Zhaoyang Chu, Jiarui Hu, Xingyu Jiang, Pengyu Zou, Han Li, Chao Peng, Peter O'Hearn, Earl T. Barr 等 11 位作者
We introduce TerminalWorld, a scalable data engine that automatically reverse-engineers high-fidelity evaluation tasks from "in-the-wild" terminal recordings. Processing 80,870 terminal recordings, the engine yields a full benchmark of 1,530 validated tasks, spanning 18 real-world categories, ranging from short everyday operations to workflows exceeding 50 steps, and covering 1,280 unique commands. From these, we curate a Verified subset of 200 diverse, manually reviewed tasks. Comprehensive benchmarking on TerminalWorld-Verified across eight frontier models and six agents reveals that current systems still struggle with authentic terminal workflows, achieving a maximum pass rate of only 62.5%. Moreover, TerminalWorld captures real-world terminal capabilities distinct from existing expert-curated benchmarks (e.g., Terminal-Bench), with only a weak correlation to their scores (Pearson r=0.20). The automated engine makes TerminalWorld authentic and scalable by construction, enabling it to evaluate agents in real-world terminal environments as developer practices evolve.
Shahan Ali Memon, Anna-Maria Gueorguieva, Anukriti Kumar, Lucy Lu Wang, Jevin D. West, Nic Weber
In the years following the public release of ChatGPT in late 2022, more than half of U.S. states have published formal guidelines around AI usage in K-12 education, creating a record of largely independent state education departments responding to a shared technological disruption. In this work, we analyze publicly available AI usage guidance documents from 35 states in the U.S. and Puerto Rico. We compare the guidance in these documents across various dimensions, including pedagogy and instruction, audience, actionability, workforce and career readiness, responsible use and equity, and alignment with state-level demographics and administrative capacity. Finally, we investigate who authored these documents and their cited sources, identifying epistemic foundations underlying their claims. We find that guidance priorities converge on topics anchored in laws such as data privacy and academic integrity, each addressed in 97% of documents. While most documents mention equity of access (86%) and algorithmic bias (97%), fewer discuss specific marginalized populations. Across documents, there is a common framing of urgency around AI integration to prepare students for workforce readiness (86%) and promote loosely-defined personalized learning (86%), which is not matched by specific guidance for capacity-building, actionability, or accountability. State priorities show no relationship to state demographics, capacity, or partisanship. Instead, we identify notable interdependencies between states with shared citations to non-profit organizations like TeachAI and Code.org, with some states reproducing TeachAI's model guidance near-verbatim, and peer citations concentrating on a few early states such as Oregon and Washington. No state includes parents as contributors on these documents, and only five include students.
We study community search when each vertex has its own tolerance for differences in position. For positions such as opinions or homes, a latitude-consistent community (LCC) contains the query vertex, is connected, has minimum degree at least k, and bounds its diameter by the smallest member tolerance. Motivated by social judgment theory, the bound exactly characterizes persistent acceptance of every interaction in a closed group under bounded-confidence dynamics with arbitrary positive weights. It also prevents rejection under social-judgment dynamics, whereas natural relaxations preserve neither guarantee. Finding a maximum LCC is NP-hard even in the plane, and with unbounded dimension, deciding whether an LCC exists is NP-complete for every fixed k >= 2. LatSweep turns the coupled bound into a sweep over the smallest tolerance, yielding two feasible heuristics, a (1,2)-bicriteria approximation and an exact branch-and-bound algorithm. Across eight graphs with up to 1.8 billion edges, the exact algorithm proves optimality on all but 9 of 3,700 efficiency queries and usually certifies absence within microseconds when no LCC exists. Its branch-and-bound solver proves more queries than a reimplemented maximum-(k,r)-core algorithm, a mixed-integer program and CP-SAT. In simulations, uneven influence disrupts initially accepted interactions in pairwise groups. Outside ties invalidate the guarantee for many real groups. On two location-based networks, tolerance-agnostic groups often place another member's home beyond a member's estimated travel radius; comparisons with spatial communities of the same size detect no co-visit loss from enforcing every member's tolerance.
