Artificial Intelligence · Cross-listed from math.GR
An Exact Counterexample to Carlson's Associated-Prime Depth Conjecture from a Group of Order 128
Xinan Dai, Wenhao Deng, Yingdong Shi et al. (+2 more)
In Question~3.1 of his 1995 paper on depth and transfer, Carlson asked whether the depth of a finite-group
cohomology ring is always realized by the dimension of one of its associated
primes. We give a negative answer. Let \[ G=\SG{128}{859},\qquad k=\kbar. \] An exact presentation certificate proves that $\depth H^*(G;k)=2$. Okuyama's associated-
prime theorem would convert an associated
prime of dimension two into a rank-two elementary abelian subgroup $E\leq G$ satisfying $\depth H^*(C_G(E);k)=2$. We enumerate all $75$ rank-two elementary abelian subgroups of $G$ and obtain six centralizer types. Duflot's
theorem gives depth at least three for four types, while exact ideal-quotient certificates exhibit regular sequences of length three for the remaining two. Hence every rank-two centralizer has cohomological depth at least three, so $H^*(G;k)$ has no associated
prime of dimension two. The finite group presentation, the three
cohomology-ring presentations, the enumeration summary, and the exact algebraic certificates are included for independent verification.
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All authors Xinan Dai, Wenhao Deng, Yingdong Shi, Tailin Wu, Yuchen Yang
Artificial Intelligence
E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios
Weihuang Zheng, Tianyuan Zou, Eileen Ye et al. (+5 more)
Large
Language Models (LLMs) are increasingly deployed as
agents that interact with stateful environments over multiple steps: gathering hidden information, composing tool calls, and committing state changes. We refer to this capability as multi-step tool use. Existing
benchmarks have advanced tool-use
agent evaluation, but often focus on isolated API calls, short trajectories, or settings that are difficult to scale or control. We introduce E-Bench, a fully synthetic
benchmark with 323 state-changing tasks across three product domains: Honor of Kings, QQ Music, and Tencent Meeting. E-Bench decouples environment synthesis from task synthesis: graph-guided database filling builds reusable, orphan-free product environments, while generator-solver asymmetry creates tasks with both an information gap and a tool gap, requiring
agents to discover hidden data and compose multiple tool calls before changing state. Outcomes are graded deterministically by database-state diffs. Since both environments and tasks are synthetic, E-Bench is controllable at the environment level and scalable at the task level. Benchmarking 11 cutting-edge LLMs shows that multi-step tool use remains challenging: Pass^3 stays below 60% for the strongest models, and even with code execution in the E-Bench-Code extension, reliability (Pass^3) remains below 70%.
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All authors Weihuang Zheng, Tianyuan Zou, Eileen Ye, Alphet Liu, Youyong Kong, Ya-Qin Zhang, Duran Zheng, Maxm Pan
Artificial Intelligence · Cross-listed from cs.CR
The Illusion of Secure LLM Code: Closing the Security Gap via Iterative Reprompting
Ishpuneet Singh, Shreyas Mahajan, Gurjot Singh et al. (+1 more)
Large
Language Models (LLMs) are increasingly integrated into software development workflows, yet their ability to autonomously generate secure authentication code remains uncertain. This paper evaluates the security architecture of authentication systems generated by five prominent AI coding assistants through a bi-modal assessment framework combining static code analysis and dynamic penetration testing, mapped to NIST SP 800-63B guidelines. The study examines model behavior across four prompting strategies Basic, Secure, NIST-Based, and Reprompting to reflect varying levels of developer guidance. Empirical results demonstrate that code generated from functional or generically secure
prompts consistently omits critical protections, particularly concerning brute-force resistance, session management, and robust password handling. While providing explicit, single-shot NIST context significantly improves compliance, the findings reveal that this remains structurally inadequate. Instead, iterative Reprompting: forcing models into a contextual self-
auditing loop is strictly required to achieve a comprehensive, defense-in-depth security architecture. Ultimately, this study proves that current AI coding assistants do not produce secure-by-default applications, dictating that enterprise deployments must transition from single-shot
prompt engineering to continuous, standards-driven verification pipelines.
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All authors Ishpuneet Singh, Shreyas Mahajan, Gurjot Singh, Maninder Singh
Artificial Intelligence
Wendi Deng, Hang Du, Guoshun Nan et al. (+11 more)
Multimodal large
language models exhibit capabilities on
reasoning tasks, yet often produce flawed intermediate steps while yielding correct final answers. This behavior undermines interpretability and reliability, suggesting reliance on spurious shortcuts rather than faithful
reasoning. Although efforts have explored step-level supervision, distinguishing decisive steps from redundant ones remains challenging. We propose $O^2$-CritiCuRL, a novel curriculum reinforcement learning framework that introduces critical-step awareness through an iterative offline-online paradigm. In the offline stage, $O^2$-CritiCuRL conducts multi-rollout analysis over step-annotated trajectories to estimate step-level importance, allowing the framework to distill critical
reasoning steps and filter out redundant ones. In the online stage, we employ a progressive step-level reinforcement learning strategy, where truncated chains guide the model to infer missing steps and refine its
reasoning, thereby sharpening its focus on critical steps and overcoming the limitations of static supervision. Extensive experiments on
multimodal reasoning benchmarks show that our method achieves state-of-the-art performance while delivering superior training and inference efficiency. Code is available at https://github.com/kk0013/CritiCuRL.
