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arXiv 最近完成扫描:2026-11-09 07:12 · 全站最近入库:2026-11-08 06:02各信源进度 →

统计口径与数据新鲜度

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版本更新 · v3arXiv:2609.15545

The Token Before the Value Is the Key

Ke Cheng, Xin Xu, Zhichao Chen, Yixiao Chen, Lei Xin, Jianbo Zhao, Fanhu Zeng, Yue Liu 等 10 位作者

原文摘要

Induction circuits retrieve a value by matching a query to information carried from the token before that value. How this address forms, incorporates context, and supports sequential retrieval remains less clear. We use hybrid language models as a tractable testbed and identify the historical source key as a common causal interface between local preparation and global retrieval. Probes and restoration interventions connect this interface across controlled models, pretrained recurrent and sliding-window hybrids, and released language models. We organize the analysis by address context radius and sequential retrieval depth. First, shared local parameters couple predecessor Carrying to recurrent state processing; separating these demands or biasing initialization promotes address formation. Second, adapting input representations extends an established literal circuit to category matching, making semantically compatible records active competitors. Earlier context resolves this competition through the source key, with a sharp visibility boundary in a controlled single-read model and compositional relays across local layers. Third, fixed relations support memorization shortcuts, whereas sequential generalization requires learning an output-to-query bridge between retrievals. Controlled state-tracking and compositional in-context tasks demonstrate this requirement across stacked stages, looped recurrence, and intermediate-token generation. Together, these results connect circuit formation, contextual disambiguation, and sequential use through a shared retrieval interface. Code is available at https://github.com/ckpassenger/bind-match-copy.

cs.LG

版本更新 · v2arXiv:2609.32991

数据应该教什么?跨越电路、存储与使用的瓶颈迁移

What Should Data Teach? Moving Bottlenecks Across Circuit, Store, and Use

Yixiao Chen, Ke Cheng, Yue Liu, Jun Zhang, Jie Jiang

AI 速读 基于摘要

作者提出一种“电路视角”的数据诊断原则,将语言模型训练中的瓶颈分为计算形成、内容可用与路径选择三类,并报告相应数据干预在350M模型实验中的效果。

cs.LG

查看原文摘要

What should data teach a language model at a particular point in training? A circuit view reveals three distinct bottlenecks: forming a computation, making its required content available, and selecting among available routes. A shared diagnosis-to-data principle connects them: localize the missing operation, preserve its causal relation, vary shortcut-bearing context, and re-audit the residual. Formation-sensitive selection and prerequisite ordering accelerate a binding-matching-transport path; a brief early prefix from the same training multiset retains a validation advantage through 100B tokens. Availability counterfactuals then distinguish writing content from invoking available memory, while paired supervision and context-opportunity ranking improve matched route decisions and long-context answer likelihood. A continuous 350M-model experiment connects the three interventions on the same facts: early circuit training improves subsequent learning, and the complete sequence outperforms stage-replacement controls on facts withheld from Use teaching. Independent query surfaces and opposed-source decisions expose conditional arbitration as the remaining frontier. Together, these results show why a change in the limiting operation calls for a change in supervision, not merely a new ranking of difficult examples.

版本更新 · v3arXiv:2609.07974

A $(3+ε)$-Approximation for Fair $k$-Means and a Matching Lower Bound

Kangke Cheng, Guanlin Mo, Shihong Song, Hu Ding

原文摘要

Fair clustering aims to prevent algorithmic bias against protected groups. We study Euclidean fair $k$-means, in which the proportion of each protected group in every cluster lies between prescribed lower and upper bounds. Because these constraints violate the nearest-center assignment property, center selection is coupled with point assignment. We introduce the drift-regularized relaxation, a new convex relaxation solved by a modified Frank-Wolfe method with linear-programming iterations, whose optimality condition controls the cost of converting its solution into $k$ fair clusters. Combined with any $ρ$-approximation algorithm for weighted $k$-means, the relaxation yields an exactly fair fractional solution of cost at most $2ρ+1+ε$ times the fractional optimum, improving the previous ratio $1+4ρ+ε$. In fixed dimension, this improves the best known ratio from $5+O(ε)$ to $3+O(ε)$. The fractional solution can be rounded to an integral one without changing the centers or increasing the cost, at a bounded additive violation of the fairness bounds. We also show that a $(3-δ)$-approximation is NP-hard for every fixed $δ>0$, even on the line with fractional assignments, so $3$ is the exact approximation threshold for fractional fair $k$-means in fixed dimension. The same guarantees hold for $k$-sparse Wasserstein barycenters, a special case of fractional fair $k$-means. More generally, they hold whenever every cluster must satisfy given homogeneous linear inequalities in the fractions of its points, such as a bound on the ratio between two groups or on the average of a numerical attribute in every cluster.

cs.CG · cs.LG

版本更新 · v2arXiv:2610.16491

Drafting Agents, Not Just Tokens: Speculative Recursive Self-Improvement for Agentic Systems

Xin Xu, Ke Cheng, Hao Wang, Yangru Huang, Yanghao Xiao, Zhichao Chen, Zhisheng Zhong, Xun Liu 等 14 位作者

原文摘要

Improving an agent through successive revisions requires balancing exploration and evaluation under a limited budget. A narrow search can miss useful sequences of edits, while fully evaluating every intermediate version often exhausts the budget before those sequences yield a verified improvement. Inspired by the draft--verify principle of speculative decoding, we propose Speculative Recursive Self-Improvement (SpecRSI), a two-level framework coupling inner-loop agent search with outer-loop improver learning. SpecRSI drafts successors before their predecessors are fully evaluated, while adopting only fully evaluated, admissible versions. In the inner loop, Verification-Aware Tree Search (VATS) allocates the budget between further edits and outstanding checks using the predicted verified return of a continuation. In the outer loop, Group Verification Optimization (GVO) updates the improver from completed searches while keeping the real evaluator fixed. We evaluate SpecRSI on scientific optimization (including active queue management (AQM) for network infrastructure), coding, and general agent-system benchmarks. Under matched evaluation-call budgets, SpecRSI improves scientific terminal gain over Janus by 60.0%--96.3% and area under the budget curve by 32.1%--91.6%, while achieving higher means across all 50 scientific backbone--task pairs and outperforming MetaRSI-v1 by 1.30--2.70 percentage points (pp) in system score across twenty backbones. On instance-level comparisons, it further improves PyBaMM terminal gain by 142.6% and SWE-Pro success by 6.0 pp at 7.0% and 9.2% lower token cost, respectively. These results demonstrate the efficacy of the proposed approach.

cs.LG · cs.AI