Onur Tuncay Bal, Samuel Coavoux, Nathalia A. Loureiro, Balazs Vedres, Mark Wittek
Cultural fields cohere not only horizontally, through collaboration, but also vertically, through lineages of artistic influence. Considerable attention has been devoted to collaboration networks in various fields of creative production; however, we know less about the structure of artistic influence that is nevertheless key to individual artistic development and the evolution of genres. We examine the structure of such an influence network in a genealogy of 52 prominent jazz trumpet players, combining musicians' discographies, demographic characteristics, geographical locations, and audio analysis of more than 16,000 recordings with exponential random graph models. Direct collaboration, overlap in collaborators, and sonic context similarity, that is, the similarity of the recordings on which players appear, are each associated with an ascribed influence tie, net of the others. Not surprisingly, ascribed ties connect musicians whose recordings share a similar sonic context, but musicians also tend to collaborate with their influences and share collaborators with them. However, players whose recordings share a sonic context and who move in the same circle of collaborators can be thought of as peers or rivals, rather than as influences of each other, because lineage implies a succession predicated on difference as much as on continuity. Taken together, our findings suggest that a field's account of its own evolution is neither a record of who played together nor a map of shared sonic contexts, but a selective ordering that requires both. While our data do not permit a causal account, our aim is to provide a rich and varied methodological strategy and descriptive as well as inferential tools to understand the yet understudied vertical integration of cultural fields through influence ties.
Integer programming is a fundamental and important NP-hard problem. This motivated extensive efforts in studying several tractable subclasses. One of the most important, unresolved complexity questions is the parameterized complexity of 4-block IPs, a natural class characterized by having a diagonal matrix with small blocks after deleting few rows and columns. Over the years, significant progress has been made in improving algorithms for 4-block IPs, but the question whether such IPs can be solved in FPT time, parameterized by the block dimensions and largest matrix coefficient, has remained open. This question is repeatedly highlighted, most recently by Koutecký [IPEC 2025] and by Eisenbrand and Rothvoss [SODA 2026]. We resolve this question in the positive by providing an FPT time algorithm. Our algorithm can optimize non-linear, separable convex objective functions, and can be extended to broader classes of constraint matrices (such as tree-fold or multi-stage) and allows appending few ``global'' columns to it, and it allows coefficients unbounded by the parameters in those columns. It is known that tractability cannot be extended further in any of those directions. The key structural property that we establish is that a function $f\colon\mathbb Z^n\to\mathbb R$ that is integer midpoint convex, i.e., $f(x)\le\tfrac12f(x-p)+\tfrac12f(x+p)$ for all $x,p\in\mathbb Z^n$, can be extended to a convex function on the set $2d\mathbb Z^n\cap L$ if $L$ is a linear subspace of dimension $d$. This closes the gap in a recent work by Ligthart [arXiv 2606.30330, 2026], which allows us to extend the previous algorithm that solves 4-block integer programs with a single global variable to incorporate any parameterized number of global variables. As an example, we apply our algorithm to solve an open problem posed by Boehmer and Koana [ACM Trans. Algorithms, 2024] in Max-Min Fair Matching.
The study of categories abstracting the structural properties of relations has been extensively developed over the years, resulting in a rich and diverse body of work. In a previous paper we offered a survey providing a modern presentation of these ``categories for relations'' as instances of gs-monoidal categories, showing how they arise as Kleisli categories of suitable symmetric monoidal monads. The end result was a taxonomy that organised numerous related concepts in the literature, including in particular Markov and restriction categories. This paper further enriches the taxonomy: it proposes two categories that are once more instances of gs-monoidal categories, yet more abstract than Markov and restriction categories. They are characterised by an axiomatic notion of mass and domain of an arrow, the latter one of the key ingredients of restriction categories, which generalises the domain of partial functions. The paper then introduces mass and domain preserving monads, proving that the associated Kleisli categories in fact preserve the corresponding equations and that these monads arise naturally for the categories of semiring-weighted relations.