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All authors Wendi Deng, Hang Du, Guoshun Nan, Haokun Tian, Jiaqi Yu, Xinlei Cao, Jaile Li, Jingfeng Chen, Ling Deng, Ting Li, Hao Yang, Jun Liu, Xudong Jiang, Sicong Leng
Artificial Intelligence
Offline-to-Online Creative Optimization with Generative Models and Adaptive Testing
Kevin Lee, Benjamin Letham, Zhiyuan Jerry Lin et al. (+5 more)
Ad creative optimization is increasingly constrained by evaluation rather than generation. Generative models can produce many plausible creatives, but reliable evaluation requires online experiments, in which only a limited slate can be tested. We study how to use data from historical A/B tests to generate and select the candidates in that slate. We developed and deployed a performance-driven offline-to-online workflow that guides creative generation with a predictive model as an inference-time critic. In the offline phase, we use a predictive model trained on historical experiments to rank and refine variants created by a generative model. A final test slate is then deployed in an online adaptive experiment. In a 50-arm field experiment, we found that the best creative generated with this method yielded 45.1% higher engagement than the best human-authored creative. Two additional experiments showed the same upper-tail pattern, with lifts of 46.7% and 36.2%. We found that despite the predictive model being too noisy to directly identify the best creative offline, it effectively guides the generative model toward creating strong candidates that can be efficiently evaluated in an adaptive experiment. The results suggest a design principle for creative optimization with generative models: use predictive models to guide generation of a slate to test, judge the slate by whether it contains high-performing candidates at a feasible test size, and use adaptive experiments to select among candidates while limiting traffic lost to weak arms.
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All authors Kevin Lee, Benjamin Letham, Zhiyuan Jerry Lin, Elodie Samson, Eric Onofrey, Poppy Zhang, Shawndra Hill, Eytan Bakshy
Artificial Intelligence
Compute Globally, Materialize Locally: The Memory Contract of Sparse Event-KV
Zefeng Cai and Zerui Cai
Long-horizon
agents increasingly reuse their KV cache as
memory: a serving system keeps a subset of cached entries and drops the rest. Eviction and episodic-
memory schemes therefore rest on a premise rarely tested directly, that a retained event is still informative once the observations that produced it are gone. We test it by omitting one earlier observation from what is served, across otherwise identical
agent histories. Among items sensitive to that observation, the answer overwhelmingly follows the omitted value, though no served span says which value is correct. We call this semantic materialization: a downstream event's cached rows act as an independently servable view of computation whose inputs are gone. It can also be written on purpose. A deliberately phrased, answer-free event raises donor-aligned recovery from 6% to 51% on Qwen3-8B without ever naming the value, whereas passively harvesting natural mentions from long-term dialog yields no detected advantage. What such a row carries is specific and bounded. Compact state survives, larger payloads decay toward chance, and whether a construction writes at all turns on phrasing rather than on meaning alone, so two phrasings the model comprehends equally well can diverge sharply. The result is a
memory contract for sparse event-KV serving: what to write, where it lands, and what survives once the source is gone. For anyone who evicts the corollary is that dropping a source event and observing no accuracy loss does not show the source was unnecessary.
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Artificial Intelligence
Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems
Mingzhou Fan, Siyuan Xu and Mingxuan Yuan
Large
language models (LLMs) enable autonomous
agents for
reasoning, planning, and tool use. Recent systems increasingly organize these
agents as graphs of specialized, interconnected nodes. Although graph-based orchestration supports flexible decomposition and coordination, it creates a key challenge:
attention allocation. As workflows grow, existing approaches often execute graph components uniformly, wasting resources on irrelevant or low-impact tasks. We introduce
Attention Orchestration, a paradigm that extends
Transformer-style
attention from
token representations to workflow-level
agent coordination. Our framework, Adaptive Goal-aware
Attention Orchestration (AGAO), dynamically estimates
agent importance based on user objectives, graph dependencies, and computational constraints. AGAO combines three components: (1) goal-aware
attention, measuring semantic relevance between user goals and
agent capabilities; (2)
topology-aware
attention, modeling structural dependencies in
agent graphs; and (3) resource-aware
attention, allocating budgets and execution priorities across heterogeneous
agents. Together, these mechanisms transform static
agent graphs into adaptive systems that focus computation on goal-critical
reasoning paths. Experiments across diverse multi-
agent workloads show that AGAO improves task effectiveness while reducing unnecessary computation, latency, and
token consumption compared with existing graph-based execution strategies. Our work establishes
Attention Engineering as a direction for scalable, intelligent multi-
agent systems. Code: https://github.com/MingzhouFan97/AGAO.
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Artificial Intelligence
SpecAHD: Localize to Specialize for Automated Heuristic Design in Large-Scale Routing Problems
Kezhao Lai, Yutao Lai and Hai-Lin Liu
LLM-based automated heuristic design (AHD) typically scores executable programs on complete instances or within fixed solver components. In large-scale routing problems, localized reconstruction reduces the size of each optimization task, but repair regions within the same incumbent can exhibit substantially different structures. One construction rule must therefore compromise across them. In this paper, we propose SpecAHD, a coupled bilevel framework for within-instance specialization. An upper-level search learns where to expose bounded repair regions, while a lower-level search evolves a complementary repertoire of executable heuristics for the induced repair tasks. The upper-level program determines the repair tasks seen by the lower level, while checked repair outcomes determine how upper-level programs are evaluated. The lower-level objective favors heuristics that perform well on average or solve tasks that the current repertoire handles poorly. For the repair tasks induced by a fixed upper-level program and a fixed lower-level candidate pool, this objective is monotone submodular, allowing greedy repertoire selection with a (1-1/e) approximation guarantee. Across four routing problems and multiple LLM backbones, SpecAHD reduces held-out objective cost by up to 57.7% against the strongest competing AHD baseline and outperforms the per-instance baseline envelope on most public instances.
