Artificial Intelligence
A Table Is Worth 64 Tokens: Pixel-level Compression for Multi-Table Document Question Answering
Iñigo Alonso and Mirella Lapata
Answering questions over real-world documents requires processing long inputs that interleave text with tables. Optical context compression, which represents context as images, promises to reduce
token cost, but its effect on table understanding remains unclear. We study pixel-level table compression for question answering over documents with multiple tables, evaluating five VLMs across two
benchmarks and five visual-
token budgets. Representing tables as images at native resolution matches text in both performance and efficiency, but downscaling them makes models compensate the loss in readability with longer, less effective
reasoning traces that cancel the expected savings. Highly downscaled tables, however, preserve enough signal to identify whether they are relevant to a question. We exploit this asymmetry with a training-free, two-step method: the model first identifies the tables needed to answer a question from a pixel-compressed context, and then reasons over those at native resolution. On long documents, our method saves 41% of total
tokens and gains 7 accuracy points over single-step QA with native resolution tables. It also uses 15% fewer
tokens than the most efficient single-step compressed configuration, with no accuracy loss.
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Artificial Intelligence
Counterfactual Bias Testing for Application Tracking System
Sai Yashwant, Shruti Bansal, Anurag Dubey et al. (+4 more)
Automated candidate-job matching systems are increasingly classified as high-risk AI under emerging regulation, yet
auditing them for demographic bias is expensive: classical correspondence-audit studies require hand-crafted resumes and manual submission, which does not scale to fast pipeline retraining cycles. This paper presents a general, reusable methodology that (1) uses task-specialized LLM
agents to synthesize identity-neutral base resumes and inject controlled demographic treatments across five protected-characteristic axes (sex/gender, age, residence,
language, disability), producing a K x (1+N) correspondence-audit
matrix; (2) qualitatively flags inferred protected characteristics per an EU AI Act-aligned
prompt; (3) ranks candidates against a job description via a fine-tuned sentence-
embedding model and cosine similarity; and (4) computes a nine-metric
fairness suite spanning counterfactual (score delta, mean absolute rank change, flip rate), group-
fairness (top-K retention, four-fifths/impact ratio), and merit-aware (Recall@K, nDCG@K, equal opportunity, equalized odds) families, each with bootstrap confidence intervals, significance tests, and Benjamini-Hochberg correction, culminating in an automated PASS/INVESTIGATE/FAIL report with a composite risk score. On an example corpus of 5 job orders, 100 base candidates, and 10 demographic treatments (90 metric x variant evaluations): score shifts, top-K retention, and merit-aware rate gaps stay within tolerance for every treatment, but a rank-stability metric (MARC) and nDCG@K each surface borderline findings - including one on the neutral baseline itself - that a score- or retention-only view would miss. The results argue for multi-metric, multi-family
auditing over any single aggregate score, and for LLM-
agent-generated audits as a practical, low-cost complement to human-curated audits for any candidate-job matching pipeline.
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All authors Sai Yashwant, Shruti Bansal, Anurag Dubey, Samaroha Chatterjee, Satyam Kumar, Shreyash Gupta, Gantala Thulsiram
Artificial Intelligence
AI agents in Algorithmic Electricity Markets: On the Emergence of Tacit Collusion
Jakub Seredyński and Georgios Tsaousoglou
As electricity
market participants increasingly adopt learning-based
agents for their bidding strategies, electricity
markets are becoming algorithmic. Evidence from algorithmic
markets in other domains shows that tacit collusion can arise purely through independent learning. Moreover, electricity
markets are typically oligopolistic and feature repeated interaction among a small number of participants, making them structurally susceptible to non-competitive behavior. In the face of these observations, this paper investigates the hypothesis that tacit collusion may emerge in electricity
markets where participants' actions are controlled by autonomous learning-based
algorithms. We model strategic bidding as a repeated game with imperfect public monitoring, and model the participants' emergent behavior using multi-
agent reinforcement learning. We propose a multi-dimensional set of criteria (going beyond profit comparisons against Nash
equilibria) to assess whether the resulting behavior constitutes tacit collusion. Our experimental results showcase that such a danger is realistic for electricity
markets: there are cases where
agents do learn to sustain supra-competitive outcomes that are supportive of tacit collusion indicators, even though the
agents were never instructed to collude.
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Artificial Intelligence · Cross-listed from cs.IR
When Memory Takes Gradients: Collaborative Vector Memory for Agentic Recommender Systems
Hanchong Chen, Xing Tang, Lingjie Li et al. (+2 more)
Agentic recommender systems ground each decision of a large
language model (LLM) in a persistent
memory of the user, and in existing
agents that
memory is text: a narrative written and maintained by further LLM calls. Text limits this
memory in two ways. It is updated one rewrite at a time, so exploiting the full interaction history is prohibitively expensive; and collaborative evidence, graded similarity over an entire catalog, does not survive translation into sentences. We propose CoVeMem (Collaborative Vector
Memory), which vectorizes the collaborative core of the
agent's
memory. Frozen LightGCN user and item states form the
memory bank; at each decision, the candidate set itself retrieves the most relevant historical states, which enter the LLM's context as soft
tokens alongside a light textual profile. Contrastive
alignment to item-semantic anchors, followed by listwise co-training with masked candidates, teaches the model to read these states and to rank through them; a pointwise yes/no readout scores each candidate. Across four instruction-grounded recommendation
benchmarks, CoVeMem matches or exceeds the strongest collaborative text-
memory agent on 19 of 20 metric
cells while requiring zero additional LLM calls for
memory maintenance beyond the shared static profile, against per-interaction calls for text
memory. The
memory now takes gradients: the full interaction history, out of reach for text, becomes available as training data for what the
agent remembers and for how it reads what it remembers.