Can AI contribute to formal demography? We report an experiment in which an AI model, Claude, was asked to explain and then simplify the proof of an earlier result: that vaccinating the oldest against COVID-19 saves both the most lives and the most years of life, because the years saved per person treated rise with age. The model proposed a new proof that is shorter, more intuitive, and somewhat more general than the original, built from a known life-table identity and the definition of convexity. It states the condition for the result as the convexity of the reciprocal of the hazard, which can be checked from the hazard alone and holds for most familiar parametric models, and it yields a lower bound on life expectancy whenever that condition is met. Prompted by a further question, the model also gave an exact form of the condition, showing how far convexity can be relaxed. The model traced these identities and related results in earlier literature. An edited record of the sessions is provided, and complete transcripts are available on request. The exercise suggests that AI can revisit established results, make them easier to understand, and prove valuable generalizations.
Maayan Yesharim, Hava Goldstein, Gabi Kolodny, Assaf Ben-Levy, Tamar Kis-Papo, Dor Gavish, Rotem Vidan, Nirit Lavie-Alon 等 10 位作者
Photo-identification underpins non-invasive capture-recapture monitoring of amphibians, but manual identification becomes slow and error-prone as reference libraries grow. Focusing on the Near Eastern fire salamander (Salamandra infraimmaculata), we developed a pipeline based on zero-shot local-feature matching (SuperPoint and LightGlue) without species-specific training and evaluated it on $2{,}440$ field photographs from Haifa and Tel Dan in northern Israel. We isolated the yellow spot pattern, scored image similarity by the number of high-confidence keypoint matches, which reduced a confound whereby many-spotted individuals accumulate spurious matches, and assigned identities across the database by two-tier clustering. Pipeline-assisted expert review corrected misidentifications, reducing the number of identities by almost a quarter. On held-out individuals, the pipeline ranked the correct individual first for $95.2\%$ of queries and recovered identities with an ARI of $0.958$. Without modification, it reached $95.1\%$ and $91.7\%$ top-1 accuracy on two datasets of the European fire salamander (Salamandra salamandra) and recognised $93.7\%$ of recaptures in one of them, compared with $38$-$77\%$ for five established programs and $93.9\%$ for visual inspection. Zero-shot local-feature matching thus offers amphibian monitoring a re-identification tool that needs no training data, transfers between related species, scales to databases of over a thousand individuals, and can audit existing databases before capture-recapture analysis.
Optical and wave-based analog computing has re-emerged as a route to accelerating linear algebra, with metamaterials and photonic circuits shown to invert matrices and solve integral equations. However, it has remained unclear which problems such passive structures can represent, and at what cost, once encoding, precision, conditioning and readout are accounted for. Here, we show that every polynomially conditioned sparse linear system can be compiled into a three-dimensional dielectric -- a cavity filled with lossless, isotropic material of bounded permittivity -- whose time-harmonic response encodes its solution. For $m$ nonzero coefficients, the volume of the structure and the cost of writing it down and reading out the answer scale as $\tilde O(m^{3/2})$, coefficients require only $O(\log m)$ bits, and every field that approximately satisfies Maxwell's equations decodes to an approximate solution. We prove the construction for the continuum equations and give a computer-assisted version for the Yee discretization underlying finite-difference solvers. Read in reverse, it implies that electromagnetic simulation is, up to this overhead, as hard as general sparse linear algebra: a sufficiently fast general-purpose Maxwell solver would yield a faster sparse linear solver.