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Artificial Intelligence · Cross-listed from cs.CL
An empirical investigation into the properties of standard word embeddings
Salomon Kabongo
The
embedding of word sequences into continuous vector spaces has been one of the most important developments in Natural
Language Processing in the recent past. Such
embeddings have found application in areas such as Automatic
Speech Recognition, Machine Translation, Sentiment Analysis and many more. This essay reviews the various mechanisms that have been proposed for the calculation of word
embeddings, investigates popular toolkits and
embedding matrices that are available in the public domain, and experiments with one or more selected implementations to better understand their characteristics. La représentation vectorielle continue de mots a été l'un des développements les plus importants dans le domaine du traitement automatique du langage naturel au cours des dernières années. Ces représentations ont trouvé application dans des domaines tels que la reconnaissance vocale, la traduction automatique, l'analyse des sentiments, etc. Ce travail passe en revue les différents mécanismes proposés pour le calcul de ces vecteurs de mots, étudie les kits d'outils populaires et les
matrices disponibles publiquement en ligne, et expérimente avec une ou plusieurs implémentations sélectionnées pour mieux comprendre leurs caractéristiques.
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Artificial Intelligence · Cross-listed from cs.HC
Plans Work in Mysterious Ways: Evaluating a Plan Mode for Spreadsheet Agents
Aayush Kumar, Avik Dutta, Sumit Gulwani et al. (+3 more)
Plan Modes have become standard features in agentic programming tools, allowing users to gain transparency and control by working with the
agent to develop a plan before task execution. However, it remains unclear whether the benefits of this feature translate to end-user programming environments such as spreadsheets. Since spreadsheet programmers tend to work iteratively and care less about technical correctness, upfront planning may not fit into their workflows as easily. In this paper, we build a prototype of a Plan Mode for spreadsheet programming and evaluate it against a non-planning baseline through a within-subjects user study (N=24). We found that despite similar task outcomes with both tools, using Plan Mode led to a reduction in refinement and a better perception of the tool across dimensions of creativity support and human-machine collaboration. We discuss the implications of these results for the future design of Plan Modes, and for the broader role of human-AI planning in end-user programming.
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All authors Aayush Kumar, Avik Dutta, Sumit Gulwani, Gustavo Soares, Advait Sarkar, Emerson Murphy-Hill
Artificial Intelligence · Cross-listed from cs.LG
CALMRec: Causally Aligned Language Memory for Long-Horizon Recommendation
Gengyu Zhan
Large
language models (LLMs) can summarize heterogeneous user evidence in natural
language, but current LLM recommenders often collapse enduring preferences, transient intent, and exposure-induced behavior into one profile. This makes recommendation vulnerable to feedback loops: repeated exposure is mistaken for preference, immediate clicks dominate delayed satisfaction, and fluent explanations need not reflect the ranking decision. We propose our method, a model-agnostic framework for long-horizon recommendation. Our method uses a frozen
multimodal language model to convert item content and feedback into evidence-grounded semantic atoms, then maintains separate short-term, long-term, and exposure
memories. Propensity-weighted updates reduce policy-induced exposure bias, while a conservative offline critic reranks candidates for delayed satisfaction under a behavior-support constraint. Explanations use only influential evidence atoms and are checked by counterfactual deletion. We provide an identification result and evaluate the framework in e-commerce-like, news-like, and short-
video-like environments. Across ten seeds, our method improves discounted long-term value over the strongest alternative by 6.1%, 7.6%, and 6.7%, respectively. Twenty-seed paired ablations show significant value drops after removing propensity correction (0.739 +/- 0.191) or conservative support regularization (0.523 +/- 0.234). A frozen instruction
language model also more than doubles semantic-atom NDCG over TF-IDF on a held-out paraphrase
benchmark.
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Artificial Intelligence · Cross-listed from quant-ph
Neural Network Learning of One-Bit Protocols for Qubit Measurement Simulation
Josep Escrig, Mani Zartab, Giulio Gasbarri et al. (+3 more)
Communication complexity provides a natural framework for quantifying the classical resources required to reproduce
quantum statistics. In the
qubit prepare-and-measure scenario, two classical bits have been shown to be necessary and sufficient to simulate arbitrary
qubit states and arbi- trary
quantum measurements exactly. However, this result does not exclude the possibility that restricted families of measurements may admit accurate 1-bit classical approximations. We use a neural network procedure to demonstrate that a single bit can achieve high average accuracy for specific measurement families. A performance analysis of our neural network reveals that symmet- ric measurements with uniformly weighted elements, such as those forming regular polyhedra, are particularly amenable to this restricted communication. By analyzing the patterns learned by the neural network, we derive an analytical protocol that is extremely accurate for finite information- ally complete symmetric configurations and becomes exact in the limit of a continuous isotropic measurement.