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All authors Hanchong Chen, Xing Tang, Lingjie Li, Xiongfeng Shan, Xiuqiang He
Artificial Intelligence
Learning-Augmented Online Allocation under Unreliable Advice: Robustness, Exposure Fairness, and Distribution Shift
Fredy Pokou
Learning-augmented
algorithms improve online decisions using predictions, but unreliable advice may harm efficiency and
fairness. We study an online allocation problem with finite candidate sets, irreversible decisions, and exposure constraints. We propose a robust and fair rule combining advice with a conservative fallback and
fairness correction. Under bounded-error assumptions, we prove consistency and robustness with loss proportional to prediction error. Experiments show stability under
adversarial advice and significant reductions in exposure disparity.
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Artificial Intelligence
Evaluating human and LLM screening workflows in a conceptually complex scoping review: Recall–workload trade-offs and run-to-run consistency
Nikol Figalová, Lynn Huestegge and Anne Böckler-Raettig
Background. Large
language models (LLMs) are increasingly used for screening in evidence synthesis, where false negatives can remove relevant studies before full-text assessment. We compared human and LLM title-and-abstract screening workflows in a preregistered study embedded in a conceptually complex scoping review. Methods. After a conservative title-only screen, 1,131 records were screened by one review lead, four trained assistants screening non-overlapping subsets, and seven complete LLM runs using different models and processing configurations, including a nominally identical repeat run. We compared retained workload, operational recall against 316 verified eligible records, agreement, run-to-run consistency, and procedural burden. Because eligibility was verified only for records advanced and assessed in the parent review, recall estimates were operational. Results. No workflow recovered all verified eligible records. The human workflows and two GPT-5.4 file-batch runs retained 42.2-45.0% of records while achieving 82.3-82.9% recall. Gemini 3.1 file batches achieved the highest recall (83.9%) but retained 56.7% of records. All-at-once configurations recovered fewer eligible records than corresponding file-batch configurations. Two nominally identical GPT-5.4 file-batch runs agreed on 91.7% of records but differed on 94 records, including 29 verified eligible records retained by only one run. Discussion. LLM screening performance depended on the implemented workflow, not model identity alone. Processing configuration, workload, record-level variation, and human-LLM decision integration are therefore substantive properties of deployed systems. For high-recall tasks, LLMs are better suited to validated, auditable, human-supervised workflows than autonomous exclusion.
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Artificial Intelligence · Cross-listed from cs.CR
PLCBench: Can Autonomous LLM Agents Turn PLC Access into Sustained Physical Impact?
Yitian Zhou, Jingyu Zheng, Qiliang Jiang et al. (+6 more)
Industrial control systems (ICSs) rely on programmable logic controllers (PLCs) to connect networked computation with physical control. Tool-using large
language model (LLM)
agents represent an emerging attack threat: can an autonomous
agent convert a network-reachable PLC into sustained adverse physical impact? However, existing evaluations focus on digital tasks or individual stages of PLC testing. In ICSs, evaluations that stop at software exploitation, an accepted write, or tool access may therefore mischaracterize physical risk. We present PLCBENCH, to our knowledge, the first real-PLC hardware-in-the-loop (HIL) framework for characterizing this cyber-to-physical capability and its boundaries. It combines vendor-native interaction, commercial PLC execution, closed-loop reduced-order process simulation, and independent outcome verification. A deterministic evaluator applies fixed rules to runner, communication, PLC-object, and process records to assign six hidden diagnostic flags, distinguishing usable PLC interaction, process-linked manipulation, and sustained physical impact. We instantiate PLCBENCH on four commercial PLCs crossed with four closed-loop workloads. Across five LLM families and 240 real-PLC episodes, 75 episodes (31.3%) sustain their respective physical objectives. Stagewise results show that 98 episodes stop before a valid native read, whereas 62 reach a process-linked write but do not sustain the final objective. Notably, richer process observation is associated with an increase in conditional objective attainment after a process-linked write from 44.2% to 64.0%. These measurements localize failure in configured PLC-process deployments and identify intervention points for future defense evaluation. To support reproducibility, we release the safely disclosable PLCBENCH code and a software-only reproduction pipeline through the accompanying artifact.
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All authors Yitian Zhou, Jingyu Zheng, Qiliang Jiang, Linkang Du, Haoming Liu, Lichao Wu, Shiyi Zhao, Mengxiang Liu, Ruilong Deng
Artificial Intelligence
C-Unseen: Weak Signal Detection in Dynamic Temporal Knowledge Graphs via LLM Reasoning
Yassir Lairgi, Ludovic Moncla, Khalid Benabdeslem et al. (+2 more)
Weak signals are early, low-visibility indicators that precede significant changes before those changes become established. Existing detection methods, based on keyword frequency, topic modeling, or untyped graph
topology, fail to capture the semantic and relational structure through which such signals manifest. In this paper, we propose C-Unseen, a self-interpretable framework for weak signal detection in Dynamic Temporal Knowledge Graphs (DTKGs). We define a weak signal as a rare, semantically coherent subgraph that proliferates across consecutive TKG snapshots. The framework operates through two modules: a Rare Subgraphs Extractor, in which an LLM identifies subgraphs whose content is in tension with the dominant snapshot narrative via chain-of-thought
reasoning, and a Weak Signal Alerter, in which the persistence of these rare subgraphs is tracked across time steps to isolate true weak signals. Experimental results demonstrate that C-Unseen outperforms keyword-, topic-, and graph-based baselines.
