the largest published EEG foundation model, names 89 datasets as its pretraining corpus. Twelve cannot be redistributed: four clinical archives that hold 82% of its hours, and eight under no-derivatives licences. The corpus holds the 76 others that REVE's sampling rate allows and one more from REVE's release card: 77 datasets, 14,075 hours from 7,272 subjects, 5.0 million 10-second samples and 6.21 billion tokens per epoch. Every dataset is fetched through EEGDash, converted once with REVE's recipe, and stored in its own buckets of a chunked Zarr store with its licence recorded beside it, so a model can train on the whole store or on any subset of datasets. To test that the store pretrains a model, we retrain REVE-Base from scratch twice with the authors' code, changed only in how it reads data, and score it on seven public OpenEEGBench tasks. It reaches 0.331 under linear probing and 0.273 after fine-tuning, against 0.333 and 0.325 for the released model and 0.200 and 0.205 for an untrained encoder. The converter, the conversion reports, and the loader changes are released with the corpus.

AI 总结：本文提出三种部分可解释决策树算法，在真实数据集上性能相近，在黑盒准确率不均的合成数据集上，延迟感知广义CART优于常规CART，可替代概念瓶颈模型后的不可解释神经网络。

链接：https://arxiv.org/abs/2611.06428

机构：University of Amsterdam（阿姆斯特丹大学）

作者：Ludovic Schwartz, Federico Berton, Tim van Erven

英文摘要：Partially interpretable models extend simple interpretable models with the option to defer complicated inputs to an accurate but uninterpretable black-box model. By deferring more inputs, the model gets more accurate; by deferring fewer, it gets more interpretable. Existing procedures to train such partially interpretable models build on linear models or decision rules. We propose three algorithms for decision trees: a partially interpretable extension of the existing Branches algorithm, which performs exact search to optimize the tree; a conversion algorithm that we run on top of standard CART, which is computationally more efficient; and a generalized CART whose splitting criterion takes into account the option to defer while growing the tree. Perhaps surprisingly, the performance of the three algorithms is similar across many realworld datasets. We can explain this through a formal bound on the potential gain of smart tree construction, provided the black box accuracy is sufficiently homogeneous across the domain. We also construct a synthetic dataset where the accuracy of the black box is deliberately inhomogeneous. There, our deferral-aware generalization of CART does outperform regular CART and matches the performance of exact search. Finally, we evaluate an application to concept bottleneck models. Partially interpretable decision trees can replace uninterpretable neural networks after the bottleneck. On undeferred inputs, the model is fully interpretable, and deferring preserves accuracy.

AI 总结：本文提出隐式神经算子求解器（INOS），通过预训练神经算子结合Newmark-β积分，在无动态轨迹监督下实现高精度动态模拟，速度较经典有限元求解器提升超4600倍，可稳定运行百万步。

链接：https://arxiv.org/abs/2611.05439

机构：The University of Hong Kong（香港大学）； Tsinghua University（清华大学）

作者：Chawit Kaewnuratchadasorn, Jiaji Wang, Xin Nie

英文摘要：High-fidelity simulation of dynamical systems is central to scientific discovery and engineering design, yet remains computationally demanding. Here we introduce the Implicit Neural Operator Solver (INOS), which learns the implicit solve and encodes a time integrator. INOS pretrains a neural operator from static equilibrium solutions and reuses it within Newmark-$\beta$ integration. At each time increment, the integrator forms an effective equilibrium problem, the pretrained neural operator infers the next-step solution field, and the Newmark relations advance the dynamic state. Numerical examples in linear and nonlinear elastodynamics demonstrate accurate full-field predictions without dynamic trajectory supervision. Comparative results show that INOS, evaluated zero-shot, achieves higher predictive accuracy than neural solver baselines trained on dynamic simulations. In addition, INOS delivers over 4,600$\times$ speedup in nonlinear simulations over sequential classical finite-element solvers. INOS natively accommodates changes in dynamic conditions and enables reuse of the pretrained operator across spatial and temporal discretizations. Further evaluation demonstrates a stable million-step rollout. These findings establish INOS as a reusable neural implicit solver for accurate, efficient, and robust dynamic modeling.

AI 总结：本文针对异构排序模型缩放规律发现难题，提出含缩放层与Axia的自动化系统，在Meta广告系统实验中获0.66%离线熵提升与0.36%在线提升，实现计算资源高效分配。

链接：https://arxiv.org/abs/2611.05643

作者：John Martabano Landy, Louis C. Tiao, Yao Xuan, Luc Kien Hang, Rupinder P. Khandpur, Arvind M. Vepa, Srinidhi Raghavendran, Anirudh Agrawal, Xiaoyi Leo Liu, Jiang Liu, Peggy Yao, Alireza Vahdatpour, Djordje Gligorijevic

英文摘要： Large-scale ranking and recommendation models of Web platforms benefit from increased compute much as large language models do, and their scaling behavior is broadly power-law in form. Yet ranking models have heterogeneous architectures which makes their scaling law less straightforward than that of LLMs. We address how to discover the scaling law of an arbitrary ranking model efficiently. We introduce a scaling layer, which groups primitive architecture parameters into semantically coherent groups and defines how they co-vary, inducing a mapping from a low-dimensional space of hyper-hyperparameters into the full architecture space. On top of it, Axia fits scaling-informed surrogates, selects configurations by a posterior-based acquisition criterion, and returns a Pareto frontier from which per-model operating points are validated -- replacing exhaustive sweeps with a small number of informative runs. A fleet-level evaluation protocol, computed on a fixed representative model set under matched trial budgets, makes refinements to either component measurable. Our contribution is an automated system that learns model-specific complexity-quality relationships and gives uncertainty-aware guidance for allocating compute across a heterogeneous model ecosystem. Across 34 model iterations in two development cycles on Meta's ads ranking and retrieval system, the workflow yielded a mean 0.66% offline normalized-entropy improvement and 0.36% cumulative online lift.

AI 总结：提出ADD-FBPINNs方法，结合POUNets与FBPINNs，自适应划分计算域，在三类方程上较固定划分的FBPINNs精度更高。

链接：https://arxiv.org/abs/2611.05659

机构：University of North Carolina, Charlotte（北卡罗来纳大学夏洛特分校）； Pacific Northwest National Laboratory（太平洋西北国家实验室）； Delft University of Technology（代尔夫特理工大学）； University of Washington（华盛顿大学）； Brown University（布朗大学）

作者：Sarah Helfert, Wanxin Chen, Amanda A. Howard, Panos Stinis, Alexander Heinlein

英文摘要：In this work, we present adaptive domain decomposition finite basis physics-informed neural networks (ADD-FBPINNs). The proposed method integrates Partition of Unity Networks (POUNets), a data-driven approach for learning domain partitions, with finite Basis physics-informed neural networks (FBPINNs) to perform more efficient domain decomposition and achieve more accurate approximations. FBPINNs improve upon the accuracy of standard physics-informed neural networks (PINNs) through user-defined subdivisions of the computational domain. By adapting the domain decomposition based on the solution's features using a separable POUNet, the framework can automatically learn partitions that are stable and can be geometrically interpretable. We test this approach on the two-dimensional Helmholtz equation, two-dimensional Poisson equation with multiscale features, and viscous Burgers equation and observe improved accuracy compared with FBPINNs with fixed domain decomposition.

AI 总结：CoReFlow是一种新模型，通过利用流图单步生成能力、采用基于条件均值的训练目标，在四个真实世界数据集上实现了最优概率交通预测精度，同时降低了推理成本。

链接：https://arxiv.org/abs/2611.05715

机构：KTH Royal Institute of Technology（皇家理工学院）

作者：Pengnan Chi, Zhiguo Zhang, Xiaoliang Ma

英文摘要：Probabilistic traffic forecasting is essential for reliable Intelligent Transportation Systems (ITS), as it captures uncertainties in complex road networks. Recent diffusion- and flow-based generative models have facilitated uncertainty modeling through a multi-step sampling process. However, beyond slow inference, this process is designed to capture complex multimodal distributions, which misaligns with the limited complexity of the conditional traffic data distribution, causing models to underperform deterministic baselines. To address this, we introduce the \underline{Co}nsistency-\underline{Re}laxed \underline{Flow} Map (CoReFlow) model, presenting the first effort to leverage the one-step generation capability of flow maps, known as solution operators of probability-flow ordinary differential equations. Besides, we improve accuracy using a concise training objective that supervises the model via interpolation endpoints over finite time intervals. Unlike existing methods (e.g., self-distillation and MeanFlow Identity) that maintain trajectory consistency to model multimodal distributions, our objective leverages conditional mean to restrict the consistency, thereby achieving higher accuracy in the probabilistic traffic forecasting task. Extensive experiments on four real-world datasets show CoReFlow achieves state-of-the-art forecasting accuracy. It surpasses recent baselines, provides competitive uncertainty modeling, and reduces inference costs compared to existing generative baselines.

AI 总结：本研究提出MIGONet多输入图神经算子及离散MMS训练法，无需解PDE，在矩形域等测试中误差远低于MIONet等，大幅降低数据生成时间。

链接：https://arxiv.org/abs/2611.05876

作者：Muhammad Hasnain, N. K. Anand, Arun Srinivasa

英文摘要：Surrogate modeling of transport requires operators that capture the combined influence of spatially distributed sources and Dirichlet and Neumann boundary conditions, together with training datasets that can be generated at low computational cost. This work presents MIGONet, a multiple-input graph neural operator, and a discrete method of manufactured solutions (MMS) that trains it without solving a partial differential equation, using steady heat conduction as the model problem. In the discrete MMS procedure, temperature fields are prescribed a priori, and the source terms and Dirichlet or Neumann boundary data are evaluated with the same discrete operators as the reference finite-difference method (FDM), so every training sample satisfies the discrete governing equations by construction. Because the boundary data are derived from each field, a Dirichlet or Neumann condition can be assigned to every boundary segment of every sample, and the boundary specification becomes an operator input. MIGONet supplies representations of the complete source field and boundary data to every node before local message passing. On the rectangular domain, it is compared with a version without the complete-source encoder (MIGONet-local) and with the multiple-input operator network MIONet, using the same training data. MIGONet achieves mean relative temperature errors of 10.31% and 10.64% for curved-wire and Gaussian-spot heating, compared with 45.97% and 48.34% for MIONet and 72.25% and 67.82% for MIGONet-local. For 10,000 samples, discrete MMS reduces data-generation time from 486.65 s to 3.15 s relative to matched iterative FDM labeling. Separately trained MIGONet models are also evaluated on L-shaped, disc-shaped, and ring-shaped domains.