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All authors Josep Escrig, Mani Zartab, Giulio Gasbarri, Estel Ferrer, Ramon Muñoz-Tapia, Gael Sentís
Artificial Intelligence · Cross-listed from cs.DB
Extending Desbordante with Probabilistic Functional Dependency Discovery Support
Ilia Barutkin, Maxim Fofanov, Sergey Belokonny et al. (+2 more)
Data profiling aims to extract complex patterns from data for further analysis and use that data in domains such as data cleaning, data deduplication, anomaly detection, and many more. Functional dependencies (FDs) are one of the most well-known patterns. However, they are poorly suited for these tasks, as real data is usually dirty, and the rigid definition of FDs does not allow
algorithms to locate them. For this reason, there are several formulations aimed at relaxing FDs to support dirty data, with approximate functional dependency (AFD) being the most popular one. Another formulation is the Probabilistic Functional Dependency (pFD), which we aim to support inside Desbordante - a science-intensive, high-performance and open-source data profiling tool implemented in C++. However, pFDs are relatively poorly studied, compared to AFDs. In this paper we study pFDs, both analytically and empirically. We start by assessing how different pFDs and AFDs are by studying cases in which pFDs have an edge over AFDs. Then, we implement the
algorithm for pFD discovery, as well as study its run time and
memory consumption. We also compare it with an AFD discovery
algorithm. Lastly, we study the output of both
algorithms to learn whether or not it is possible to use AFD discovery
algorithm to get pFDs and vice versa.
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All authors Ilia Barutkin, Maxim Fofanov, Sergey Belokonny, Vladislav Makeev, George Chernishev
Artificial Intelligence · Cross-listed from cs.LG
Variational-Ising-Attention:Tailored Attention Matters for Science
Rui Wang
Attention enables context modeling via query-key scoring with softmax normalization. Driven by industrial long-context demands, mainstream research has converged toward sparsity and efficiency, yet softmax's independence assumption persists. For scientific tasks unburdened by long-
token constraints, however, richer structured coupling may often be essential, making tailored
attention both viable and more appropriate. To this end, we propose Variational-Ising-
Attention (VIA), which augments softmax normalization with an interacting Ising model;
attention patterns emerge from learnable pairwise couplings via variational mean-field inference, extending
attention from a ranking over isolated items to a collective state over interacting entities. We instantiate VIA on retrosynthesis reaction center prediction and, as a controlled internal ablation, on
protein residue contact prediction, two structured prediction tasks governed by cooperative constraints: cooperative bond-breaking for retrosynthesis and inter-residue interactions for
protein contact prediction. Comprehensive experiments across model variants, coupled with mechanistic analyses, demonstrate that VIA substantially outperforms standard softmax
attention. More broadly, our findings suggest that for scientific problems, the optimal solution is not general-purpose efficiency, but appropriately tailored
attention aligned with intrinsic domain structure. This work provides a theoretically grounded and empirically validated instantiation of this paradigm.
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Artificial Intelligence · Cross-listed from cs.DB
Order in Desbordante: Techniques for Efficient Implementation of Order Dependency Discovery Algorithms
Yakov Kuzin, Dmitriy Shcheka, Michael Polyntsov et al. (+3 more)
Science-intensive data profiling focuses on discovery and validation of various patterns in
datasets. This study considers discovery of one such pattern - order dependency (OD). Simply put, OD states that some list of columns is ordered according to another one. It is of use for database query optimization, data cleaning and deduplication, anomaly detection, and much more. Existing discovery methods have approached this problem solely from the algorithmic standpoint, without focusing on the implementation side. At the same time, this problem is very computationally intensive, and therefore this part should not be ignored, as it brings ODs closer to industrial use. In this paper, we study two
algorithms for OD discovery which target different OD axiomatizations - FASTOD and ORDER. We start by reimplementing these
algorithms in C++ in order to speed them up and lower their
memory consumption. We then analyze their bottlenecks and propose several techniques which improve their performance even further. To perform evaluation, we have implemented these
algorithms inside Desbordante - a science-intensive, high-performance, and open-source data profiling tool developed in C++. Experiments have demonstrated a performance improvement of up to 3x obtained by reimplemented versions, and, with the application of our techniques, up to 10x.
Memory consumption has been lowered by up to 2.9x.
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All authors Yakov Kuzin, Dmitriy Shcheka, Michael Polyntsov, Kirill Stupakov, Mikhail Firsov, George Chernishev
Artificial Intelligence · Cross-listed from cs.SE
Where Is the Tradeoff in Using Third-Party API Routers for Agentic Software Development?
Donghao Fu, Jingxin Li, Xue Jiang et al. (+1 more)
Third-party API routers have become a common layer that unifies access across increasingly diverse LLM providers. In coding-
agent workflows, high-autonomy operation is widely adopted because it reduces interaction overhead. As a result, a third-party API router, which sits between the
agent and the upstream provider, inevitably occupies the trusted path. It can inspect and modify every request and response, yet no mechanism verifies
alignment between the provider's output and the repository-level actions ultimately executed by the
agent. Consequently, client-side permission mechanisms may become ineffective in practice. Whether this control gap produces real, hard-to-detect effects on software development tasks remains empirically unmeasured. In this paper, we conduct an empirical study of router-side injection in coding
agents, examining four intervention levels of increasing subtlety: Response Substitution (L1), Response Append (L2), LLM-Polished Injection (L3), and LLM-Polished with Distribution
Alignment Injection (L4). Moreover, we develop SIDEL, a framework for trace recording, replay, injection, and defense evaluation, with a curated
dataset of 400 samples. We evaluate four representative coding
agents, and further evaluate whitelist-based execution control and LLM review. Router-side intervention substantially alters repository-level actions and remains difficult for existing client-side safeguards to detect. Without additional mitigations, all evaluated
agents achieved a defense success rate of 0 percent across all injection levels. Client-side mitigations and reactive reviews improve resistance but do not fully restore end-to-end control, motivating provider-side output-integrity guarantees. Our code is available at https://github.com/Riyasushin/SIDEL.