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All authors Yassir Lairgi, Ludovic Moncla, Khalid Benabdeslem, Rémy Cazabet, Pierre Cléau
Artificial Intelligence
BekchiAI: Measuring, Observing, and Controlling LLM Agents in One Click
Mesut Toruk
Large
language model
agents reason, call tools, and act autonomously over many steps, but their agentic skills-correctly sequencing tools, planning under dependencies, judging untrusted inputs, and grounding generated arguments-are hard to measure with accuracy-only leaderboards. We present BekchiAI, which addresses both sides: a
benchmark for measuring agentic skill and a platform for observing and controlling live
agents. The BekchiAI-
Benchmark, a suite of 13 tool-using ReAct
agents across 7 task categories (arithmetic, structured/SQL, security detection, URL grounding, planning, orchestration, and tool-policy), totalling 2,057 deterministic, committed test tasks. Every task is verifier-checkable gold answers are computed by running canonical SQL against a real database, computing the exact schedule of a directed acyclic graph (DAG), or evaluating closed-form lambdas including
adversarial security samples paired with deliberately imperfect signature scanners so a score reflects the model's own judgment, not the copying of an oracle. We define a small set of behavioral metrics beyond accuracy-tool-call adherence, URL
hallucination and source-match, and per-model
token cost and report a four-model comparison (Qwen3.7-Max, gemma-4-31B-it, gemma4:26b, gpt-oss-120b) whose story is in the per-family spread, not the aggregate. The
benchmark runs are executed using the provided evaluation scripts. BekchiAI-Platform is a complementary web-based observability and control layer for deployed
agents, providing full
token and latency telemetry as well as remote run termination. The
benchmark, evaluation tools, and platform are publicly released.
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Artificial Intelligence · Cross-listed from cs.LG
Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic
Biao Yin, Abderrahmane Kasmi and Nadir Farhi
Connected and automated vehicle (CAV) platooning offers a promising approach to improving road safety and traffic capacity. However, platoon control in real-world traffic is challenging due to uncertainty and heterogeneous driving behaviors. Reinforcement learning (RL) has strong potential for addressing such control problems, but its practical deployment raises challenges related to safety and learning efficiency. This paper proposes a generic modeling and simulation framework for investigating CAV platoon joining maneuvers and comparing deep reinforcement learning (DRL)-based control
algorithms. The problem is particularly challenging in mixed-traffic environments, where CAVs coexist with human-driven vehicles exhibiting heterogeneous longitudinal and lateral behaviors. The objective is to achieve safe and efficient joining maneuvers by either incorporating penalties for risky behaviors into the learning process or using an external safety controller to constrain the learned policy. An
agent-based modeling framework coupled with the Simulation of Urban MObility (SUMO) simulator is used to evaluate Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Proximal Policy Optimization (PPO). Results show that PPO outperforms DQN and DDQN, achieving a joining success rate of approximately 98 % and a collision rate below 1 %, largely due to risk-related penalties incorporated into the reward function. However, this improved performance requires more decision steps to complete the maneuver, revealing a trade-off between safety, joining effectiveness, and decision efficiency. An external safety controller effectively prevents collisions, although its interventions may reduce joining efficiency. The results highlight the importance of jointly considering safety and efficiency when designing RL-based controllers for CAV platoon joining in mixed traffic.
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Artificial Intelligence · Cross-listed from cs.CV
Haowen Gu, Gensheng Pei, Junzhu Mao et al. (+3 more)
Although
Multimodal Large
Language Models (MLLMs) have demonstrated impressive performance in Medical Visual Question Answering (Med-VQA), their reliance on global image features often lacks precise pixel-level grounding, thereby limiting clinical trustworthiness. To bridge the semantic gap between high-level clinical
reasoning and spatial localization, we propose \textsc{MedREAL} (Medical
REasoning-driven Answering and Localization), a unified framework that seamlessly aligns linguistic
reasoning with spatial grounding. Specifically, MedREAL introduces Seg Anchored
Reasoning Pooling (SARP) to distill task-relevant semantic evidence directly from [SEG]
tokens within the MLLM's hidden states. Furthermore, a
Reasoning-to-Visual (R2V) fusion mechanism is proposed to effectively inject these
reasoning-aware features into a
segmentation pipeline for accurate mask decoding. To facilitate this paradigm, we construct MedRAVS-13K, a comprehensive
dataset comprising 13,824 expertly validated samples across four diverse imaging modalities. Extensive experiments demonstrate that MedREAL significantly outperforms state-of-the-arts, achieving 68.49% gIoU and 70.47% cIoU on
benchmark evaluations. By generating evidence masks that are strictly consistent with textual diagnoses, MedREAL provides a robust, interpretable framework for
reasoning-driven medical image analysis.