AI 总结：针对时间序列基础模型应用的领域偏移问题，提出两阶段细粒度知识增强框架，通过解耦趋势与季节信号构建残差码本，动态检索合并细粒度模式，提升模型效果与效率。

链接：https://arxiv.org/abs/2611.05993

机构：The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））； Tencent（腾讯）； Beijing Information Science and Technology University（北京信息科技大学）； Beijing Normal-Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）

作者：Yuqian Wu, Qingxiang Liu, Yuxin Guo, Weifeng Su, Jian Li, Pengfei Xu, Xi Chen, Yutao Yue, Yuxuan Liang

英文摘要：Time series forecasting is critical for demand prediction and resource management in modern Web services, yet applying pretrained Time Series Foundation Models (TSFMs) to downstream tasks remains challenging due to domain shifts. Existing retrieval-augmented approaches typically retrieve coarse-grained window contexts and may introduce context-specific noise. To address this problem, we argue that temporal patterns should be reused at a decomposition level, as effective trend and seasonal dynamics may originate from different contexts. We propose \method, a two-stage \underline{F}ine-grained \underline{K}nowledge \underline{A}ugmentation framework that decouples and consolidates pre-training knowledge into compact, task-shared residual codebooks to enhance frozen TSFMs. Specifically, Stage I constructs dual-branch residual codebooks for decoupled trend and seasonal signals, while Stage II dynamically retrieves and merges these fine-grained patterns into target representations via task-adaptive aggregation and gate fusion. Extensive experiments across eight real-world benchmarks with four representative TSFM backbones demonstrate the superior effectiveness and computational efficiency of \method, while further validating the strong transferability of the residual codebooks across different backbones. The source code is available at the anonymous repository: this https URL

AI 总结：本研究探究联邦时间序列预测中静态上下文的学习效果，发现仅每批次梯度聚合的FedAdam能充分利用静态输入，在多数据集上表现优于其他联邦优化方案。

链接：https://arxiv.org/abs/2611.06064

机构：Karlsruhe University of Applied Sciences (HKA)（卡尔斯鲁厄应用技术大学）； Fraunhofer Institute for Industrial Mathematics (ITWM)（弗劳恩霍夫工业数学研究所）

作者：Viktor Walter, Andreas Wagner, Fabian Stricker

英文摘要：Time series forecasting is shifting from locally trained, per-series models to global models trained across many heterogeneous series, which requires learning what sets the series apart. When the raw data cannot be centralized, such a global model is trained with federated learning (FL), one client per data owner. State-of-the-art forecasters such as the Temporal Fusion Transformer (TFT) tell series apart through a static input that describes each series and does not change over time. In the special case of a federated setting where each client holds a single series, this static input is constant within each client and varies only across clients. We ask whether a federated TFT can learn from it. Per-batch FedAdam, which aggregates gradients after every batch, uses the static input almost as well as central training, whereas communication-efficient schemes with local epochs (FedAvg, FedAdam+Prox) extract far less from it and gain nothing significant on either application dataset. At the same local computation, an Adam-compatible SCAFFOLD keeps the static pathway responsive on wind power and gains significantly there. On building energy its error still drops, but the static pathway falls silent, so drift and communication frequency cannot be fully separated as the cause. Only per-batch FedAdam matches or beats the per-client local models across all datasets. We confirm the effect on building-energy meter data, a wind-power dataset, and a controlled synthetic dataset. The loss is not specific to the TFT: on the synthetic and the wind data two further architectures with different static-conditioning mechanisms show it as well.

AI 总结：本文提出神经随机（NS）模型，结合Transformer、循环网络与随机潜过程，在两种膜系统中准确预测膜蛋白集体扩散，优于变分自回归模型，可生成长轨迹并保留关键统计特征。

链接：https://arxiv.org/abs/2611.06157

作者：Jacob Ede Levine, Mario Hidalgo-Soria, Chen Yun Wen, Yun Lyna Luo, Sai Chandra Kosaraju

英文摘要：Predicting the long-time diffusion of membrane proteins is challenging because molecular dynamics (MD) simulations are computationally expensive, while experimental single-particle tracking is often limited by finite observation times. Here, we introduce a neural stochastic (NS) model for forecasting the collective diffusion of membrane proteins from coarse-grained MD trajectories. The model combines a transformer-based representation of protein configurations, recurrent networks operating at multiple temporal scales, and a stochastic latent process with learned memory. A cross-attention decoder then generates correlated protein displacements while accounting for the instantaneous system geometry and periodic boundary conditions. We evaluate the model in two membrane systems: seven interacting PIEZO channels and a mixed system containing seven PIEZO and ten TREK channels. For the seven PIEZO system, the NS model reproduces the anomalous diffusion exponent measured in MD simulations $\alpha \sim 1$, substantially improving on a previously developed variational autoregressive model that exhibits superdiffusive behavior. The model also generates trajectories extending to 40,000 frames and captures the distinct diffusion characteristics of PIEZO and TREK populations in the mixed system. Comparisons of time-averaged mean-square displacements, Van Hove displacement distributions, and non-Gaussian parameters show that the model preserves key statistical features of the simulated dynamics. These results establish neural stochastic modeling as a promising approach for generating long trajectories of multi-protein membrane diffusion and extending the statistical analysis of molecular dynamics simulations.

AI 总结：FaCoDiff是一种用于多变量概率预测的因子相关扩散模型，通过低秩算子等技术避免稠密协方差成本，在9个数据集上的多数指标中表现最优，且验证了变量间依赖关系的预测价值。

链接：https://arxiv.org/abs/2611.06166

机构：School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）； School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）； Graduate School of Information, Production and Systems, Waseda University（早稻田大学信息生产系统研究科）； School of Economics and Management, Dalian University of Technology（大连理工大学经济管理学院）

作者：Jiaxuan Li, Yao Liu, Yuewei Xue, Shijie Dong, Zixiang Jin, Xiaoyu Guo, Qiao Liu

英文摘要： Multivariate probabilistic forecasting requires modeling cross-variable dependence alongside marginal uncertainty. The challenge is to learn this dependence while controlling marginal noise scales and avoiding dense covariance costs. We propose Factor-Correlated Diffusion (FaCoDiff), which couples a compact conditional statistical estimator with structured denoising. History-conditioned factor loadings use distinct coefficients for forecast-center correction, scale adjustment and noise dependence. Diagonal compensation preserves the estimated marginal noise variances, while low-rank operators enable residual Gaussian likelihood evaluation and noise transport without dense covariance storage or factorization. Factor-derived feature modulation explicitly conditions the denoiser, and a geometry-aware objective accounts for errors after noise transport. The same conditional geometry governs forward perturbations and reverse sampling. Across nine datasets, FaCoDiff achieves the lowest mean variogram score on eight datasets and ranks among the top two on 43 of 54 dataset-metric combinations in the reported comparisons. Matched ablations on Solar and Weather show that factor covariance improves mean Energy and Variogram scores under all four combinations of center and scale correction. Disrupting cross-variable sample pairings while preserving marginal trajectories worsens joint and aggregate scores on both datasets, demonstrating the predictive value of generated cross-variable dependence.

AI 总结：本研究复现了BG-CFQS方法，发现其风险保证依赖校准划分、无法服务低预算、静态校准不改进排名、组条件在线重新校准效果不可复现，为低轨卫星链路的风险控制吞吐量预测提供了经验性结论。

链接：https://arxiv.org/abs/2611.06307

机构：School of Computer Science and Engineering, Vellore Institute of Technology（韦洛尔理工学院计算机科学与工程学院）； Nelco Network Products Ltd.（Nelco网络产品有限公司）

作者：Mayan Sharma, Kriti Saini, Devansh Behl, Suganya R, S. Vijayaragavan

英文摘要：Risk-budgeted throughput forecasting converts a point forecast of LEO satellite capacity into a conservative bound whose overestimation rate is held at a user-specified budget, so that admission control can act on it safely. We reproduce the state of the art, BG-CFQS, on the three Starlink traces its authors used and on an independent fourth dataset, and report four findings. First, its risk guarantee is reproducible but conditional on the calibration split: under a random split we recover every published quantity, including risk-pass on 3 of 3 datasets, but under a contiguous temporal split, which is what a deployed terminal faces, it holds on 1 of 3. Second, independent of any split, the method cannot serve a risk budget below 0.15, the low end of its candidate quantile set. On all four datasets the achieved rate is identical at budgets of 0.05, 0.10 and 0.15, overshooting by 1.4x to 3.7x at the tightest. Third, static conformal calibration cannot improve a ranking, only shift an operating point; we confirm this exactly on latency spike detection, where online calibration improves AUPRC by up to 51%. Fourth, group-conditional online recalibration splits into two mechanisms. The online part replicates: it tracks the budget within 1% from 0.05 to 0.35, where static calibration is 16 to 21% off. The group-conditional part does not: on three of four datasets, conditioning on any available covariate, including measured serving-satellite geometry, is worse than not conditioning, and our attempt to predict which datasets benefit failed. What is new here is empirical, not algorithmic: we claim the findings, not the methods.

AI 总结：该研究提出Gfm4Mtsad框架，通过SICA、MAPS、TGEC模块解决预训练GFMs适配MTSAD的问题，在两种GFMs主干上实现了优于基线的多变量时间序列异常检测性能。

链接：https://arxiv.org/abs/2611.06441

机构：College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）； University of Exeter（埃克塞特大学）； City University of Hong Kong（香港城市大学）； The American University in Cairo（开罗美国大学）； University of Bologna（博洛尼亚大学）

作者：Zhuangyan Hu, Zhuoxuan Liang, Wei Li, Xiaohua Jia, Moustafa Youssef, Sumi Helal

英文摘要：Multivariate time series anomaly detection (MTSAD) relies on modeling both temporal dynamics and inter-sensor dependencies to identify complex system faults. Although graph-based detectors explicitly model sensor topologies, they train task-specific networks from scratch on limited target-domain data, remaining vulnerable to spurious correlations and unable to reuse transferable priors. While pretrained Graph Foundation Models (GFMs) offer transferable topological priors and anomaly discriminability, directly adapting them to MTSAD is hindered by cross-modal feature-topology mismatch, unreliable pseudo-anomaly supervision, and decision distortion during node-to-window aggregation. To fill these gaps, we propose a novel adaptation framework, Gfm4Mtsad, which unleashes pretrained GFMs for MTSAD. We introduce the Structure-and-Information Contract Alignment (SICA) module to bridge continuous time series and the pretrained GFM space. This module harmonizes principal subspace token projection, normal-prior-guided graph construction, and residual recovery to suppress spurious edges while retaining fine-grained temporal details. We propose a Mechanism-Aligned Pseudo-Anomaly Screening strategy (MAPS) to steer unsupervised learning. It applies localized dependency breaks while preserving empirical residuals, selecting hard negatives near the normal boundary for targeted supervision. We develop a Tail-Gated Evidence Calibration mechanism (TGEC) to eliminate node-to-window aggregation distortion. It calibrates multi-granular graph responses against normal operational references, strictly preserving benign score rankings while selectively refining extreme anomaly candidates in the tail distribution for reliable window detection and interpretable sensor evidence. Extensive evaluations with two GFM backbones demonstrate that Gfm4Mtsad achieves superior detection performance over existing baselines.