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All authors Donghao Fu, Jingxin Li, Xue Jiang, Yihong Dong
Artificial Intelligence · Cross-listed from cs.CR
DualityCert: Verifier-Gated Language-Model Repair of Broken Duality Claims in Quantum Field Theory
Xingyang Yu
We present DualityCert, a symbolic verifier for candidate Seiberg-duality claims in four-dimensional N=1 quiver gauge theories. The verifier evaluates 't Hooft anomaly matching, superpotential R-charge consistency, central-charge matching, and a bounded
chiral-ring proxy. A claim that passes receives a consistency certificate, which states that no tested inconsistency was found, not that the duality is proven. We use the verifier as a repair environment for
language-model
agents, which receive a deliberately broken claim and must edit it until it certifies. On a preregistered
benchmark of 145 broken claims, with the analysis fixed before the first confirmatory model call, verifier-gated retry improves final repair success over a single attempt by +8.3 percentage points (pp) on deepseek-chat and +7.1 pp on qwen-plus (Holm-adjusted p<0.002). Under an equal budget of eleven attempts, the stop-first strategy portfolio underperforms independent verifier-filtered resampling by 10.3 percentage points on deepseek-chat but outperforms it by 14.7 points on qwen-plus, reversing the ordering of the two tested verifier-exploitation policies across the two confirmatory models. On qwen-plus, category-level verifier feedback is worth +8.7 pp over content-free retry, and interpretable obligation identities alone are worth +6.4 pp over structurally identical masked feedback. Neither effect is detected on deepseek-chat. Separately, a preregistered MiniMax-M2.5 extension again finds an iteration gain and independent verifier-filtered resampling outperforming the strategy portfolio. Which policy is better thus differs between the two models, while every winning policy uses the same cheap certificate. The verifier,
benchmark, protocol, and all per-attempt records are released.
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Artificial Intelligence
Hybrid Advantage Estimation with Unified Critic for VLM Agentic Reinforcement Learning
Wenxuan Zhang, Yuhui Wang, Donggang Jia et al. (+5 more)
Large
Vision-
Language Models (VLMs) now act as
agents in interactive environments, where success requires coherent
reasoning and decision-making across turns. Although end-to-end training in agentic environments can improve such multi-turn decision-making abilities, current methods mainly rely on either
token-wise optimization over concatenated
token trajectories or turn-wise optimization with uniform within-turn credit. In this work, we establish theoretical formulations for the two levels of optimization and derive a hybrid advantage that serves both objectives. Furthermore, with an appropriate choice of discount factor and learning target, we prove that a unified critic model can estimate values for both turn-wise and
token-wise. As such, we propose HyGAE, an actor-critic framework that jointly optimizes
token- and turn-level objectives with the hybrid advantage and unified critic. We conduct extensive evaluations of HyGAE across five multi-turn decision-making environments, where it achieves an average success rate of 91% and a significant improvement of 10% over other methods. Furthermore, we provide an in-depth analysis showing that the exact analytic form of the hybrid advantage and return is crucial for optimization. Project Page: https://wx-zhang.github.io/hygae-web/.
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All authors Wenxuan Zhang, Yuhui Wang, Donggang Jia, Xiaoqian Shen, Jian Ding, Ivan Viola, Jürgen Schmidhuber, Mohamed Elhoseiny
Artificial Intelligence · Cross-listed from cs.RO
Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models
Liangyu Li, Qingwen Liu, Mingqing Liu et al. (+1 more)
Controllers based on sampling and latent world models assign a predicted terminal cost to each candidate action sequence, choose the minimum, execute its first action block, and replan. This rule can fail even when the terminal cost perfectly and accurately reflects the true task objective in the physical world. Residual prediction error can give an infeasible sequence an anomalously low cost, and a larger proposal pool gives such errors more chances to outrank feasible alternatives. We call this conditional failure proposal overgeneration. In Cube candidate execution audits, increasing the total proposal budget from 72 to 288 reduces the feasibility of selection by minimum latent cost from .375 to .062 for position targets and from .344 to .031 for targets defined by position and yaw, although every larger pool contains a feasible sequence. We introduce Adjacent Set Action Reconstruction (ASAR). Among proposals with low cost, ASAR identifies an adjacent set using standardized early action prefixes and reconstructs a full action sequence through locally weighted aggregation with a light anchor from the sequence with minimum cost. On a Carry and Release evaluation set of 75 queries, Kernel ASAR improves event completion success over matching selection by 28.0, 24.0, and 18.7 percentage points under latent cost and by 18.7, 20.0, and 17.3 points under a trajectory reachability cost at 72, 144, and 288 proposals. Analysis of finite proposal pools characterizes selection risk from the lower tail, separation by a related radius support statistic, and sequence containment under an explicit local feasibility condition.