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All authors Haowen Gu, Gensheng Pei, Junzhu Mao, Qiong Wang, Mingwu Ren, Yazhou Yao
Artificial Intelligence
LiveSim: Simulating Environment-Shaped Users in Multi-Agent Live-Stream Ecosystems
Jiaqi Xu, Yiran Qiao, Jing Chen et al. (+3 more)
User behavior simulation with large
language models~(LLMs) is increasingly used to support multi-
agent ecosystem simulation. Existing simulators typically rely on static user profiles inferred from historical observations, which become inadequate in socially intensive environments such as live streaming where interaction dynamics continuously reshape user behavior. We propose LiveSim, an LLM-based framework for live-stream ecosystem simulation. It represents users as editable behavioral hypotheses and progressively refines them through trajectory-grounded interactions, where discrepancies between simulated and observed trajectories reveal missing environmental shaping effects. These signals are further extracted as transferable environment-behavior patterns and accumulated in a collective behavioral
memory to improve user-level behavioral fidelity and support ecosystem-level simulation. Experiments on real-world live-stream risk-control data validate the effectiveness of LiveSim in improving user-level behavioral fidelity and enabling ecosystem-level analysis of risk
evolution and platform intervention effects.
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All authors Jiaqi Xu, Yiran Qiao, Jing Chen, Qiwei Zhong, Xiang Ao, Xueqi Cheng
Artificial Intelligence · Cross-listed from cs.CV
MedFG-VQA: Low-Frequency Memory and Graph Attention for Lightweight Medical VQA
Haowen Gu, Gensheng Pei, Zeren Sun et al. (+4 more)
Medical Visual Question Answering (Med-VQA) holds significant promise for clinical decision support, yet faces challenges due to limited annotated data and the high computational demands of existing large
vision-
language models. We propose MedFG-VQA, a lightweight framework that leverages a
memory bank to augment DCT-based low-frequency features and employs graph-enhanced cross-
attention for effective visual-textual
alignment. Specifically, our approach features two key components: Frequency-
Memory Fusion (FMF), which enhances low-frequency features by retrieving from a learnable
memory bank built on DCT decomposition, and Graph-Aware Cross-
Attention (GACA), which aligns visual-textual features via cross-
attention and refines them through graph-convolutional aggregation. To address data scarcity, we construct SynMed-VQA, a large-scale synthetic
dataset comprising over 2 million question-answer pairs across 9 imaging modalities and 10 major organs, generated with GPT-4o. Extensive experiments on SynMed-VQA and three other standard biomedical VQA
benchmarks demonstrate that MedFG-VQA achieves competitive or superior performance compared to much larger models while maintaining significantly lower computational costs, highlighting its efficiency and potential for clinical deployment.
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All authors Haowen Gu, Gensheng Pei, Zeren Sun, Mingwu Ren, Xiangbo Shu, Yazhou Yao, Fumin Shen
Artificial Intelligence · Cross-listed from cs.CL
Evaluating Confidence-Gated Retrieval with Matched Trajectory Replay
Prateek Chhikara
Interactive
language-model
agents use confidence signals to decide whether to answer immediately, retrieve additional evidence (from
memory or external knowledge), or defer. Yet confidence is usually evaluated in isolation, without measuring the trajectory-level consequences of the actions it triggers. We propose matched trajectory replay, a controlled protocol for comparing confidence-to-action mappings. The protocol holds candidate answer states, evidence points, budgets, and action costs fixed. We use it to compare raw verbalized confidence with post-hoc isotonic calibration in a multi-hop question-answering system using Mistral, GPT, and Qwen models on HotpotQA and MuSiQue
datasets. At the same numerical commitment threshold, calibration changes which questions
agents ultimately commit to answering. Across all six model-
dataset pairs, it increases accuracy among committed answers by up to 41 percentage points. However, it can reduce coverage and increase
retrieval use. Overall accuracy improves by up to 15 percentage points on HotpotQA but falls by up to 17 percentage points on MuSiQue. These effects reflect a shift to a more selective, lower-risk operating point, not improved answers or confidence ranking. A calibration map fitted before
retrieval improves held-out calibration through
retrieval depths one and two, but is worse than raw confidence at depth three for all three models. Additional evidence helps on average, but this aggregate effect does not establish whether confidence identifies which individual episodes will benefit from another
retrieval. Taken together, these results show that calibration can make commitment risk interpretable, but it does not estimate the expected benefit of another
retrieval.
Retrieval therefore requires a separate value-of-information or utility estimate. Evaluations should report held-out calibration, risk-coverage, and
retrieval cost.
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Artificial Intelligence
SymbolLKG: Towards Verifiable Logical Reasoning via Logical Knowledge Graph and Symbolic Solvers
Haizhao Fan, Yuchi Xiong, Jize Wang et al. (+2 more)
Large
Language Models (LLMs) have demonstrated remarkable proficiency in natural
language understanding, yet they struggle with strict multi-step
reasoning, frequently suffering from
hallucinations and inconsistency. Existing solutions like Chain-of-Thought (CoT) lack rigorous verification mechanisms, while standard
Retrieval-Augmented Generation (RAG) often misses the complex, structural dependencies inherent in logical tasks. To bridge this gap, we propose a Neuro-Symbolic architecture that integrates a Logical Knowledge Graph (LKG) with dynamic solver routing. Specifically, we introduce an ontology-based LKG that treats logical rules and constraints as first-class topological nodes, enabling explicit modeling of dependencies extracted from text. We further design a Logic Router to dynamically dispatch tasks to the optimal symbolic engine, which is supported by a
topology-aware hybrid
retrieval mechanism. Experimental results on logical
reasoning benchmarks demonstrate that our framework significantly outperforms state-of-the-art prompting and RAG baselines, delivering higher accuracy and verifiable
reasoning paths.