AI 总结：本研究针对长期时间序列预测的固定激活配置局限，提出校准激活选择策略，经1225次Informer训练评估，平均降低7.86%均方误差，提升72.55%任务性能。

链接：https://arxiv.org/abs/2611.06559

机构：School of Automation, Nanjing University of Information Science and Technology（南京信息工程大学自动化学院）； School of Engineering, Hong Kong University of Science and Technology（香港科技大学工程学院）； Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong（香港中文大学深圳大数据研究院）； Shenzhen Loop Area Institute（深圳河套学院）

作者：Shijie Xu, Boyan Tang, Jianghua Wu

英文摘要： Long-term time-series forecasting supports decisions in energy, transportation, and other applications where reliable predictions are needed beyond the observed data. Although forecasting research has advanced model architectures, fixed activation configurations provide limited guidance on how activation choices should vary across datasets and prediction horizons. This study aims to establish when task-specific activation choices improve forecasting accuracy and how previous experiments can guide the selection of suitable configurations for new tasks. We develop a calibrated selection policy that learns a reliable default from prior experiments, adapts the activation function and its location to each task, and retains the default when evidence for a change is insufficient. In an evaluation supported by 1,225 Informer training runs across 17 dataset variants and 51 forecasting tasks, the policy reduces mean squared error by 7.86% on average relative to the conventional fixed-activation baseline and improves 72.55% of tasks. These findings establish activation choice and placement as practical opportunities for improving existing forecasting models and show how accumulated experimental evidence can guide configuration decisions without repeating an exhaustive search for every task.

AI 总结：针对协同过滤的浅层自编码器EASE无法充分捕捉用户关系的问题，本文提出利用用户信息的替代方案及混合模型，在推荐基准上性能优于EASE。

链接：https://arxiv.org/abs/2611.06610

作者：Mario Mallea, Angela Nebot, Francisco Mugica

英文摘要：Collaborative filtering (CF) is a cornerstone of recommender systems and plays a relevant role in many modern applications. CF uses user-item interaction data to discover future preferences. Although deep learning models have shown promise in CF, Embarrassingly Shallow Autoencoders for Sparse Data (EASE) has gained attention for its outstanding ranking accuracy provided by its closed-form solution. EASE relies primarily on relationships among items to fit a full-rank high-dimensional linear mapping. We hypothesize that this design limits its capacity to capture similar but not equivalent fine-grained user relationships, consequently limiting its recommendation accuracy. This paper introduces an alternative formulation based on EASE that takes advantage of user-user information. Furthermore, we propose a hybrid model that combines both user-user and item-item adjacency distributions. Our experiments reveal that the proposed models outperform EASE on well-known recommendation benchmarks, highlighting the significance of including the user alternative in shallow autoencoder studies for CF.

AI 总结：本文提出上下文-答案元模型，可在首个答案标记生成前，利用上下文激活值预测大语言模型的答案激活值，该模型泛化性好且能跨模型迁移。

链接：https://arxiv.org/abs/2611.06837

机构：Tufa Labs（图法实验室）； Mila, Université de Montréal（米拉研究所，蒙特利尔大学）

作者：Tommy He, Thomas Jiralerspong

英文摘要：Under stochastic decoding, a single context can elicit many different answers from a language model. Characterizing this variation typically requires many expensive autoregressive rollouts, each revealing only one realization. We ask whether the activations produced while reading the context can directly forecast the model's rollout activations. We train context--answer metamodels that map a context activation sequence to point or distribution forecasts of mean-pooled answer activations. We find that (i) the four metamodel families we study forecast mean-pooled answer activations with high fidelity, and the distribution metamodels also capture rollout variability; (ii) performance improves as the training set grows to 500,000 prompts, and the metamodels generalize zero-shot to out-of-domain datasets; (iii) their forecasts retain behavior-predictive signal; and (iv) context activations from Qwen2.5 predict mean-pooled answer activations from Qwen3.5 and Gemma 4, suggesting that activation forecasts can transfer across models. Together, these results suggest that context activation traces can support scalable forecasts of behaviors in LLM answers before the first answer token is generated.

AI 总结：该研究针对多目标对齐下主动偏好学习的高标注成本问题，提出OGC-DPO框架，将（响应对、目标）组合作为查询，通过策略隐式梯度选查询，实验证明其优于现有方法，且信息性冲突可作为训练信号。

链接：https://arxiv.org/abs/2611.05506

机构：Nanyang Technological University（南洋理工大学）； National University of Singapore（新加坡国立大学）； VinUniversity

作者：Duc Anh Vu, Thong Nguyen, Hoang Pham, Ponhvoan Srey, Luu Anh Tuan

英文摘要：Aligning large language models (LLMs) with several objectives, such as helpfulness, harmlessness, and honesty, typically relies on preference judgment for each objective, growing the annotation cost linearly with the number of objectives. Active preference learning can reduce this cost by selecting the comparisons whose labels are expected to change the policy most. Existing remedies either lack an objective-aware query selection mechanism for the multi-objective setting or regard conflicting preferences as harmful, therefore excluding them from the training data. We introduce OGC-DPO (Objective-conditioned Gradient Coverage DPO), an active learning framework that treats each (response pair, objective) combination as a separate query and measures its informativeness through the policy's own implicit gradient, without relying on an external reward model. OGC-DPO then selects the queries that most expand each objective's gradient coverage. Empirical experiments across varying numbers of objectives demonstrate the effectiveness of OGC-DPO over existing active preference learning methods under the same annotation cost. Further analysis demonstrates that informative conflicts, rather than being uniformly harmful, provide a useful policy training signal.

AI 总结：本研究提出CoCo框架，通过LLM与TSFM的分阶段对齐，利用合成多模态数据集，在多领域基准上较现有模型实现最高12% MAE提升。

链接：https://arxiv.org/abs/2611.05515

机构：The University of Texas at Austin（德克萨斯大学奥斯汀分校）

作者：Hemanth Ram, Sai Shankar Narasimhan, Shubhankar Agarwal, Sandeep Chinchali

英文摘要： Textual information is ubiquitous in real-world time series datasets, appearing as news, reports, and annotations that hold key predictive signals not deducible from numerical history alone. Large Language Models (LLMs) interpret text but lack numerical precision, while Time Series Foundation Models (TSFMs) are quantitatively accurate but cannot reason about the effects of text. Moreover, the lack of large-scale paired multimodal datasets limits progress in bridging this gap. In this work, we address this challenge by providing a scalable approach for generating realistic multimodal datasets by annotating real-world time series with LLMs. Building on this, we propose Context as Covariates (CoCo), a forecasting framework in which an LLM distills its textual reasoning into numerically grounded forecast and confidence covariates that guide a TSFM backbone, thereby exploiting the strengths of both LLMs and TSFMs. We align the two models in stages, first fine-tuning the LLM via Group Relative Policy Optimization (GRPO) with a reward tied to covariate-induced improvement in forecasting performance, and then fine-tuning the TSFM on covariates generated by the aligned LLM. Our experiments show that models trained on our synthetic multimodal corpus generalize to real benchmarks spanning finance, economics, weather, traffic, and security. Notably, CoCo with Qwen3-4B-Instruct-2507 as the LLM and Chronos2 as the TSFM achieves up to 12% MAE improvement over state-of-the-art TSFMs and LLM forecasters, such as GPT-4o. Code, checkpoints, and the evaluation datasets are publicly available.

AI 总结：本研究提出新核度量置信度分数，其与分类准确率的相关性优于部分现有核度量，可用于核选择与超参数调整。

链接：https://arxiv.org/abs/2611.05528

机构：McMaster University（麦克马斯特大学）

作者：Elizabeth Hofer, Martin v. Mohrenschildt

英文摘要：Across a wide range of applications, kernel measures are used to tune hyperparameters, select among candidate kernels, and assess the suitability of kernels for specific learning tasks. For classification, kernel measures are used to predict which kernels may produce accurate kernel classifiers. Many existing kernel measures, such as alignment to the ideal kernel, are designed to reward kernels which captures all samples from the same class as similar. While effective in many settings, this requirement is unnecessarily restrictive for classification, where classes may exhibit substantial internal structure. In this work, the "Confidence Score", a new kernel measure, is proposed. Unlike alignment-to-the-ideal kernel measures, the Confidence Score measures the alignment between an encoding of the ground truth and that ground truth projected by the kernel, allowing kernels that preserve intra-class structure to achieve high scores. Through experiments, it is demonstrated that the Confidence Score exhibits a higher correlation with classification accuracy across a variety of kernels on both real-world and synthetic datasets, compared to select other kernel measures.

AI 总结：本文证明支持向量分类与回归的对偶目标是两个高斯测度间受约束的KL散度最小化任务，去除域约束并应用L²正则化时该公式可恢复核岭回归目标，为核机器提供统一信息论模型并奠定相关算法统一的基础。

链接：https://arxiv.org/abs/2611.05557

作者：Ivan Silajev

英文摘要：Support vector machines (SVMs) are fundamentally grounded in geometric margin maximisation and currently lack a mainstream probabilistic interpretation. This keeps them theoretically isolated from probabilistic machine learning models that are naturally comparable and combinable. In this paper, we bridge this gap by proving that the dual objectives of both support vector classification and support vector regression are exact, constrained Kullback-Leibler (KL) divergence minimisation tasks between two Gaussian measures. We further show that this same divergence formulation recovers the exact objective function for kernel ridge regression when domain constraints are removed and $L^2$ regularisation is applied. By demonstrating that SVM duals directly minimise relative entropy, our results provide a unified information-theoretic model for standard kernel machines and lay the foundation for unifying broader kernel-based algorithms under probability theory.

AI 总结：该研究通过三项预注册测试发现，自监督世界模型的流体隐空间表征中，物理量组织与可操控性相分离，普通自编码器可突出涡量，路由法能构建部分可操控旋钮，相关数据与代码公开。

链接：https://arxiv.org/abs/2611.05577

机构：Modint Intelligence（莫丁特智能）

作者：Manvendra Modgil

英文摘要：Self-supervised world models are often said to organize their latents around physical quantities. We test this on 2D decaying Navier-Stokes flow in three pre-registered studies, each tested once on held-out data. In Study 1, a joint-embedding predictive architecture (JEPA) trained to predict the flow fails the pre-registered criteria for organizing its latent around fluid quantities better than an untrained network of the same architecture (not supported); a JEPA trained on non-physical linear dynamics and a static invariance model that never sees time do as well or better. In Study 2, plain autoencoders that render the field make enstrophy prominent in their slowest unsupervised latent directions (best r 0.80-0.86 vs 0.38 untrained; fresh seeds, sealed data). Adding VICReg lowers this prominence in every comparison, though not by the pre-registered margin for vorticity, while raising the autoencoders' linear decodability (partially supported). In Study 3, we build steerable directions ("dials") by routing: the decoder gets overall scale only through a small conditioning code, while its other pathway carries a degraded copy of spectral shape. Label-free rules find a loudness dial in all 8 trained models and a shape dial in 3 of 5 fresh seeds on sealed data, against a pre-registered bar of 4; neither control shows a shape dial (partially supported). Across the studies, what a latent contains and what can be controlled through it come apart. In exploration, a supervised direction reading spectral shape almost perfectly moved nothing when steered. During the build, the loudness dial had to be found by its effect on the output (closest to a pure rescaling), not by data variance; for shape, a variance rule and an effect-constrained rule each worked in 3 of 5 seeds. The program used about 70 logged hours on one consumer GPU; all pre-registrations, deviations, code and outputs are public.