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All authors Liangyu Li, Qingwen Liu, Mingqing Liu, Wen Fang
Artificial Intelligence
Are You Still the Agent I Authorized? Earned Authority under a Fixed Ceiling for Evolving Agents
Zhaoxi Zhang and Xiaomei Zhang
Long-lived AI
agents increasingly evolve after deployment by retaining experience, acquiring skills and tools, revising workflows, delegating work, and moving across task phases. This improves adaptation but creates a distinct authorization problem. Tool-enabled
agents can turn model errors and
prompt injections into consequential external actions; when
evolution occurs under a live grant, the subject exercising that authority or the context in which it acts may no longer match what the user evaluated.
Evolution can change both the effects reachable under an old grant and the authority required by the task, which may rise, fall, or become incomparable. Existing tool policies constrain actions but do not determine when a grant survives this change. We formulate authorization continuity: when does an existing grant remain valid, how may active authority change, and what boundary must never move? Our state-bound model fixes a transition envelope and an immutable effect ceiling at grant time. The envelope determines whether the grant survives a mutation; below the ceiling, authority may contract freely and expand only under specified evidence conditions. We distinguish requested from realized effects and prove that, under complete mediation, sound effect abstraction, attenuating delegation, and monitor integrity, mutation cannot amplify protected effects beyond the user-issued ceiling.
Agent-produced evidence may allocate authority below the ceiling but cannot raise it. Finally, we map six mutation classes to their authorization consequences.
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Artificial Intelligence
Verification-Notebook Learning for Source-Aware Multimodal Misinformation Detection
Junyuan Tan
Multimodal misinformation verification is challenging because misleading signals may come from different parts of a post and require different forms of evidence. LVLMs are well suited to this task, but their verification performance often depends on the inference procedure applied to each instance. Existing methods improve this procedure through stronger prompting,
retrieval, or deliberation, but rarely retain the verification patterns learned from previous examples. We propose Verification-Notebook Learning (VNL), a non-parametric framework that learns an external verification procedure for a frozen LVLM before inference. VNL builds a compact notebook of decision principles, evidence cues, and recurring pitfalls from prior verification experience. The notebook remains fixed during inference and guides the verification of new examples. Rather than updating model parameters or storing demonstrations, VNL records learned knowledge in an artifact that can be inspected directly. Experiments show that VNL consistently outperforms a range of competitive baselines. Further analyses show that the Verification Notebook improves fine-grained source attribution while remaining compact and interpretable, providing an effective way to accumulate verification knowledge without model training.
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Artificial Intelligence · Cross-listed from cs.CV
D3O: Dynamic Distribution Distillation for Ordinal Regression
Chunlai Dong, Yaojun Hu, Yuyang Xu et al. (+2 more)
Ordinal regression is widely used in scenarios where labels are discrete yet inherently ordered. In practice, however, ordinal labels are often obtained by discretizing underlying continuous semantics through subjective human judgment, resulting in ambiguous boundaries and annotation noise. Such uncertainty challenges existing methods that rely on fixed supervision targets, which may reinforce biased ordering under subjective annotations. To address this limitation, we propose D3O, a dynamic distribution
distillation framework that replaces static supervision with training-driven
evolution of ordinal label distributions via self-
distillation. Specifically, we introduce a contrastive ordinal-aware label enhancement module that leverages
vision-
language alignment to recover refined label distributions capturing both inter-class ambiguity and instance-level uncertainty. Furthermore, we design a CDF-based cross-layer interaction
distillation mechanism to propagate cumulative ordinal structure across network hierarchy, ensuring consistent ordinal geometry in intermediate representations. Extensive experiments on four general ordinal regression tasks demonstrate that our proposed D3O consistently outperforms existing approaches, particularly under severe class imbalance and noisy supervision. These results highlight the effectiveness of dynamic supervision in learning robust ordinal representations beyond fixed targets. The code will be publicly available.
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All authors Chunlai Dong, Yaojun Hu, Yuyang Xu, Haochao Ying, Jian Wu
Artificial Intelligence · Cross-listed from cs.LO
An Unofficial FastLAS Tutorial: A Programmer's Guide
Fabio Aurelio D'Asaro
FastLAS is a scalable system for Inductive Logic Programming (ILP): you give it some background knowledge, a
language bias, and a set of examples, and it searches for a set of logic program rules (a hypothesis) that explains the examples. These notes are a hands-on introduction to writing FastLAS programs. They are organised as a programmer's guide: syntax first, then a ladder of worked, numbered examples of increasing difficulty. Every self-contained example here has been run against FastLAS 2.2.0 and shows the tool's actual output. We keep theory to the minimum needed to write correct programs; throughout, set-off notes flag where FastLAS differs from its sibling system ILASP, and where the two learning
algorithms (–opl and –nopl) behave differently. The document is intended as an unofficial tutorial to FastLAS 2.2.0, not as an official
language specification.