Handwritten text recognition (HTR) for the registers of Pope Honorius III depends on modern expert transcriptions, which exist only for regnal years 1-2 of Reg. Vat. 9. For the remaining registers, nineteenth-century scholarship offers Pressutti's regesta and partial editions. On fol. 1-203, where both layers exist, we measure what this legacy layer can contribute, on transcriptions that we first audit against it, and find a division of labor across three tiers. At the orthographic tier, raw editions are at best neutral for adapting a recognizer to the second regnal year and harmful without replay, whereas projection onto curial spelling with replay of modern source lines turns them into zero-label adaptation targets: pooled over all 134 year-2 sides, the WER falls by 1.50 points (95% CI -1.84 to -1.15), closing 33% of the gap to an oracle; the spelling changes at the year split, through the scribe or the transcriber, whereas the hand changes visibly only at fol. 191r. At the lexical tier, the corpus-wide legacy lexicon contains 82.2% of the out-of-vocabulary words of unedited entries and flags recognition errors with 66.7% precision, and labels from pre-trained medieval NER models, distilled into the recognizer's NER head, reach F1 82.1% on an expert validated benchmark, level with the best tagger applied after recognition. At the topological tier, Pressutti's regests, the closing datum clauses, and the regests' dates converted to the chancery's Roman form find 73.9% of entry starts in the recognized line stream, and edition agreement estimates a CER of 7.80% beyond fol. 203. Antiquarian scholarship thus cannot replace modern transcription, but it can steer, check, and segment HTR across the untranscribed registers.
New technologies are usually adopted first by people with more formal education. This paper asks whether the education gap in generative AI use in 2025 is smaller or larger than the internet's gap in the same country in the year when overall internet use was at a similar level. It uses public Eurostat survey data on ICT usage by individuals aged 16 to 74. Each country's 2025 rate of generative AI use in the last three months is matched to the internet year whose overall last-three-months use was closest, within 5 percentage points, and the gap between people with high (ISCED 5 to 8) and low (ISCED 0 to 2) completed education is measured as a log ratio and as a percentage-point difference. Sixteen of 35 countries could be matched; most of the others were early internet adopters. In the matched countries, the generative AI gap was smaller in 13 of 16 countries on the log ratio (median change -0.625, 95% bootstrap interval -0.969 to -0.291, two-sided sign test p = 0.021) and in 15 of 16 on the percentage-point gap (median change -27.6 points, interval -32.2 to -16.8, p = 0.0005). The point-gap result keeps its sign in every generative-AI-versus-internet sensitivity analysis with 8 or more countries, including comparisons restricted on both sides to ages 25 to 54 and 55 to 74, but its size is sensitive to matching that takes education composition into account (about -10 to -11 points in 7 and 8 countries). The ratio result is fragile: it fails the decision rule when countries matched at their first internet observation are removed (9 of 11, p = 0.065). The analysis follows an unpublished plan whose timing cannot be verified, so it is not presented as pre-registered. The results describe unadjusted education contrasts in a non-random set of countries, not an education effect, and do not generalise to the EU as a whole.