AI 总结： 该研究针对GRPO固定预算的低效问题，提出C-GRPO共形校准方法，在多基准测试中大幅减少训练rollout并保持准确率，实现最优pass@1且可跨模型迁移。

链接：https://arxiv.org/abs/2611.05591

机构：University of Southern California（南加州大学）

作者：Arya Fayyazi, Seyedarmin Azizi, Mehdi Kamal, Massoud Pedram

英文摘要：Reasoning-oriented language model training increasingly relies on multiple on-policy rollouts, but standard Group Relative Policy Optimization (GRPO) uses a fixed group size, wasting compute on easy prompts and under-exploring hard ones. Existing adaptive-budget methods alleviate this inefficiency, yet their stopping rules are heuristic and do not provide a finite-sample calibration guarantee. We propose CGRPO, which replaces the fixed rollout budget of standard GPRO with calibrated conformal thresholds over task-specific nonconformity scores, combining Adaptive Prediction Set (APS)-style calibration for closed-form reasoning, a first-success score for execution-based code, automatic miscoverage selection, and periodic re-calibration. We prove finite-sample coverage for the resulting stopping rule and show that, under monotone policy improvement, recalibration induces a non-increasing expected training budget. Across six benchmarks spanning mathematics, graduate-level science, and code generation, CGRPO reduces mean training rollouts by 49-70% relative to fixed-budget GRPO while retaining strong fixed-budget accuracy, achieves the strongest pass@1 among evaluated static and dynamic baselines on different datasets, and transfers across model families with up to 82% rollout savings.

AI 总结：该研究针对多模态采样传输学习的模式崩溃问题，提出分量退火传输（CAT）方法，通过构造混合分解与自适应退火调度，在合成分布和贝叶斯推理实验中提升了传输学习与采样效率。

链接：https://arxiv.org/abs/2611.05605

机构：Duke University（杜克大学）

作者：Han Chen, Sifan Liu

英文摘要：Variational approaches to learning transport maps for multimodal sampling can suffer from mode collapse. One way to mitigate this problem is to learn transport along a prescribed path of intermediate distributions defined by geometric annealing. We show that annealing can take a substantial detour in Wasserstein space: when mass moves between modes, the path can be much longer than the distance between its endpoints. We further link path geometry to transport learning, showing that long annealing paths can slow population optimization, while sufficiently regular paths permit rapid convergence. Motivated by these findings, we propose componentwise annealed transport (CAT) to reduce the need for mass transfer between modes. Starting from a mode-informed mixture reference, CAT constructs an exact mixture decomposition of the target and learns separate transport maps along componentwise annealing paths. We estimate the component weights separately and show that these estimates remain consistent even when the learned flows are inexact. We also develop adaptive annealing schedules guided by transport effort. Experiments on multimodal synthetic distributions and Bayesian inference problems demonstrate improvements in transport learning and sampling efficiency.

AI 总结：该研究提出 LACS 标签无关置信度选择器，用于动态数据剪枝，可减少训练计算量，在多类任务中提升效率，部分场景下准确率接近基准且耗时降低。

链接：https://arxiv.org/abs/2611.05616

机构：Yonsei University（延世大学）

作者：Heejin Choi

英文摘要：Training on large datasets motivates selecting which examples receive expensive training computation. Fixed quotas determine the amount of pruning in advance, while absolute confidence cutoffs depend on the score scale. We study a population-relative alternative that adapts the threshold and retained count together. We introduce LACS (Label-Agnostic Confidence Selector), a training-time rule based on maximum predictive confidence, requiring neither labels at selection time nor per-example gradients. LACS removes examples at or above a log-mean-exp reference plus a margin. We characterize its sensitivity to score gaps and mass, and derive an exact no-pruning condition. For $0

AI 总结：研究混合整数规划中约束族的分组问题，提出JointDW方法结合语言模型预测与分阶段搜索，发现语言模型在特定场景下识别约束组的准确率与前瞻相当，推理可提升模型准确率。

链接：https://arxiv.org/abs/2611.05726

机构：Institute of Operations Research and Analytics, National University of Singapore（新加坡国立大学运营研究与分析学院）； Wenzhou Buyi Pharmacy（温州布衣药房）； McCormick School of Engineering, Northwestern University（西北大学麦考密克工程学院）； College of Computer Science and Artificial Intelligence, Wenzhou University（温州大学计算机与人工智能学院）； City University of Hong Kong（香港城市大学）

作者：Junbo Jacob Lian, Kaixuan Chen, Hanzhang Qin, Huiling Chen, Chaoyu Zhang, Chung-Piaw Teo

英文摘要： Dantzig-Wolfe reformulation strengthens the linear relaxation of a mixed-integer program by convexifying a chosen set of constraint families, and every candidate costs a column generation run. The choice is hard when families strengthen the bound only together, because a search that adds one family at a time then stops at the linear relaxation. The classical inequalities of lot sizing, network design and facility location rest on such zero-marginal groups. We prove that when each evaluation convexifies at most $s$ of $k$ families, any method that observes only bounds needs a covering number of evaluations, of order $k^w$, to find a hidden group of $w$ families, also on actual Dantzig-Wolfe relaxations. A prediction that misses $e$ members reduces the cost to $\Theta(k^e)$, through one staged search that does not need to know $e$, so the evaluations a prediction saves measure what the predictor knows. We study two sources of predictions. A one-pass lookahead at an optimal LP dual is provably blind to groups whose families share no variable, and a language model reads the names and text of the model. JointDW verifies the prediction of the language model with the staged search and interleaves it with the lookahead at a bounded loss. Groups that strengthen the bound only jointly appear in a quarter of the MIPLIB-NL models whose bound can be improved. There the strongest language models identify them about as often as the lookahead, their predictions rest on the names for textbook models, and reasoning about doubles the accuracy of the same model. On larger models with distracting families, the lookahead finds the group in one evaluation of three families. Language models given only the text seldom name it, while one reasoning model given every coefficient names it in all but one answer and then needs fewer evaluations than the lookahead for pairs of families.

AI 总结：本研究提出FRESHPart框架，通过几何与搜索反馈优化谱嵌入，在Titan23基准上较hMETIS取得更优割减少效果，且单GPU运行耗时短。

链接：https://arxiv.org/abs/2611.05729

机构：NVIDIA（英伟达）； Stevens Inst of Tech（史蒂文斯理工学院）

作者：Rongjian Liang, Zhuo Feng, Brucek Khailany

英文摘要：With scalable Laplacian solvers now available, the next frontier is to exploit them more effectively for high-quality hypergraph partitioning. In this work, we revisit spectral hypergraph partitioning from the perspective of spectral embedding geometry. Through a systematic analysis of circuit hypergraphs and controlled synthetic (hyper)graphs, we identify a ray-like embedding structure that helps explain the effectiveness of projection-based partitioning. We further show that the geometry of spectral embeddings can be systematically influenced through Laplacian design and hyperedge reweighting. Guided by these insights, we propose FRESHPart, a feedback-reshaped spectral partitioning framework that refines the spectral embedding through Geometry Feedback and Search Feedback, producing embeddings that are better aligned with projection-based partitioning. On the Titan23 benchmark, FRESHPart achieves average cut reductions over hMETIS of 13.09% at $\epsilon=2\%$ and 25.21% at $\epsilon=20\%$, surpassing the strongest previously reported results (12.16% and 22.53%, respectively), while completing every benchmark in under 120 s on a single GPU.

AI 总结：针对预测多样性下不同模型预测分歧导致的追索权失效问题，提出ORPM算法，在多类数据集上验证其优化与有效性表现，同时分析共同行动要求对追索权及群体差异的影响。

链接：https://arxiv.org/abs/2611.05774

机构：Drexel University（德雷塞尔大学）

作者：Shahin Jabbari

英文摘要：Algorithmic recourse identifies modifications that can change an unfavorable model prediction into a favorable one. Under predictive multiplicity, similarly performing models can disagree on individual predictions, so recourse valid for one model may fail for another. We formulate robust recourse under predictive multiplicity as a budget-constrained minimization of the worst-case validity loss across a competing set of models, which can include models with different architectures or from different hypothesis classes. We propose an oracle-efficient algorithm called ORPM and prove sufficient conditions for its convergence. We evaluate ORPM on one synthetic and five real datasets, and competing sets consist of linear, neural, mixed, and decision-tree models. Our experiments show decreasing optimization-gap bounds and high validity in many tested settings, with favorable performance against baselines designed for specific model classes or architectures. We also quantify how requiring a common action across competing models changes recourse success at a given budget and examine disparities in this success across demographic groups.

AI 总结：本研究建立了带正则化修正与资格迹的梯度时间差分学习的收敛性，提出TDRC(λ)-Huber算法，推导了收敛条件与共享的正则化收敛机制。

链接：https://arxiv.org/abs/2611.05869

机构：University of Alberta（阿尔伯塔大学）； Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所）

作者：Jingyi Jin, Martha White, Hao Wang

英文摘要：Temporal-difference (TD) learning underlies many prediction and control algorithms, but can diverge when combined with off-policy learning, bootstrapping, and function approximation. TD with Regularized Corrections (TDRC) is theoretically sound and practically simple, but existing convergence theory covers only the one-step case under i.i.d. sampling, while the Huber Bellman-error formulation has not been extended to TDRC or eligibility traces. In this work, we establish convergence of constrained TDRC($\lambda$) with diminishing stepsizes and non-i.i.d. off-policy data, for state-dependent and history-dependent $\lambda$-parameters. We also propose TDRC($\lambda$)-Huber, which preserves the TDRC($\lambda$) fixed point while reducing sensitivity to large Bellman errors. For both algorithms, we apply an ODE-based weak-convergence method from stochastic approximation theory. For the nonlinear mean dynamics of TDRC($\lambda$)-Huber, we give an integral small-gain condition that guarantees uniqueness and global asymptotic stability of the target equilibrium. We then establish convergence of the constrained algorithm, derive computable sufficient conditions for convergence, and identify a regularization-based convergence mechanism shared by linear TDRC($\lambda$) and TDRC($\lambda$)-Huber. When the mean TD matrix is positive stable, both constrained algorithms converge under sufficiently strong regularization without requiring positive definiteness.

AI 总结：该研究针对自动调制分类，提出CPU高效的FuseAMC决策融合模型，结合CNN与累积量分类器的互补性，在RadioML 2016.10a数据集上提升了分类准确率，适配无硬件加速的处理器。

链接：https://arxiv.org/abs/2611.05937

机构：Rice University（莱斯大学）； Duke University（杜克大学）

作者：Zhiyi Yin, Zhizhao Wen, Ruoxin Zhang, Chao Wang, Puyang Xu, Yifan Jiang, Yusi Gong

英文摘要： Automatic modulation classification (AMC) underpins spectrum sensing and non-cooperative signal identification in cognitive radio, yet the two standard CPU-feasible approaches to it, a compact convolutional neural network (CNN) on raw in-phase and quadrature (I/Q) samples and a classical higher-order-cumulant feature classifier, are rarely compared against each other conditioned on signal-to-noise ratio (SNR). We show that they are, in fact, complementary: the CNN is markedly more robust at low SNR, where hand-crafted statistics collapse toward chance, while the cumulant classifier is more accurate at high SNR, where higher-order statistics cleanly separate dense constellations. We exploit this complementarity with FuseAMC, a lightweight decision-fusion model that trains a logistic-regression meta-combiner over the concatenated class-posteriors of an approximately 198k-parameter residual CNN and a gradient-boosted-tree cumulant classifier, on a validation split disjoint from test. On RadioML 2016.10a (5 seeds, 44k-frame held-out test set), FuseAMC reaches 55.8% overall accuracy, improving on the best cumulant-feature baseline by 5.1 points and on the CNN alone by 3.2 points (paired-bootstrap 95% confidence-interval lower bounds of 5.0 and 3.1 points, both positive), while matching the best classical baseline in its own strong high-SNR regime and the CNN in its strong low-SNR regime. With about 0.2 million parameters, a model footprint of a few megabytes, and roughly 3 MFLOPs per frame, the whole pipeline trains and runs on one CPU core, targeting general-purpose and embedded processors without hardware acceleration.