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Artificial Intelligence · Cross-listed from cs.SI
GTIN: A Unified Scalable Framework for Joint Event and Time Prediction in Temporal Graphs
Mohammad Ostadmohammadi, Sepehr Kazemi and Hamid R. Rabiee
Temporal graphs are increasingly used to model dynamic systems across diverse domains, including social, financial, and traffic networks. Predicting both what the next event will be and when it will occur in these systems is crucial for understanding and anticipating complex behaviors, but has not been studied much yet, especially in large-scale graphs. To address this gap, we propose a scalable unified mathematical framework that captures varying degrees of complexity across temporal graphs with events occurring on the edges. Our framework is flexible and expressive enough to accommodate a wide range of network structures and temporal dynamics. Building upon this analysis, we introduce our novel scalable approach for jointly predicting the next event and its occurrence time. Empirical evaluations across multiple
datasets demonstrate that our method consistently outperforms existing techniques, particularly in scenarios involving irregular event patterns and complex temporal dependencies. These findings highlight the potential of our framework as a robust foundation for future research in temporal event prediction.
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Artificial Intelligence · Cross-listed from cs.LG
Neonatal Hypoxic-ischaemic Encephalopathy Classification from the EEG and HRV Signals Using a Conformer based Masked Autoencoder
Shuwen Yu, William P Marnane, Geraldine B. Boylan et al. (+1 more)
In this paper, we propose the MAEConformer, a novel self-supervised learning framework that combines the Conformer architecture with the Masked Autoencoder (MAE) paradigm for large-scale representation learning from unlabelled electroencephalography (
EEG) and heart rate variability (HRV) signals. By integrating convolutional operations with
Transformer-based self-
attention, MAEConformer effectively captures both local temporal patterns and long-range contextual dependencies in physiological time series. To enhance reconstruction fidelity and representation quality, a multi-resolution short-time
Fourier transform (MR-STFT) loss is incorporated alongside the reconstruction objective, enabling the model to jointly learn temporal and spectral characteristics across multiple scales. Modality-specific
EEG and HRV MAEConformer models were pretrained on 6,030h and 4,868h of unlabelled recordings, respectively, and subsequently transferred to expert-annotated downstream tasks. Experimental results demonstrate that the learned representations provide strong transferability and data efficiency. In
EEG-based hypoxic ischemic encephalopathy (HIE) severity classification, the pretrained MAE-
EEG model achieved test AUCs of 97.19% and 96.56% for binary and four-class classification tasks, respectively, outperforming a range of state-of-the-art supervised and self-supervised baselines. On the HRV-based HIE severity classification task, MAE-HRV achieved a test AUC of 82.42%, surpassing both self-supervised
Transformer-based and supervised convolutional baselines. These findings demonstrate the effectiveness of MAEConformer for learning robust and transferable representations across multiple physiological modalities.
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All authors Shuwen Yu, William P Marnane, Geraldine B. Boylan, Gordon Lightbody
Artificial Intelligence
ObsDriveBench: Benchmarking Multimodal Understanding under Adverse Weather with Observability Awareness
Qiao Yan, Yihan Wang, Zhenghao Xing et al. (+2 more)
Autonomous driving under adverse weather remains a critical challenge, yet existing
vision-
language benchmarks mainly evaluate under standard conditions, synthetic corruptions, or single modality. As a result, it remains unclear how
vision-
language models behave under real-world adverse weather with multi-modal inputs. We argue that a key difficulty lies in degraded environmental observability: under fog, rain, snow, and low illumination, multi-modal observations become unreliable and cross-modally inconsistent, posing challenges to scene understanding, and subsequent decision-making. To study this, we introduce ObsDriveBench, a real-world multi-modal
benchmark for adverse-weather autonomous driving. Our
benchmark is designed with three capability dimensions: observability awareness, spatial reliability, and risk-aware decision-making, enabling fine-grained diagnosis of model behavior under degraded observations. We construct the
benchmark through observability meta-annotation, scene description, and capability oriented multiple-choice tasks over synchronized camera,
LiDAR, and radar inputs, forming a
benchmark with over 14k training and 13k test questions. Experiments reveal consistent performance degradation of existing
vision-
language models. We further introduce ObsDrive model with normal-weather supervised fine-tuning and adverse-weather reinforcement learning, improving robustness across all three capabilities. The
dataset and evaluation code will be released at \href{https://github.com/russellyq/ObsDriveBench}{ObsDriveBench}.
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All authors Qiao Yan, Yihan Wang, Zhenghao Xing, Jiaqi Xu, Pheng-Ann Heng
Artificial Intelligence · Cross-listed from cs.CR
Mission-Level Runtime Assurance for LLM-Assisted ISR Swarms over a Verification-Aware Fabric
Nikolaos Kekatos, Michael Ioannou, Panagiotis Katsaros et al. (+4 more)
Swarms of LLM-assisted autonomous
robots are increasingly proposed for cooperative intelligence, surveillance, and reconnaissance (ISR) in contested environments. A growing class of their assurance failures arises not within any single platform but across the swarm: individually-compliant actions compose into a mission-level violation: a prohibited objective split across platforms to evade per-platform limits, or a collective budget quietly exceeded. Per-platform guardrails miss these by construction, and contested communications let the violation hide behind lost or delayed evidence. We present a three-tier (platform/squad/mission) compositional runtime-verification framework that decomposes a mission policy into per-
agent and cross-
agent aspects, aggregates per-platform verdicts over a verification-aware messaging fabric, and fuses them with an evidence-aware, two-axis (security x completeness)
algebra whose provenance names the platforms that jointly triggered a violation. Because the fabric makes evidence loss and silence observable, unsupported negative verdicts are downgraded to an explicit unknown rather than reported as mission-wide all-clears. On a simulated ISR mission, an indirect
prompt injection that causes real LLM planners to split a prohibited collection task across four platforms is invisible to every per-platform monitor yet detected compositionally with full provenance; under an injected fault campaign a best-effort central monitor emits silent false all-clears while the verification-aware fabric emits none.