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All authors Haizhao Fan, Yuchi Xiong, Jize Wang, Xinping Guan, Xinyi Le
Artificial Intelligence · Cross-listed from cs.CL
Behavior2Trip: Towards Personalized Travel Planning via User Behavior Trajectory
Zihao Cheng, Yingyu Shan, Hongru Wang et al. (+6 more)
Travel planning
agents assist users in generating personalized travel plans by modeling their individual preferences. Existing
agents either rely on explicit user instructions or engage in multi-turn clarification to elicit user preferences. However, both approaches overlook the rich behavioral signals latent in users' past behaviors, which implicitly encode their preferences. This over-reliance on active user input increases interaction burden and limits plan personalization. To bridge this gap, we introduce a new task, Behavior-Aware Travel Planning, which infers user preferences directly from past behaviors and generates personalized travel plans. To facilitate research on this task, we introduce Behavior2Trip, a
benchmark constructed from one of the largest Chinese online travel platforms, comprising 11,400 instances. Each instance represents an average of 39.8 past user behaviors spanning 14 attributes across 5 preference dimensions. We further propose B2T-
Agent, a reinforcement learning-based
agent that leverages user behavior trajectories, interacts with external tools for preference-aligned
retrieval, and maintains an internal
memory module. Experiments on Behavior2Trip show that GPT-4.1 achieves a full-constraint pass rate of only 0.5% on the hardest tasks, while B2T-
Agent built upon Qwen3-8B outperforms all baselines, highlighting the substantial challenge of this task. Moreover, Qwen3-8B trained with B2T-
Agent also outperforms GPT-4.1 on the TravelPlanner
benchmark, demonstrating strong generalization. Code and data are available at https://github.com/BUAA-IRIP-LLM/Behavior2Trip
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All authors Zihao Cheng, Yingyu Shan, Hongru Wang, Zeming Liu, Xinyi Wang, Xiangrong Zhu, Yuhang Guo, Wei Lin, Yunhong Wang
Artificial Intelligence
Decoupling Planning and Control for Instructable Agents
Zineng Tang, Kelsey R. Allen, Sjoerd van Steenkiste et al. (+2 more)
Recent work shows that pre-trained, instruction-tuned
vision-
language models (VLMs) perform well at mapping from instructions and observations to high-level plans, but struggle to realize such plans as reliable low-latency action sequences in unfamiliar environments. At the same time, world-model controllers excel at fast observation-to-action control, but lack open-ended task guidance. In this work, we combine these strengths into a single system, Instruct-to-Act, where we train a world-model controller to act autonomously at high frequency when conditioned on sparse, higher-latency, and high-level text instructions generated by a VLM planner. To train controllers to be
language-instructable, we relabel segments of controller policy rollouts with synthetic instructions and jointly optimize a behavior-cloning objective along with existing reward-maximizing and world-modeling objectives. We evaluate our proposed approach across seven
embodied environments, including three multi-
agent environments where VLM planners coordinate through
language while trained controllers serve as their actuators. Under matched observation and action spaces, our decoupled approach consistently outperforms controller-only and direct VLM action-generation variants, preserves fast control, and lets us swap in different pretrained VLM planners without fine-tuning, while remaining competitive with strong
vision-
language-action and multi-
agent RL baselines on six of seven tasks.
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All authors Zineng Tang, Kelsey R. Allen, Sjoerd van Steenkiste, Ishita Dasgupta, Alane Suhr
Artificial Intelligence
AI Control Scientist: LLM-driven Agentic System for Automated Control Design
Haiteng Wang, Weihao Li, Jing Zhang et al. (+1 more)
Control system design is critical for modern industry, such as chemical process temperature regulation and aero-engine control. However,traditional control design workflows rely heavily on expert knowledge and extensive manual parameter tuning, resulting in limited efficiency and scalability. To this end, this paper proposes AI Control Scientist (AICS), the first large
language model (LLM)-driven
agent capable of automatically generating optimized controller from
language design requirements. Specifically, a Task Modeling
Agent interprets user requirements to engineering constraints; a Controller Design
Agent generate candidate controller structures and executable code; and a Parameter Tuning
Agent refine controller parameters under closed-loop performance criteria. Experiments demonstrate that the proposed agentic system can automatically generate multiple representative control systems, outperforms existing automated baselines in both design success rate and optimization efficiency. This work has the potential to transform control system design from human-driven to
agent-driven, paving the way for model predictive control and other advanced control systems design.
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All authors Haiteng Wang, Weihao Li, Jing Zhang, Lei Ren
Artificial Intelligence
Categorizer Automata for Discounted-Sum Payoffs
Nathalie Bertrand, Pranav Ghorpade, Senthil Rajasekaran et al. (+2 more)
Categorizing continuous data into discrete bins is a fundamental operation in artificial intelligence. We introduce the categorizer automaton, a deterministic automaton that reads an infinite sequence of rewards and identifies which of finitely many bins contains its discounted sum. Categorizer automata generalize comparator automata, the special case of two bins, which have already proven useful in quantitative synthesis. Our main technical contribution is the construction of a categorizer automaton whose state space is linear in the number of bins, rather than exponential as obtained by a cross-product of comparator automata. We then apply categorizer automata to Markov decision processes, where they allow one to synthesize policies that maximize the expected utility of a discounted-sum payoff for utility functions that may be discontinuous. For piecewise-constant utility functions, the resulting
algorithm is exact and runs in pseudo-
polynomial time. For piecewise-Lipschitz utility functions, a class that includes any utility with bounded slope between finitely many jumps, it again runs in pseudo-
polynomial time and yields an $\varepsilon$-optimal policy. We also show that the synthesis problem considered is PSPACE-hard already for piecewise-constant utilities.