AI 总结：BAM是一类带成对交互作用的广义加性模型，将成对形状函数约束为简单参数形式，在六个表格基准上达到与标准GAM相当的预测准确率，同时提升了可解释性。

链接：https://arxiv.org/abs/2611.05940

机构：Tohoku University（东北大学）

作者：Atsushi Koike

英文摘要：Generalized Additive Models with pairwise interactions (GA$^2$Ms) are regarded as glass-box models, since each pairwise shape function can be visualized as a contour plot. In practice, the shape functions learned by methods such as EBM or NODE-GAM are so complex (irregular contours and sharp local fluctuations) that a practitioner cannot meaningfully interpret them or check them against domain knowledge. Auditability in this stronger sense requires substantially simpler shape functions. We argue that such simplification can match the accuracy of standard GAMs if the right feature pairs are captured. We propose Bivariate Additive Models (BAMs), a class of GA$^2$Ms in which every pairwise shape function is constrained to a simple parametric form, instantiated as an asymmetric bivariate Gaussian. Each component is visualizable as a unimodal contour map, and its feature pair is selected automatically during training. Experiments on six tabular benchmarks show that BAM attains predictive accuracy comparable to standard GAMs and approaches that of GA$^2$Ms. We also show the trade-off between accuracy and the number of components to inspect.

AI 总结：该研究将模型崩溃解读为罗杰斯悖论，通过三步验证，发现依赖模型复制会导致知识丢失，需引入外部信息或用户自主学习打破悖论。

链接：https://arxiv.org/abs/2611.06022

机构：University of Vermont（佛蒙特大学）； Vermont Complex Systems Institute（佛蒙特复杂系统研究所）

作者：Nam H. Le

英文摘要：Generative models trained on the output of earlier models lose knowledge across generations, rare knowledge first. The remedies that work all bring in information from outside the loop, and the theory of model collapse treats that supply as given: a fixed archive, drawn from a fixed distribution. We argue that the supply is the problem, and that cultural evolution has the theory of it. We read model collapse as Rogers' paradox: a population that comes to rely on copying ends up no better off than one that never copied, because copies go stale unless someone keeps learning for themselves. We test the identification in three steps. First, Rogers' model, fitted only to fully synthetic training, predicts to within a few percentage points how much rare knowledge language models, from word trigrams to a pretrained transformer, retain under other mixtures of fresh data. Second, when the world itself changes, a lineage that copies its own past needs more fresh data to keep up the faster the world changes, as Rogers' model says it must and as analyses that assume a fixed target cannot; a lineage that restarts every generation from fixed pretrained weights is largely insulated, because those weights are knowledge that is never copied. Third, we close the loop: when the people who write a model's training data can ask the model instead of finding things out, the supply of fresh data becomes endogenous. At equilibrium, the model is exactly as reliable as it is costly for people to find out for themselves, the population is no better off than without the model, and every increase in the share of model-written text must be paid for by more people learning for themselves. Users who check what they are told and relearn when it is wrong break the paradox. Filters that select plausible text are conformist and accelerate collapse; only checks that carry new information act as critical learning.

AI 总结：针对交易网络反洗钱的聚合不匹配问题，提出TRACK多实例学习方法，在5个AML数据集上仅用账户窗口监督，实现优于基线的检测性能，且实例排名表现良好。

链接：https://arxiv.org/abs/2611.06035

机构：KFTC（韩国金融监督院）； UNIST（蔚山国家科学技术研究所）； KAIST（韩国科学技术院）； LG AI Research（LG人工智能研究院）； Sogang University（西江大学）； LinqAlpha（林克阿尔法）

作者：Seonkyu Lim, Inwoo Tae, Gwangui Hong, Jaehoon Lee, Jeongwhan Choi, Taehoon Kim, Min-Soo Kim, Yongjae Lee

英文摘要：Anti-money laundering (AML) investigations are organized around accounts, while the suspicious evidence for an account may reside in only a few of its transactions. Yet many account-level detectors summarize transactions before learning, and message-passing models may aggregate once more over neighboring accounts. We name this discrepancy the aggregation mismatch and study it under an account-window benchmark label defined by the presence of at least one flagged transaction. We recast account-level AML detection as multiple instance learning, in which an account-window is a bag and its transactions are instances. We propose TRACK, which encodes every transaction with a shared encoder and selectively pools the encodings into an account decision. To emulate coarse supervision, we withhold the transaction labels of five AML datasets from TRACK throughout training. Under a time-ordered protocol, TRACK achieves the highest average precision among the tabular and graph baselines trained from account labels on every dataset. Despite using only account-window supervision, it remains competitive with edge classifiers trained directly from transaction labels. Its instance scores also rank flagged transactions well above chance and approach or exceed directly supervised ranking performance across datasets.

AI 总结： 提出Act-HiMotif分层基序生成模型，通过学习可复用活动结构基序，在出行数据上优于基线，可生成符合个体特征的真实每日活动日程。

链接：https://arxiv.org/abs/2611.06038

机构：Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）； School of Computer Science and Engineering, Macau University of Science and Technology（澳门科技大学计算机科学与工程学院）； Shenzhen University of Advanced Technology（深圳理工大学）； University of Chinese Academy of Sciences（中国科学院大学）； City University of Hong Kong（香港城市大学）； Beijing Normal-Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）

作者：Yushi Bai, Kang Liu, Ling Yin, Wei Wang, Yilin Lyu

英文摘要：Generating daily activity schedules from individual attributes (e.g., age and income) is valuable for activity-based travel modeling and human behavior simulation. The task is challenging because exact activity structures follow a highly imbalanced, long-tailed distribution, while the same structure can admit multiple duration allocations under a fixed full-day budget. Direct generation must therefore learn many exact activity structures from highly uneven observations while modeling their possible temporal realizations. However, many exact structures share recurring activity organization. Based on this observation, we propose Act-HiMotif, a hierarchical motif-based generative model that introduces learned, reusable activity-structure motifs as an intermediate representation between individual profiles and specific activity structures. Act-HiMotif first distinguishes home-only days from active days, then samples a motif from a profile-conditioned prior to guide activity-structure generation, and finally applies a budget-constrained duration allocator to generate activity durations that sum exactly to the full-day horizon. This hierarchy models home-only routing, activity-structure generation, and duration allocation at their corresponding levels, while motifs allow related structures to share statistical support without losing their distinct sequences. Experiments on large-scale travel survey data show that Act-HiMotif outperforms strong baselines in both in-distribution and spatial generalization settings, demonstrating the effectiveness of hierarchical generation and reusable activity-structure motifs for producing realistic profile-conditioned daily activity schedules.

AI 总结：提出SelPI模型，联合训练区间、值与弃权头，通过双门控结合Fréchet惩罚，在18个表格基准上优于宽度阈值及深度集成分歧选择方法，缩小与最优选择器的差距。

链接：https://arxiv.org/abs/2611.06158

机构：Ben-Gurion University of the Negev（内盖夫本-古里安大学）； Faculty of Computer and Information Science（计算机与信息科学学院）

作者：Lidor Rubi, Gilad Katz

英文摘要：Many high-stakes regression decisions ask three things of a model at once: a point estimate to act on, an interval that bounds the risk of acting, and the option to abstain when neither can be trusted. Deep learning provides these in isolation: prediction-interval methods return an interval, and at best a value estimate inside it, for every input, while selective-prediction methods learn when to abstain, but only for point predictions and with no statement of how far the truth may lie from the estimate. We introduce SelPI, which trains interval, value and abstention heads jointly under a single loss with an explicit acceptance-rate budget. A natural objection is that interval width already tells us when to abstain. We show that it does not: across 18 tabular benchmarks, width predicts large point errors well (AUROC 0.72) but is anti-correlated with interval misses (AUROC 0.38), so abstaining on wide intervals discards precisely the inputs the interval got right. SelPI therefore selects with two gates, one trained on the interval loss and one on the value loss, coupled by a Fr'echet penalty that ties their separate budgets to the joint acceptance rate they deliver. With the base predictor held fixed, the learned gate beats width thresholding on a proper interval score on all 18 tabular datasets, closing a median 87% of the gap to an oracle selector, and it also beats selection by deep-ensemble disagreement on every dataset. The advantage grows with the heteroscedasticity of the task, and we report the settings in which it disappears.

AI 总结：针对降维技术计算成本高、难以用于交互式场景的问题，提出GPU加速的GPUMAP算法，通过重构UMAP操作、引入修改及利用GPU并行性实现百万级数据集的交互式分析，并研究了渐进式降维的相关特性。

链接：https://arxiv.org/abs/2611.06191

机构：Sungkyunkwan University（成均馆大学）

作者：Myeongwon Jung, Jaemin Jo

英文摘要：Dimensionality reduction (DR) techniques are fundamental to the analysis of high-dimensional data, yet their substantial computational cost limits their use in interactive settings. To address this limitation, we present GPU-accelerated Progressive Uniform Manifold Approximation and Projection (GPUMAP), an efficient progressive DR algorithm designed for interactive use. We identify three atomic operations within UMAP and restructure them into a progressive pipeline. As part of this adaptation, we introduce modifications to UMAP, including operation queues, instance-wise local join, and scheduling strategies. We further accelerate computation by leveraging GPU parallelism, enabling interactive analysis of million-scale datasets on commodity GPU hardware. Finally, we employ our implementation to systematically study the behavior of progressive DR, investigating how projections evolve over time relative to the ground-truth projection and how biased data streams influence the quality and stability of the resulting projections.

AI 总结：该研究针对ICA的联合对角化问题，证明高斯源下分离成分的代价为里斯2能量，得出不同环境数对应的样本速率规律，且模拟与fMRI数据验证了结论。

链接：https://arxiv.org/abs/2611.06228

机构：Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（跨机构神经成像与数据科学转化研究中心（TReNDS））； Georgia State University（佐治亚州立大学）； Georgia Institute of Technology（佐治亚理工学院）； Emory University（埃默里大学）； Huazhong University of Science and Technology（华中科技大学）； University of Pennsylvania（宾夕法尼亚大学）； Lancaster University（兰卡斯特大学）

作者：Yuda Bi, Yubo Guo, Jiaxiao Cai, Xiaonai Li, Zhuolin Li, Vince D. Calhoun

英文摘要： Can independent components be recovered when all of them are Gaussian? Yes, if their variances change across environments; but how many environments, and how many samples as the number of components $d$ grows? We answer both for the joint diagonalization of $K$ covariances. Each component has a signature, its variances across the environments. Separating component $j$ costs the Riesz 2-energy $E_j$ of this signature, a sum, over the other components, of inverse pairwise Fisher informations. For Gaussian sources no estimator in $O(d)$ beats $cE_j/(n+E_j)$ at any $n$ and $d$, and above an explicit sample size a spectral estimator attains $E_j/n$ up to a constant, for every mixing and without the signatures (given a lower bound on the pairwise information). Since signatures live in $K-1$ dimensions, the rate obeys a dimension law: $d^2/n$, $d\log d/n$ and $d/n$ for $K=2$, $3$ and $K\ge4$ well-spread environments. What counts is the dimension the signatures span, not their number, and $\log d$ well-designed environments make $d\log d$ samples suffice. Projecting the covariances onto one linear combination pays $d^2/n$ at first order instead. In simulations up to $d=1000$ the predictions hold and joint diagonalization with $K\ge4$ attains the rate once $n/d$ is in the hundreds; on task fMRI the energy says which components are hard to separate.