Weixin Chen Hong Kong Baptist University, Yuhan Zhao, Jingyuan Huang Rutgers University, Zihe Ye Rutgers University, Mingxuan Ju Snap Inc., Tong Zhao Snap Inc., Neil Shah Snap Inc., Li Chen Hong Kong Baptist University 等 9 位作者
The evolution of recommender systems has shifted from traditional collaborative filtering to LLM-based agentic systems, which rely on semantic user and item memories to make predictions. However, existing agents maintain these memories in isolation. This overlooks crucial collaborative signals, such as user-item co-engagements and peer relationships across the community, which significantly limits their ability to uncover hidden preferences and accurately infer user needs, particularly for data-sparse users. To bridge this gap, we introduce collaborative memory, a paradigm that connects isolated semantics to enable the sharing of relational insights. Yet, naively utilizing collaborative memory causes severe context overload and introduces noise to downstream LLMs, alongside prohibitive computational costs. To resolve this, we propose MemRec, a framework that architecturally decouples memory management from reasoning. MemRec introduces a dedicated, lightweight language model LM_Mem to efficiently manage and synthesize a dynamic collaborative memory graph in the background. It provides only distilled, high-signal contexts to a downstream, heavyweight large language model (LLM_Rec) for the final recommendation. Extensive experiments on four benchmarks demonstrate that MemRec achieves state-of-the-art performance. Code: https://github.com/rutgerswiselab/memrecHomepage: https://memrec.weixinchen.com
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cs.CL · cs.AI
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Lingxiao Tang, He Ye University College London, University of London, Zhaoyang Chu, Muyang Ye, Zhongxin Liu Zhejiang University, Xiaoxue Ren Zhejiang University, Lingfeng Bao
Code LLMs still struggle with code execution reasoning, especially in smaller models. Existing methods rely on supervised fine-tuning (SFT) with teacher-generated explanations, primarily in two forms: (1) input–output (I/O) prediction chains and (2) natural-language descriptions of execution traces. However, intermediate execution steps cannot be explicitly verified during SFT, so the training objective can reduce to merely matching teacher explanations. Moreover, training data is typically collected without explicit control over task difficulty. We introduce ExecVerify, which goes beyond text imitation by incorporating verifiable white-box rewards derived from execution traces, including next-statement prediction and variable value/type prediction. Our work first builds a dataset with multiple difficulty levels via constraint-based program synthesis. Then, we apply reinforcement learning (RL) to reward correct answers about both intermediate execution steps and final outputs, aligning the training objective with semantic correctness at each execution step. Finally, we adopt a two-stage training pipeline that first enhances execution reasoning and then transfers to code generation. Experiments demonstrate that a 7B model trained with ExecVerify achieves performance comparable to 32B models on code reasoning benchmarks and improves pass@1 by up to 5.9% on code generation tasks over strong post-training baselines.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Alphaeus Dmonte, Roland R Oruche, Marcos Zampieri George Mason University, Prasad Calyam, Isabelle Augenstein University of Copenhagen
The large and ever-increasing amount of data available on the Internet, coupled with the laborious task of manual claim and fact verification, has sparked interest in the development of automated claim verification systems. Several deep learning and transformer-based models have been proposed for this task over the years. With the introduction of Large Language Models (LLMs) and their superior performance in several NLP tasks, we have seen a surge of LLM-based approaches to claim verification along with the use of novel methods such as Retrieval Augmented Generation (RAG). In this survey, we present a comprehensive account of recent claim verification frameworks using LLMs. We describe the different components of the claim verification pipeline used in these frameworks in detail, including common approaches to retrieval, prompting, and fine-tuning. Finally, we describe publicly available English datasets for this task.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表
AI 速览：一本以教科书方式系统介绍多臂老虎机（multi-armed bandits）的入门著作，覆盖IID奖励、对抗性奖励、上下文老虎机及与经济学相关的扩展。
Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. This book provides a more introductory, textbook-like treatment of the subject. Each chapter tackles a particular line of work, providing a self-contained, teachable technical introduction and a brief review of the further developments; many of the chapters conclude with exercises. The book is structured as follows. The first four chapters are on IID rewards, from the basic model to impossibility results to Bayesian priors to Lipschitz rewards. The next three chapters cover adversarial rewards, from the full-feedback version to adversarial bandits to extensions with linear rewards and combinatorially structured actions. Chapter 8 is on contextual bandits, a middle ground between IID and adversarial bandits in which the change in reward distributions is completely explained by observable contexts. The last three chapters cover connections to economics, from learning in repeated games to bandits with supply/budget constraints to exploration in the presence of incentives. The appendix provides sufficient background on concentration and KL-divergence. The chapters on "bandits with similarity information", "bandits with knapsacks" and "bandits and agents" can also be consumed as standalone surveys on the respective topics.