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All authors Nathalie Bertrand, Pranav Ghorpade, Senthil Rajasekaran, Sasha Rubin, Moshe Vardi
Artificial Intelligence
DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?
Jiahui tang, Kuicai Dong, Dexun Li et al. (+7 more)
Faithful chart generation in real-world data-science workflows requires grounding visualizations in scattered evidence, computing chart-ready quantities, and rendering them accurately. Modern LLMs can produce visually plausible, instruction-compliant charts, yet data-level
hallucinations remain difficult to detect in long, noisy, and
multimodal contexts. To measure this gap, we introduce DEEPCHART, an expert-annotated
benchmark of 1,482 task-conditioned chart-generation instances drawn from real-world scientific papers, financial filings, and ecosystem reports. DEEPCHART formulates chart generation as an Extract–Reason–Visualize pipeline and evaluates source-data extraction, derived-data
reasoning, and chart rendering stage by stage. Experiments with state-of-the-art models show that visually plausible charts often conceal data-level
hallucinations, with extraction and
reasoning errors common in realistic long and
multimodal settings. These findings suggest that larger context windows alone are insufficient; faithful chart generation also requires reliable evidence extraction and quantitative
reasoning before rendering. Our
benchmark and associated resources are available at https://github.com/tangdouer1005/DeepChart.
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All authors Jiahui tang, Kuicai Dong, Dexun Li, Hongchao Gu, Haocheng Yu, Wei Han, Chen Zhang, Yong Liu, Hao Wang, Enhong Chen
Artificial Intelligence · Cross-listed from cs.SE
Beyond Execution: Auditing Experimental Fidelity in LLM-Driven Scientific Research
Lezhi Yu, Xiaogang Xu, Yuhua Zhou et al. (+2 more)
LLM
agents used for scientific experimentation must do more than generate executable code: they must implement the reference method faithfully, design experiments that test the paper's claims, and provide evidence supporting those claims. We show that
agents often produce methodological
hallucinations: silently reducing
datasets or training budgets, replacing failed learning or generative components with lookup or oracle functions, or drawing conclusions from resource-limited settings where a method's claimed advantage disappears. To detect these failures, we introduce ABE-Ralph, a reference-anchored
auditing framework that represents claims, protocols, required components, baselines, and metrics as structured experimental constraints, guides implementation through an 8-step workflow, and performs quantitative, qualitative, and code-level verification. Across 30 long-horizon reproduction runs covering 12 machine learning domains, ABE-Ralph achieves a 93% robust execution rate and identifies five scientific failure modes. In 23 NatureBench discovery tasks, ABE-Ralph matches or exceeds state-of-the-art performance on 5 tasks. These results show that reliable evaluation of AI scientists must assess whether the experimental design faithfully tests the intended claim and whether the resulting evidence supports it, rather than treating code execution or plausible metrics as evidence of scientific success.
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All authors Lezhi Yu, Xiaogang Xu, Yuhua Zhou, Shuibing He, Aimin Pan
Artificial Intelligence
Discovering Relationships in Data Lakes Using Large Language Models: An Industrial Case
Ahlame Diouan, Eric Ferey, Sabine Loudcher et al. (+1 more)
Data lakes rely on metadata to remain usable, yet this meta data is often limited or weakly informative for column relationship discovery, especially in ERP-derived
datasets with coded or abbreviated schema labels. We propose ColRel, a two-stage method that builds column
embeddings from metadata and data available at ingestion time. In difficult cases, such as coded schemata, business dictionaries help better interpret column names and support the generation of short natural-
language descriptions used in the second stage. Experiments on public
benchmarks and an industrial ERP
dataset show that ColRel is particularly effective in semantically related, weak-signal settings.
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All authors Ahlame Diouan, Eric Ferey, Sabine Loudcher, Jérôme Darmont
Artificial Intelligence
AgentFold: Closed-Loop Agentic Search for Protein Folding Model Design
Mingquan Liu, Jiangyu Chen, Hanqun Cao et al. (+9 more)
Scientific LLM
agents have shown promise in literature
reasoning, tool use, and experiment planning, but it remains unclear whether they can autonomously improve large, tightly coupled scientific machine-learning systems through executable code changes and computationally expensive validation. We study this question in
protein folding, where progress requires coordinated architectural modifications, multi-objective evaluation, and domain-aware interpretation. We present AgentFold, a multi-
agent framework that formulates folding-model development as a closed-loop search over executable code variants. Starting from ESMFold, AgentFold proposes hypotheses, implements and debugs code-level modifications, evaluates model variants, analyzes experimental outcomes, and stores both successful and failed interventions in structured
memory. An MCTS-style policy allocates computational resources across high-scoring search branches. On an engineering-scale
protein-folding codebase comprising more than 2,000 lines of code, AgentFold explores approximately 80 model variants using approximately 5,000 GPU-hours and 170 million LLM
tokens. Under a matched computational budget, AgentFold improves the best lDDT by 7.5% over independent Codex proposals and outperforms a random-search control. Beyond model improvement, the resulting intervention traces reveal recurring empirical design patterns: stable gains tend to arise from early, soft, learnable priors and gated refinement, whereas direct
geometric perturbations and geometry-conditioned feedback often destabilize training. The code and experimental resources are publicly available at https://github.com/lmqfly/AgentFold.