AI 总结：该研究针对少步生成模型的奖励对齐问题，提出LeNO方法将奖励引导的初始噪声优化摊销为学习到的残差传输，在文本到图像和蛋白质结构生成任务中提升目标奖励并保持生成质量。

链接：https://arxiv.org/abs/2611.06255

机构：KAIST AI（韩国科学技术院人工智能学院）

作者：Hongeun Kim, Jeongsol Kim, Jong Chul Ye

英文摘要：Reward alignment for diffusion- and flow-based generative models is often formulated over stochastic trajectories, making it less suitable for few-step deterministic generators. Initial noise optimization provides a trajectory-free alternative, but existing gradient-free methods require costly per-sample search at inference. We introduce LeNO (Learning Noise Optimization), which amortizes reward-guided initial noise optimization into a learned residual transport while keeping the pretrained generator frozen. LeNO learns an anchor-conditioned distribution over noise corrections using Local GRPO and scalar rewards, eliminating iterative test-time optimization. For Gaussian initial noise with norm-preserving projection, we derive a tractable upper bound on the KL divergence between the corrected noise distribution and the original prior, providing a principled regularization objective. We evaluate LeNO on few-step text-to-image and protein structure generation, including expensive black-box designability rewards. Across both domains, LeNO improves target rewards with a single noise correction at inference while largely preserving generation quality and diversity.

AI 总结：该研究发现加密流量分类的性能高度依赖评估拆分协议，服务器不相交拆分会显著降低宏F1，提出单标识符诊断法，并验证了少样本注册可恢复跨语料库的零样本迁移缺陷。

链接：https://arxiv.org/abs/2611.06290

作者：Mayan Sharma, Kriti Saini, Devansh Behl

英文摘要：Encrypted traffic classifiers are routinely reported at 95-99% accuracy. We show that on one of two public corpora, the same model and code score macro-F1 0.992 under the evaluation protocol standard in the literature and 0.567 under a protocol that keeps flows from one packet capture on one side of the split. Nothing changes but the partition (gap 0.426 over eight seeds, p = 0.008 after Holm-Bonferroni correction). The inflation is not a property of random splitting as such. On a month of backbone QUIC traffic, we evaluate five split protocols at eight seeds each: random, temporal and client-disjoint splitting are statistically indistinguishable from session-disjoint, while server-disjoint splitting costs 0.192 macro-F1 (d = 17.0, p = 0.008). Which axis matters depends on how the corpus was collected, in a way invisible from its label space. We give a diagnostic that identifies the right axis before any model is trained: single-identifier probes. A decision tree on the capture identifier alone reaches 0.983 under a random split and exactly 0.000 under session-disjoint splitting. Under this evaluation, a metric-trained flow embedding leads four deep baselines but does not beat gradient-boosted trees on twenty packet sizes. Five of our own pre-registered architectural claims fail: distance-based rejection of unknown applications beats softmax thresholding, but ablating every metric objective makes rejection better, because class separation correlates at r = -0.88 with rejection quality. Few-shot enrollment is flat within a corpus but, across corpora, five labelled flows per class recover 91% of the deficit left by failed zero-shot transfer. All splits, results, code and negative findings are released.

AI 总结：本文通过多数据集系统评估发现，配备灵活可学习基线和核函数的霍克斯过程性能与神经TPPs相当，仅在MIMIC-IV ICU记录上神经TPPs在事件类型建模上仍具优势，还开源了TorchPP框架。

链接：https://arxiv.org/abs/2611.06348

机构：German Cancer Research Center (DKFZ)（德国癌症研究中心）； Goethe-Universität Frankfurt（法兰克福大学）； European Molecular Biology Laboratory (EMBL)（欧洲分子生物学实验室）； Ruprecht-Karls-Universität Heidelberg（海德堡大学）

作者：Hendrik Mehrtens, Florian Buettner, Oliver Stegle

英文摘要：Temporal point processes (TPPs) are the canonical framework for event sequence data, with applications spanning seismology, social media, and electronic health records. Recently, neural TPPs have been reported to substantially outperform classical TPPs, such as Hawkes processes, but these comparisons pitted flexible neural models against Hawkes processes in their most restricted form, and did not consider large-scale medical data. Here, we revisit this comparison through a systematic evaluation of Poisson and Hawkes processes of increasing flexibility against neural TPPs, on five established benchmark datasets and on two large-scale medical evaluation tasks derived from UK Biobank disease histories and MIMIC-IV ICU records, under autoregressive next-event prediction. First, the previously reported gap between Hawkes processes and neural TPPs largely disappears once Hawkes processes are equipped with flexible, learnable baselines and kernels. Second, the flexibility of the intensity parametrization also limits prior neural TPPs: a Transformer with a spline-based intensity head outperforms previous intensity-based neural TPPs and is on par with the intensity-free model. Decomposing the log-likelihood into a time term $\log p(t)$ and a type term $\log p(d \mid t)$, we find that this flexibility advantage lies almost entirely in the time density: more flexibility does not improve the type term. On these benchmarks, the gains of neural TPPs thus lie mostly in modeling when events occur, not which. Third, our results underscore the value of complex datasets for evaluating TPPs: only on the MIMIC-IV ICU records do neural TPPs retain an advantage in the type term. Our results inform both future method development and model choice. We release our framework, TorchPP, as an open-source package with consistent implementations of classical and neural TPPs.

AI 总结：该研究针对LLM测试时计算资源分配问题，发现最优分配由先验而非策略决定，提出结合先验与验证器的汤普森采样方法，可提升最难工作负载的已解决比例。

链接：https://arxiv.org/abs/2611.06372

机构：Hankuk University of Foreign Studies（韩国外国语大学）

作者：Jinhyung Bae

英文摘要：When many inputs share a budget of repeated attempts by a large language model (LLM), a common rule gives harder inputs more. On public math, code and formal-proof samples, the best published difficulty-proportional rule captures half or more of the oracle gain on benchmarks the model mostly solves, but at most 13% on hard workloads, even given true pass rates. Because the value of one more attempt peaks at intermediate difficulty, the optimal static allocation is inverted-U and gives little to inputs that are hopeless at the current budget. In a strict survey of 221 LLM allocation papers, at least 13 give the hardest inputs the most attempts; their easier settings sit at budgets where this is near-optimal, so the cost appears on harder workloads. Acting on it requires knowing which inputs are solvable, so information, not the policy, drives the gain. With a verifier and a prior from a related model's past runs on the same inputs, Thompson Sampling raises the solved share on the hardest workload from 14% to 27%; without such a prior, no policy we tested beats round-robin. For the policies we tested, the gain vanishes between prior rank correlations of about 0.3 and 0.55, which cheap text-based priors barely reach on the weakest models, and it requires a verifier whose false acceptances are rare relative to the pass rate. On new Qwen2.5 samples the pattern holds, and tempering the prior avoids losses on easy workloads. We turn these results into a decision guide.

AI 总结：该研究探究基于物理定律的低维流域模型能否达到深度学习精度，提出CaRT+模型，在三大洲602+593+505个流域实验中，其精度接近LSTM等深度学习模型。

链接：https://arxiv.org/abs/2611.06382

机构：School of Engineering, University of Birmingham（伯明翰大学工程学院）

作者：Xilin Xia

英文摘要：Dooge asked whether hydrology has laws at the catchment scale. We ask whether a low-dimensional system built under physical laws can match the accuracy of neural networks that outperform process-based models on the same inputs. Three laws constrain a lumped catchment model. Conservation makes it a flux-connected storage network, dissipation fixes the sign of every flux and aggregation determines how the unresolved composition enters the fluxes, through effective laws over threshold distributions and a memory of the forcing. These laws admit four kinds of refinement. A parsimonious model under them, CaRT (11 parameters per catchment), is refined in a loop assisted by AI agents. Each refinement addresses an observed deficit through a hypothesis from the literature. The result, CaRT+, has fourteen states, 35 parameters per catchment and no daily neural network. Most refinements follow from aggregation over thresholds or from variables that carry no water but modulate fluxes. On 602 British and 593 US gauged catchments, single CaRT+ models reach median Nash-Sutcliffe efficiencies of 0.863 and 0.765 in held-out years. A stateful LSTM reaches 0.869 and 0.751 and an entity-aware LSTM 0.862 and 0.754, while three-seed LSTM ensembles remain ahead of the CaRT+ ensemble by 0.003-0.012 in median paired NSE. On 505 drier Australian gauged catchments unused in development, single CaRT+ models reach 0.778 against 0.762 and 0.753 for the two LSTMs. The results suggest that catchment-scale laws exist as laws of form, with the constitutive families shared and only their parameters learned.

AI 总结：KolTa是一种主动学习框架，结合物理约束与PRC-UCB规则，在有限数据下的配方发现任务中，在AULC指标上优于多数对比策略，可适配多种配方问题。

链接：https://arxiv.org/abs/2611.06394

机构：School of Artificial Intelligence, Sichuan University（四川大学人工智能学院）； School of Aeronautics and Astronautics, Sichuan University（四川大学航空航天学院）

作者：Boyong Tang, Tao Wang, Lei Yang

英文摘要：Finding high-performing formulations is difficult when few have been measured and experimental budgets are limited. We propose KolTa, an active learning framework that selects batches to discover the top 5% of formulations at a target temperature under a fixed query budget. KolTa combines a three-stage predictor of component contributions, role-conditioned interactions, and anchored temperature response with task-specific soft physics constraints. Its Physics-Residual-Calibrated Upper Confidence Bound (PRC-UCB) acquisition rule combines predicted utility, ensemble disagreement, and a target-temperature physics residual, with nonnegative exploration weights fitted to out-of-fold signed errors. We evaluate the framework on three measured tasks and one simulated task covering binary-mixture viscosity, drug solubility, ionic-liquid viscosity, and electrolyte conductivity. KolTa ranks first or second in mean area under the learning curve (AULC) on every task and outperforms seven of the eight comparator strategies in paired tests, winning 75 of 78 paired comparisons across tasks and seeds against those seven strategies. Across the three measured tasks, its largest gap to the best mean AULC is 0.032, compared with at least 0.056 for every comparator. On Drug, reaching 80% average recovery of the remaining targets across drugs requires an estimated median of 348 additional formulation queries, about 18% fewer than the fastest comparator. Researchers can adapt the framework to diverse formulation problems through component roles and physical constraints.