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All authors Nikolaos Kekatos, Michael Ioannou, Panagiotis Katsaros, Alexios Lekidis, Theodoros Nestoridis, Tom Nianios, Dimitrios Nikou
Artificial Intelligence
Delegation Intelligence in Deep Search: A Controllable Framework for Disentangled Capability Diagnosis
Xinhao Yao, Yuanzhuo Liu, Changhao Wang et al. (+6 more)
Deep search is becoming a core capability of modern
agent systems, yet it is typically evaluated solely based on end-to-end answer accuracy. This coupled evaluation paradigm entangles
retrieval quality, long-context comprehension, evidence verification, and tool-use decisions, making it difficult to determine whether a model truly knows when and how to delegate information seeking to search. To this end: (1) We formalize this meta-capability as Delegation Intelligence in deep search and decompose it into complementary dimensions-Search Decision-Making (recognizing information insufficiency and deciding whether, when, and how to search) and Information Synthesis and Verification (aggregating evidence from multiple sources, judging source reliability, and synthesizing information under noisy, potentially
adversarial conditions). (2) To enable disentangled and reproducible measurement, we develop a controllable synthesis pipeline built on document-grounded reverse engineering. This yields a general recipe for constructing controlled deep-search evaluations rather than a single fixed
dataset. (3) As a concrete instantiation, we construct DelegSearchBench, together with a disentangled evaluation protocol that isolates each capability dimension by varying document composition and tool access. (4) Across representative models, we demonstrate that deep-search competence cannot be adequately characterized by final-answer accuracy alone...
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All authors Xinhao Yao, Yuanzhuo Liu, Changhao Wang, Yunfei Yu, Haoran Tan, Yuyao Zhang, Ruifeng Ren, Minlong Peng, Yong Liu
Artificial Intelligence · Cross-listed from cs.CY
Auditing Alignment Controllability in LLMs via Political Axes
Bartol Bućan, Nikola Sočec, Sarah Isufi et al. (+5 more)
Political audits of large
language models (LLMs) usually reduce each to one point on a political compass. But that resting point barely matters in deployment: a model must land somewhere, and what counts is how far, and in which directions, its answers can be steered. That steering runs through the system
prompt: the personalization layer a platform sets, or one induced from a user's history, not necessarily written by hand. We run a dispersion-first stress test of
prompt-based controllability across 12 ideological personas plus an unsteered baseline, 70 Political Compass items, ten replicates, and seven leading LLMs: GPT-5, Claude, Grok, Gemini, DeepSeek, Kimi, and Qwen (63,700 responses). Contextual framing explains roughly 88%-93% of variance on the economic and society axes, model identity under 3%: responses are highly instruction-adjustable. Models do not shift alike: some move more, and some saturate under extreme framings. Conflicting directional-steering results in prior audits resolve once baselines are recognized as non-centered: displacement and proximity diverge, so the effect is
geometric, not differential compliance. Under authoritarian
prompts, models produce similar shifts on the same questions. Political-coordinate audits therefore need steerability audits reporting dispersion,
symmetry, saturation, and refusal floors. We release
prompts,
benchmark data, and code.
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All authors Bartol Bućan, Nikola Sočec, Sarah Isufi, Morena Granić, Luka Hobor, Agneza Krajna, Mihael Kovac, Mario Brcic
Artificial Intelligence · Cross-listed from cs.CL
Novel Claim or Déjà Vu? Rethinking "Contamination-Free” Dynamic Evaluation for Multimodal Automated Fact-Checking
Haorui He, Xinwen Chen, Dacheng Wen et al. (+3 more)
Multimodal automated fact-checking (MAFC) verifies claims by retrieving and
reasoning over external evidence. However, most existing static
benchmarks risk contamination: they primarily consist of outdated claims verifiable using an LLM's internal knowledge without external evidence. This can inflate performance estimates and fail to reflect true capability on novel claims that require up-to-date information. To address this, emerging dynamic
benchmarks collect claims published after LLMs' knowledge cut-off dates, assuming they are uncontaminated. This work revisits this assumption by empirically studying contamination risks in both the state-of-the-art (SOTA) static AVeriTeC
benchmark and our newly constructed dynamic ClaimReview2025Q4
benchmark, as well as their impact on MAFC evaluation. Our experiments yield 16 findings, highlighting three key results: (1) Dynamic evaluation reduces but does not eliminate contamination risks, as 17.09%–29.30% of post-cut-off claims remain potentially contaminated; (2) Many newly published claims can be verified either directly or by synthesizing multiple pieces of public knowledge available before the cut-off; and (3) Contamination can induce statistically significant inflation in MAFC performance, increasing Macro-F1 by up to 11.34 points and distorting system rankings. In light of these findings, we re-evaluate SOTA LLMs under a strictly contamination-controlled setting. Our study provides practical guidelines for trustworthy MAFC evaluation.
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All authors Haorui He, Xinwen Chen, Dacheng Wen, Reynold Cheng, Francis C. M. Lau, Yupeng Li