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All authors Mingquan Liu, Jiangyu Chen, Hanqun Cao, Xujun Zhang, Pengsen Ma, Xiangru Tang, Shuting Jin, Zhuo Yang, Annie Zheng, Tianfan Fu, Fang Wu, Xiangxiang Zeng
Artificial Intelligence · Cross-listed from cs.SE
FaultLens: Learning Compact Behavioral Test Suites for Generated Operational Programs
Zeming Liu, Hang Lyu and Jingtao Zhang
Generated operational programs are often validated with either a few hand-written examples or exhaustive regression suites. The former can miss sparse boundary and interaction faults, while the latter can be unnecessarily expensive. We introduce FaultLens, a method for learning compact behavioral test suites while preserving an auditable connection to executed evidence. It executes a rich probe domain once, stores the fault-probe kill relation as a sparse outcome cache, and learns probe orderings only from earlier program generations. A fault-driven greedy component exploits known kill structure, while a mutation-independent diversity component covers probe families, cases, templates, and temporal bins. Their alternating hybrid remains useful when a new program contains a fault mechanism absent from ordering construction. We evaluate twenty generated operational policies across four environments, ten execution seeds, 1,200 measured run summaries, 2,160 controlled program transformations, and 4,120,200 executed program-probe pairs. Of 1,960 intended faulty transformations, 1,779 alter a contract or output somewhere in the finite audit domain; 200 additional controls preserve behavior. A 32-probe hybrid learned on generations 1-3 covers 576/582 (99.0%) dynamically killable faults in generations 4-5 using 1.2-2.0% of the exhaustive domain. With an entire fault family withheld from training, diversity raises scenario-family macro coverage from 84.6% to 94.9%. In a downstream deployment study, a conservative admission rule reduces severe tail regressions from 15/20 program-environment groups to 0/20. FaultLens provides a prioritized evidence mechanism, not a proof of correctness, and makes its budget, evidence source, generalization split, and misses explicit.
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Artificial Intelligence
Graph-Guided Selective Unlearning for Language Models: Controlling Support Routes Beyond Forget Seeds
Waqas Khan, Tabinda Sarwar, Jingyue Cong et al. (+2 more)
Enterprises fine-tune
language models on proprietary data that may later require removal due to
privacy, contractual, or compliance obligations. Selective unlearning removes requested knowledge while preserving model utility, offering a practical alternative to full retraining, but existing methods treat the explicitly identified forget examples as the complete deletion scope. This is insufficient when target knowledge remains recoverable through paraphrases, aliases, or neighboring training examples. We propose GRAPHSU, a graph-guided controller that expands the deletion scope beyond forget seeds by constructing a weighted support-route graph, propagating deletion pressure through it, and applying graded forgetting strengths to high-risk neighbors. On the Task of Fictitious Unlearning (TOFU), a synthetic author-profile question-answering
benchmark, and PISTOL, a structural-unlearning
benchmark built around interconnected factual samples, with GPT-2 Medium and Llama-3.2-3B-Instruct, GRAPHSU achieves the lowest utility-feasible soft leakage across all deletion settings, reducing leakage by up to 49.5 percentage points over a matched seed-only baseline, demonstrating that effective enterprise unlearning requires controlling support routes, not just forget seeds.
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All authors Waqas Khan, Tabinda Sarwar, Jingyue Cong, Xun Yi, Estrid He
Artificial Intelligence · Cross-listed from cs.CR
Daydreaming: Stealing Hidden Agent Skills through Black-Box Task Interaction
Yu-Lin Tsai, Yu-An Lu, Ci-Yang Tsai et al. (+3 more)
Agent skills bundle instructions, reference data, and executable helpers that let a general
agent perform specialized tasks. Hosted providers can keep these files secret while selling access to task results, making the skill itself a valuable target. Existing disclosure defenses can block requests that ask for the skill or reproduce its text, but they cannot block customers from submitting the ordinary tasks the service is built to complete. We present Daydreaming, an execution-only attack that steals a multi-file skill through black-box task interactions. The victim is never asked to reveal the skill or grade a reconstruction. Instead, Daydreaming adaptively creates crafted tasks whose results distinguish possible hidden behaviors. It tests individual behaviors, uses attacker-controlled shadow
agents to choose a design, and completes each file using stored victim results and local execution checks. We formalize three nested threat levels of access as Differential, Trace, and Output, and focus on Output, where the attacker sees only the final response and returned files. Across 7 skills and 4 victim models, Daydreaming recovers 86.8% of the original skill's capability at Output, outperforming SigLeak by almost 4x. It produces installable skills using a median of 32 victim calls per skill even with disclosure defenses enabled. These results show that hiding skill files and filtering direct disclosure do not, by themselves, prevent functional reconstruction through normal use.
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All authors Yu-Lin Tsai, Yu-An Lu, Ci-Yang Tsai, Muxi Lyu, Raluca Ada Popa, Chia-Mu Yu
Artificial Intelligence · Cross-listed from cs.LG
Rethinking Message Passing as Retrieval for Text-Attributed Graph Learning
Jintang Li, Yuhong Chen, Ruofan Wu et al. (+4 more)
Graph neural networks (GNNs) are typically conceptualized as message-passing neural networks, yet it remains unclear why neighborhood aggregation reliably outperforms node-wise multilayer perceptrons (MLPs). Despite its empirical success, this paradigm can be computationally expensive and sensitive to imperfect graph structures. In this work, we present a
retrieval-augmented view of GNNs: each layer makes predictions by applying an MLP to a node representation together with a permutation-invariant summary of retrieved graph context. Motivated by this perspective, we propose RTA, a simple MLP-based framework that replaces structural message passing with label-aware
retrieval and propagation. We provide theoretical insights that (i) connect
retrieval-based aggregation to softmax-
attention message passing, and (ii) establish the robustness of retrieved-context supervision to mis-retrieved outliers. Experiments on multiple text-attributed graph
benchmarks show that RTA matches or even outperforms strong GNN and graph LLM baselines while improving efficiency and robustness across diverse scenarios.