AI 总结：本研究提出用临近预测器预测模型未来准确率，结合适者生存（SOTF）训练进行种群剪枝，在ResNet-18、ViT等性能差异大的模型库中，可缩小与最优模型的性能差距。

链接：https://arxiv.org/abs/2611.06512

机构：University of St.Gallen（圣加仑大学）； University of Montreal（蒙特利尔大学）； Weight Space Labs（权重空间实验室）

作者：Rico Städeli, Léo Meynent, Boris Knyazev, Damian Borth

英文摘要：Training high-performing neural networks often involves evaluating populations of candidate models with varying hyperparameters, architectures, or random seeds. Training every candidate to completion is computationally expensive, yet stopping seemingly weaker runs early risks discarding models that would ultimately perform better, as learning curves can cross. To address this challenge, we investigate whether intermediate training checkpoints can predict future model accuracy and enable more informed pruning decisions. Our nowcasters predict test accuracy five epochs ahead or at the end of training. They are trained with accuracy and loss histories, weight statistics, and SANE representations as inputs. SANE is a transformer autoencoder that encodes a checkpoint as a sequence of latent tokens. We then use the nowcasters in Survival-of-the-Fittest (SOTF) training. SOTF repeatedly ranks runs during training by predicted accuracy and stops the lowest-ranked ones. Our results show that weight statistics work best for five-epoch forecasts, and SANE representations work best for final accuracy prediction. For evaluation, we compare SOTF with pruning the population by current accuracy. In homogeneous model zoos such as the CNN zoos, pruning by current accuracy already retains a final model only slightly worse than the best. Model zoos with a larger spread in performance, such as the ResNet-18 and ViT zoos, are more difficult to prune by current accuracy alone. In these zoos, SOTF with nowcasted final accuracy almost closes the gap to the best model.

AI 总结：该研究提出Spider Network Filtering Framework，基于FDIC协助的55项破产银行收购数据构建交互感知决策框架，经评估其性能未显著优于基准模型，且相关收益关联在行业基准下消失。

链接：https://arxiv.org/abs/2611.06514

机构：Rutgers University（罗格斯大学）； Kean University（肯恩大学）； Eindhoven University of Technology（埃因霍温理工大学）； Seton Hall University（薛顿贺尔大学）

作者：Yousef Nejatbakhsh, Malihe Aliasgari, Parsa Safaee, Sina Shokoohyar

英文摘要：Acquirers of failed banks weigh acquisition value, regulatory protection, risk, buyer strength, and execution difficulty jointly, because each signal's meaning depends on the others: high non-performing loans matter less when government loss sharing absorbs the losses. The proposed Spider Network Filtering Framework encodes such dependencies as four interpretable interaction rules among five dimensions, yielding graph-inspired features for a logistic model and a Pursue/Caution/Avoid decision rule. We evaluate it on 55 acquisitions assisted by the Federal Deposit Insurance Corporation in 2008-2024 by acquirers with a parent listed in the United States. Six financial inputs come from regulatory reports published before each failure; two deal terms, which the winning bidder knows when it bids but investors learn at the announcement, come from the regulator's press releases. The outcome, a positive three-day abnormal return around the announcement, records the market's initial reception, not realized success. Under grouped, nested cross-validation, the Spider-enhanced model reaches a mean area under the receiver operating characteristic curve of 0.594, against 0.615 for logistic regression on the same inputs and 0.632 for ridge regression; permutation tests that repeat the full model selection give at most weak evidence that any of them ranks better than chance. The Spider score used directly does not rank better than chance, and an apparent link between target distress and higher returns disappears against a bank-sector benchmark. Thresholds tuned for the F1 score approve most acquisitions with negative returns and realize negative utility when each costs three times what a positively received acquisition gains.

AI 总结：提出EvoOPD将PI获取视为优化问题，维护随学生状态演化的策略库，在数学推理和代码生成任务上较基线有显著提升。

链接：https://arxiv.org/abs/2611.06542

机构：University of the Chinese Academy of Sciences（中国科学院大学）； Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）； Southwest University（西南大学）； Southeast University（东南大学）； Jilin University（吉林大学）； United Arab Emirates University（阿联酋大学）； Vast Intelligence Lab（智源实验室）

作者：Ximo Zhu, Ruiqi Liu, Yi Han, Zhengan Wu, Gengsheng Li, ZiAn Wang, Zhu Yuantian, Jin Jiang, Wenhao Wang

英文摘要：Proprietary language models exhibit strong reasoning and coding capabilities, but their output distributions are inaccessible, making fine-grained distillation methods that rely on teacher logits difficult to apply directly to black-box models. We observe that rich textual feedback from a black-box teacher can be treated as privileged information (PI) and converted, through a PI-conditioned privileged teacher, into dense supervision signals on the student's own trajectories. However, what PI should be acquired from the teacher to most effectively support student learning remains unclear. PI strategies that differ in where they focus, what information they request, and how much detail they provide can have substantially different effects on the current student, and their relative effectiveness can further change as training progresses. Based on this observation, we propose Evolving Privileged Information Strategies for Black-Box On-Policy Distillation (EvoOPD), which treats PI acquisition itself as an optimization problem and maintains a PI Strategy Bank that evolves with the student state. EvoOPD uses the measured repair utility of PI on the current student to adapt the focus, information type, and granularity of teacher feedback, allowing the acquired information to continually match the student's learning needs. We systematically evaluate EvoOPD on mathematical reasoning and code generation with Qwen3-1.7B, 4B, and 8B students. EvoOPD achieves the best overall performance in all six model-task settings, improving over the strongest baseline in each setting by an average of 2.14 percentage points in mathematics and 1.30 points in code. Further analysis shows that the relative effectiveness of PI acquisition strategies changes with student state, making a fixed strategy difficult to sustain as the best choice throughout distillation.

AI 总结：本研究开发热力学引导神经网络TiNN，通过学习自由能路径表示深共晶溶剂的氢键网络，其在溶解度预测中精度优于COSMO-RS，可用于木质纤维素分馏的溶剂筛选。

链接：https://arxiv.org/abs/2611.06590

机构：King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）； Chongqing University（重庆大学）

作者：Yanpeng Ye, Chaoran Duan, Mian Xu, S. Mani Sarathy

英文摘要： Deep eutectic solvents (DESs) offer a tunable platform for lignocellulose refining, but their solvation behavior depends on multicomponent hydrogen-bond networks rather than individual constituent properties. We develop TiNN, a thermodynamics-informed neural network that hierarchically represents HBA-HBD association and solute accommodation through a learned free-energy pathway. Trained on 938 records, TiNN achieves a test-set $R^2$ of 0.917 and generalizes to new HBA-HBD combinations and sparse conditions. Ablation identifies solute-specific stabilization as the largest contributor to prediction accuracy, while the importance of hydrogen-bond compatibility depends on the dissolution mechanism. Experimental measurements of glucose, xylan, and Kraft lignin in acidic, basic, and neutral DESs show that TiNN reduces the geometric mean solubility error from more than 23-fold with COSMO-RS to approximately 1.5-fold. Without training on fractionation data, TiNN also distinguishes DES formulations with high and low lignin and hemicellulose removal from corn stover. These results support chemically grounded solvent screening for lignocellulose fractionation.

AI 总结：提出VGSC框架，结合生成器多样性与验证器正确性，在多推理基准上提升测试时推理准确率。

链接：https://arxiv.org/abs/2611.06606

机构：ETH Zürich（苏黎世联邦理工学院）； MBZUAI（Mohamed bin Zayed 人工智能大学）； Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）

作者：Ekaterina Fadeeva, Xueyan Li, Mrinmaya Sachan, Maxim Panov

英文摘要：Large language models can solve many multi-step reasoning problems, but their reasoning remains brittle: plausible intermediate steps often lead to incorrect answers. Improving reasoning at test time requires balancing two imperfect signals: the generator, which provides diversity, and the verifier, which guides correctness. Existing approaches largely favor one side of this tradeoff. Verifier-weighted voting relies on trajectories sampled from the generator and cannot steer generation toward correct ones. By contrast, beam-style verifier-guided search can steer generation, but may over-concentrate on narrow trajectory sets and amplify verifier errors. We propose Verifier-Guided Self-Consistency (VGSC), a stochastic decoding framework that combines step-level correctness and future-outcome estimates to guide reasoning during generation. VGSC repeatedly proposes candidate next steps from the base language model and uses both signals in an acceptance/resampling procedure, while maintaining multiple trajectories for final answer-level self-consistency. We provide a probabilistic interpretation of VGSC as approximate sampling from a verifier-tilted trajectory distribution. Across multiple reasoning benchmarks, generator models, and verifier families, VGSC achieves higher average accuracy than representative baselines.

AI 总结：本文提出测试时自一致性优化方法，将未冻结的序列重构器转为插补器，在多数数据集-骨干组合中降低MAE，可作为掩码感知插补的补充适配器。

链接：https://arxiv.org/abs/2611.06612

作者：Björn Ludwig, Alexander Windmann, René Heesch, Oliver Niggemann

英文摘要：Complete sensor-channel failures create a difficult form of time-series missingness: local interpolation is unavailable, and deploying a separately trained imputer for every failure pattern may be impractical. We ask whether a sequence reconstructor trained without artificial mask augmentation and then frozen can be converted into an imputer at inference time. Our method treats the missing entries of each test window as optimization variables, clamps all observations, and backpropagates through the frozen model to minimize reconstruction error only on observed coordinates. The resulting observed-self-consistency objective requires neither artificial mask augmentation nor full-channel dropout during reconstructor training. We characterize when this refinement is informative through the cross-Jacobian from missing inputs to observed residuals, and use an optional temporal-smoothness prior when that coupling is weak. Across five real-world datasets with exactly one channel synthetically masked per test window and four reconstruction architectures, no-prior test-time refinement reduces MAE relative to one-pass reconstruction in 19 of 20 dataset-backbone combinations. Compared with mask-aware imputers trained under either standard structured masks or matched full-channel dropout, the best refined reconstructor achieves the lowest MAE on two datasets and trails the strongest baseline on three. These gains require substantially more inference time and are sensitive to cross-channel coupling. Our results establish test-time self-consistency as a conditional inference-time adapter for frozen reconstruction models that complements mask-aware imputation without retraining the reconstructor.

AI 总结：该研究针对API蒸馏攻击，利用请求时间戳指纹识别其调度策略，通过20%种子集可在20分钟内以0.92-1.00 AUC排名攻击账户，固定密钥可规避检测但会降低吞吐量，证明时间特征可暴露共享基础设施模式。

链接：https://arxiv.org/abs/2611.06661

作者：Snigdha Chandan Khilar

英文摘要：API distillation campaigns try to hide model-stealing traffic by splitting requests across thousands of load-balanced accounts, which standard content classifiers often miss. However, request timestamps, specifically arrival time and stream completion time, reveal the underlying dispatching strategy, such as round-robin counters or key-shuffling pools. Using a 20 percent seed set, this timing analysis can forensically rank the remaining accounts of a campaign with 0.92 to 1.00 AUC within 20 minutes, even across thousands of real production accounts. While adaptive attackers struggle to hide rotating dispatchers using jitter, pinning keys successfully evades detection, though it can severely throttle throughput when quotas bind. Ultimately, timing exposes shared infrastructure patterns even when content filters fail.