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All authors Jintang Li, Yuhong Chen, Ruofan Wu, Binli Luo, Jiayi Ji, Hui Li, Rongrong Ji
Artificial Intelligence
Knowing When Not to Reuse: Conditional Experience Transfer in Autonomous LLM Post-Training
Tingyun Li, Wenfeng Feng, Weiqing Li et al. (+3 more)
Large
language models offer broad capabilities, but adapting them to evolving domains, tools, and requirements often entails repeated post-training. Autonomous systems automate parts of this process by proposing updates, training candidates, and using evaluation feedback to select subsequent proposals. As evidence accumulates, a central problem emerges: which past update evidence remains actionable after subsequent training has changed the parent model? An update's effect depends on its parent, data, and training stage. Treating past success as context-free permission can waste compute. If the resulting child is promoted, it can also degrade the subsequent training trajectory. We formulate this problem as conditional experience transfer and introduce Boundary-Calibrated Intervention Transfer (BCIT), a method that authorizes experience reuse before weight-changing training. BCIT binds an observed effect to its source context, checks applicability conditions, vetoes candidates with named hard conflicts, and obtains current-state evidence through a bounded training trial when needed. Fully trained candidates still face a shared adoption rule, and only observed events extend
memory. On one 4B model adapted across finance
reasoning, text-to-SQL, and function calling, candidate updates exhibit heterogeneous target and retention effects across the evaluated contexts. Under matched candidates, evidence, and compute, BCIT authorizes fewer harmful updates and attains higher equal-budget final-model quality than the evaluated alternatives. These results support treating experience authorization as a distinct problem in autonomous post-training.
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All authors Tingyun Li, Wenfeng Feng, Weiqing Li, Abudukelimu Wuerkaixi, Guohua Liu, Yuewei Zhang
Artificial Intelligence · Cross-listed from cs.CV
LiveVVT: High-Fidelity Video Virtual Try-On in Real Time
Yushe Cao, Shikun Feng, Ruxiang Duan et al. (+4 more)
Diffusion-based
Video Virtual Try-On (VVT) achieves high visual fidelity through bidirectional spatio-temporal modeling, but complete-clip dependence incurs prohibitive latency and computational overhead in practical continuous deployment. Naively enforcing causality disrupts pretrained bidirectional priors and substantially degrades synthesis quality. We introduce LiveVVT, a rolling streaming
diffusion framework that preserves bounded bidirectional modeling within causal recurrent generation. Within a fixed-size window, LiveVVT jointly denoises multiple
video chunks under bounded look-ahead, preserving local bidirectional interactions while emitting one clean chunk per iteration. Beyond the window, two complementary
memories sustain long-term consistency: a bounded temporal
memory propagates recent dynamics and occlusion context, whereas a persistent global appearance
memory, constructed once from the target garment and a frontal try-on keyframe, anchors garment details and dressed appearance throughout the stream. We further introduce a progressive
distillation framework integrating bidirectional VVT learning, teacher-trajectory regression for causal few-step adaptation, and Collaborative Matching
Distillation, which couples teacher-distribution matching with rolling flow matching on real
videos to align optimization with recurrent inference. Experiments on paired and unpaired long-sequence
benchmarks demonstrate superior generation quality over similarly sized models, with $26\times$ lower latency and $11\times$ higher throughput, enabling high-fidelity real-time streaming VVT.
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All authors Yushe Cao, Shikun Feng, Ruxiang Duan, Liyong Wang, Dianxi Shi, Chun Yu, Junliang Xing
Artificial Intelligence · Cross-listed from cs.CV
AesCanvas: A Large-Scale Dataset and Benchmark for Aesthetic Critique and Contextual Suitability
Xuanwei Hu, Haoyu Dong, Kejun Wu et al. (+2 more)
Recent advances in
Multimodal Large
Language Models (MLLMs) have extended Image Aesthetic Assessment (IAA) beyond scalar scores toward interpretable critique and guidance. Yet existing
benchmarks mainly assess intrinsic visual quality or fixed domain criteria, leaving open whether an appealing image is appropriate for a specific purpose, audience, cultural setting, or domain convention. We introduce AesCanvas, a unified suite with two complementary components: CritiqueCanvas with 519,136 instruction-response pairs from 54,300 images supports long-form, multi-dimensional critique across photography, painting, and virtual imagery, whereas ContextCanvas with 301 expert-reviewed use scenarios evaluates contextual aesthetic suitability in realistic use scenarios. Under a unified protocol, we evaluate closed-source frontier, open-weight general, and aesthetic-specific MLLMs. Results reveal a clear separation between critique generation and context-sensitive judgment: reference-based lexical and semantic metrics only partially capture critique quality, while aesthetic specialists remain competitive on selected critique metrics yet substantially lag strong general-purpose MLLMs on ContextCanvas. Further analyses show that aesthetic specialization does not reliably transfer to contextual suitability and that model decisions may fail to track or ground themselves in decisive contextual visual cues. These findings establish culturally situated, evidence-grounded suitability as a distinct objective for aesthetic modeling.
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All authors Xuanwei Hu, Haoyu Dong, Kejun Wu, Tianyi Liu, Jianjun Gao