AI 总结：该研究针对自主苹果品尝的部分反馈模型，在可实现与不可知设定下，基于VC维等参数建立了错误数或遗憾的速率分类，拓展了Raman等人的相关工作。

链接：https://arxiv.org/abs/2611.06677

机构：Purdue University（普渡大学）

作者： Tanmay Devale, Reed Phillips, Arvind Ramaswami

英文摘要：We study the classical apple-tasting setting, a partial feedback model in which the learner observes the true label only when it predicts $1$. A combinatorial characterization for the rates of this problem in the online adversarial setting has been studied by Raman et al. (2024). We study the self-directed setting, in which the learner can adaptively choose the order of the points it predicts on. In the realizable setting, we establish a tetrachotomy on the expected minimax number of mistakes made by any learner. Depending on the VC dimension, the $\emptyset$-centered star number, and the self-directed dimension, we show that the rates could possibly be $\Theta(1)$, $O(\log n) \cap \omega(1)$, $\tilde \Theta(\sqrt{n})$, or $\Theta(n)$. In the agnostic setting, the minimax regret is $\tilde \Theta(\sqrt{n})$ or $\Theta(n)$, depending on the VC dimension.

AI 总结：本研究提出SecureGARD框架，通过动态本体引导治理，在AgentDojo等三个基准上实现0%攻击成功率，同时提升受攻击时的任务效用，强化使用工具的LLM智能体的执行安全。

链接：https://arxiv.org/abs/2611.06682

机构：Nanjing University of Aeronautics and Astronautics（南京航空航天大学）； Tsinghua University（清华大学）； Beihang University（北京航空航天大学）； Tongji University（同济大学）

作者：Guoqin Xiong, Shengtao Wen, Xiang Chen, Xinyi Zeng, Zhaoxin Fan, Wei Ye, Xiaofeng Cao, Sheng-Jun Huang

英文摘要：Tool-using large language model (LLM) agents translate model outputs into consequential actions, turning indirect prompt injection into an execution-security problem. Existing defenses constrain tool calls, trajectories, or information flows, but often leave action semantics, state dependencies, and evidence relations implicit, limiting generalization across heterogeneous tools and complicating safe updates. We present \ours{}, a dynamic ontology-guided governance framework. \ours{} grounds heterogeneous calls in a shared semantic representation and combines an executable ontology with episode-level task state to derive deterministic ready, waiting, and forbidden decisions. A waiting decision identifies an unmet but recoverable obligation, enabling bounded replanning without expanding the agent's authorization boundary. \ours{} evolves its ontology offline from verified training executions. An offline LLM proposes instance-independent action--state axioms, which are accepted only if they address ontology errors exposed by training failures, preserve logical consistency and protected entailments, and pass held-out validation before the ontology is frozen. We evaluate \ours{} on AgentDojo, ASB, and AgentDyn under benign and indirect prompt-injection settings. \ours{} achieves a 0\% attack success rate (ASR) across all three benchmarks while improving task utility under attack compared with existing baselines. These results show that explicit semantic governance can strengthen execution security without indiscriminate blocking.

AI 总结：该研究提出PINR-ESI框架，无需配对源标签或模拟监督预训练，通过物理信息自监督隐式神经表示实现脑电图扩展源成像，经模拟及真实数据验证可有效定位活跃皮层区域。

链接：https://arxiv.org/abs/2611.06700

机构：University of Science and Technology of China（中国科学技术大学）； The Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）； Peking University（北京大学）

作者：Zhiyong Zhou, Sheng Liao, Youshen Xiao, Xiaolin Li, Yakang Dai

英文摘要：EEG extended-source imaging aims to localize active cortical regions and characterize their spatial extent, yet supervised inverse networks typically depend on synthetic EEG-source pairs and may be sensitive to synthetic-to-real mismatch. We propose a physics-informed self-supervised implicit neural representation (INR) framework for spatial source reconstruction without paired source labels or simulation-based supervised pretraining. The lead field matrix encodes the source-to-sensor volume-conduction model and constrains the INR through agreement between predicted and measured EEG. Cortical current density is modeled as a continuous coordinate-conditioned field. A classical inverse estimate provides warm initialization and spatial constraints on source activity, while patch-integrated projection maps the field to discrete source coefficients for forward modeling. Simulations with varying noise levels, source counts, and source extents showed the clearest advantage in identifying active cortical regions, particularly for multiple and larger sources. Quantitative and qualitative evaluations using real EEG recordings from epilepsy patients and intracranial stimulation experiments further supported the feasibility of the framework for source imaging. These findings suggest the potential of physics-informed self-supervised INR for extended-source imaging.

AI 总结：本研究提出模板间隙重构（TGR）方法，结合1870万条输出构建的模板库训练模型，在小型语言模型的Shell输出模拟中提升输出编辑相似度、减少生成令牌数，为小型Shell模拟器提供实用设计方案。

链接：https://arxiv.org/abs/2611.06731

机构：University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）

作者：Jarrod Ragsdale, Rajendra Boppana

英文摘要：Small language models offer a compact basis for shell simulation without executing submitted commands, but full-output decoding requires them to predict recurring utility structure alongside content that varies across executions. We introduce template gap reconstruction (TGR), which supplies reusable output literals interspersed with sentinels to be interleaved with autoregressively generated gaps. A template selector chooses which template to be reconstructed, with the empty template retaining full-output decoding. We construct a template bank from 18.7 million outputs collected across heterogeneous execution environments and train a template selector and five reconstruction models initialized from various small language model checkpoints on a target environment excluded from bank construction. Evaluation demonstrates that TGR improves output edit similarity (O-ES) over full-output decoding (FOD) when the optimal template covers more than 25\% of the template, while performing similarly for lower-coverage outputs. Additionally, TGR outperforms FOD for shorter gap sequences, with O-ES values of 0.964 and 0.812, respectively, while maintaining similar performance for longer sequences. TGR reduces the number of tokens generated by 26.3--38.3\% across used tokenizer families. These results support using a reusable output structure as a practical design choice for small shell simulators, improving response fidelity and shortening generation targets while keeping template selection and residual prediction central to response quality.

AI 总结：针对ARC-AGI-3任务，提出基于空间感知的SARBLOH智能体，通过零参数感知引擎提升评分、减少令牌使用，在公开及隐藏数据集上均大幅优于基线。

链接：https://arxiv.org/abs/2611.06793

作者：Harsidak Singh Banwait

英文摘要：Sample-efficient fluid intelligence requires agents that rapidly form working models of unfamiliar interactive environments. ARC-AGI-3 tests this across uninstructed visual games scored by Relative Human Action Efficiency, which penalizes exploratory waste quadratically. When frontier models read raw integer grids, serialization destroys spatial topology, and they spend hundreds of actions rediscovering basic boundaries and symmetries. We introduce SARBLOH, an open-weight agent built on the premise that spatial perception, not planning capacity, governs interactive reasoning on ARC-AGI-3. A zero-parameter O(HW) perception engine turns each frame, in 35 ms, into an object inventory with bipartite temporal tracking, a geometric briefing that separates proven invariants from affordance hypotheses, and an annotated canvas. Paired with a code-as-action runtime, the agent writes algorithmic hypotheses instead of low-level moves. Across eight presentations, grounded perception raises the rubric score from 3/28 to 25/28 points while cutting token use by 89.4%. On 23 held-out games, geometric invariants generalize with 100% accuracy, while single-frame affordance guesses fall to 6-50%, so active verification probes remain necessary. On the public set, Run A (briefing off) solved 143 of 183 levels in 8.2 hours at 59.4% single-pass efficiency; Run B (briefing on) reached 63.5%. Within the nine-hour budget on one Kaggle RTX PRO 6000 GPU, SARBLOH scored 16.53 on the hidden Kaggle evaluation, 13.7x the first Milestone winner.

AI 总结：本文研究解释忠实性评估标准的分歧，引入忠实性函数重构现有标准，通过案例研究明确场景对选择的约束，为从业者提供应对分歧的方法。

链接：https://arxiv.org/abs/2611.06846

机构：Max Planck Institute for Security and Privacy（马克斯·普朗克安全与隐私研究所）； Yale University（耶鲁大学）

作者：Grant Shanklin, Jiří Němeček, Asia Biega

英文摘要：Faithfulness requires that algorithmic explanations accurately reflect the model's behavior. Yet existing operationalizations of faithfulness can disagree about whether the same explanation is faithful, leaving developers and auditors to choose among conflicting evaluations. In this paper, we investigate disagreement among evaluation criteria and offer a practical path forward for evaluating explanation faithfulness. We first identify how operationalizations of faithfulness differ. We introduce a faithfulness functional that locates their disagreements in what an explanation claims about the model, which comparisons bear on that claim, and how agreement across those comparisons should be aggregated, and we reconstruct existing evaluation criteria within it. We argue these reconstructions support understanding faithfulness as a common though incompletely specified relation between the model and explanation, so the differences reflect choices left open by the general requirement. Justifying an operationalization then comes down to justifying these choices, which, on our view, draws on explanatory, institutional, and normative considerations. We support this argument with a case study of adverse action notices, reconstructing the evaluation implicit in an industry-proposed procedure and identifying which of its choices the setting justifies and which remain open. Our work gives auditors and practitioners a way to navigate disagreeing evaluations of faithfulness. It also explains why the literature should expect multiple operationalizations, while showing how concrete settings can narrow the choice among them. More broadly, our analysis shows how a shared requirement of faithfulness can support contextually justified evaluations despite disagreement among existing metrics.

AI 总结：本研究提出 AMM（自适应多模式），通过在线探测优化景观特征在多种优化引擎间路由预算，在 bbob 等基准测试中多数场景下优于竞品，已发布为开源 Python 包。

链接：https://arxiv.org/abs/2611.06879

作者：Benjamin Jacob Bodner

英文摘要：The best classical optimizer for a continuous black-box problem depends on its budget and landscape. Existing routers decide from the dimension and budget, past improvement or features fitted to one benchmark, and few test an optimizer's implicit assumptions online. AMM (Adaptive Multi Mode) routes a known budget among quasi-Newton, trust-region, coordinate, multistart and evolution-strategy optimization engines with online probes of curvature, plateaus, separability, kinks, basins and fractal structure. Each probe has a default, a dimension-by-budget validity range and a cost charged to the budget. In its pre-registered development loop on bbob, 32 of 36 predictions held over the last 16 confirmations. AMM was then tested once on held-out bbob instances 101-110 (D=2-40, 10*D-1000*D) against up to seven competitors. It has the highest target share in 382 of 600 function-dimension-budget cells (236 outright, 146 tied) and, by a descriptive posterior rule, is credibly behind in 35, mostly at 10*D and 30*D. At every budget it also leads the controls built from its own engines (the best engine alone, the best engine per dimension and budget, and a greedy schedule), narrowly at 10*D and 30*D, and nevergrad's NgIohTuned. In pre-registered runs its lead over IPOP-CMA-ES holds under affine and box transformations, on box-constrained bbob and, pooled, on rotated f2-f4, but rotating the separable f3 and f4 removes most of the lead there. On held-out MA-BBOB its final share at 2000*D is level with IPOP-CMA-ES's and its anytime area lower. We published AMM as an open-source Python package (pip install amm-optimizer; this https URL ).