Artificial Intelligence · Cross-listed from cs.SE
InteractBench: Benchmarking LLMs on Competitive Programming under Unrevealed Information
Jiaze Li, Aocheng Shen, Bing Liu et al. (+4 more)
Competitive programming is increasingly being used to evaluate the algorithmic
reasoning capabilities of large
language models (LLMs). However, existing
benchmarks primarily focus on full-information tasks where all problem inputs are provided upfront. This overlooks a critical dimension of algorithmic
reasoning: the ability of generated programs to operate when key information is not revealed upfront. Interactive problems, a distinctive component of competitive programming, embody this challenge. These problems require programs to engage in multi-round interaction with an interactor (a judge program) under strict protocol constraints and limited query budgets, with new information revealed only in response to queries. To address this gap, we introduce InteractBench, a
benchmark comprising 322 high-quality interactive problems curated from Codeforces, AtCoder, IOI, and ICPC. Each problem is packaged with executable local interactors, enabling fully offline evaluation. Unlike existing
benchmarks, InteractBench assesses whether model-generated code can acquire information and track state dynamically. Our evaluation reveals a significant interaction gap: even the most advanced
reasoning models achieve limited success on interactive problems. Beyond success rates, we propose a fine-grained failure taxonomy to diagnose the root causes of these deficiencies. Although algorithmic logic errors remain dominant, protocol violations and query-budget overruns are frequent. Code is available at https://github.com/kmsgk0/InteractBench.
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All authors Jiaze Li, Aocheng Shen, Bing Liu, Boyu Zhang, Xiaoxuan Fan, Qiankun Zhang, Xianjun Deng
Artificial Intelligence · Cross-listed from cs.CL
MI-Distillation: Selecting from Model-Interpolated Instruct-Reasoning Data Spectrum for Chain-of-Thought Distillation
Yangsong Lan, Renkai Hu, HongKai Zheng et al. (+4 more)
Recent advances in large
reasoning models (LRMs) have shown strong performance on complex problems through long chain-of-thought (Long CoT)
reasoning. However, distilling such trajectories into smaller student models remains challenging: direct Long CoT supervision often provides limited gains and can be less effective than concise Short CoT rationales. In this work, we investigate this phenomenon from a gradient-centric perspective. Our analysis shows that Long CoT induces larger gradient magnitudes and more concentrated update directions than Short CoT, with this effect becoming more pronounced as student model capacity increases. These findings suggest that effective Long CoT
distillation requires balancing the
reasoning information density of
reasoning trajectories with their distributional
alignment to the student model. Motivated by this insight, we propose Model Interporlation
Distillation (MI-
Distillation), a framework that constructs a continuous Instruct-
Reasoning data spectrum through model interpolation. To select suitable trajectories from this spectrum, we further introduce Sequential Learnable Surprisal Score (SeqLSS), which favors
reasoning paths that are both informative and learnable for the student. Extensive experiments on
reasoning benchmarks show that MI-
Distillation consistently improves small model CoT
distillation over strong Long CoT baselines.
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All authors Yangsong Lan, Renkai Hu, HongKai Zheng, Bo Zhang, Renzhi Wang, Hongliang Dai, Piji Li
Artificial Intelligence · Cross-listed from cs.MA
AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing
Xinke Jiang, Yue Fang, Zhibang Yang et al. (+12 more)
Retrieval-Augmented Generation (RAG) improves the factuality of large
language models (LLMs), yet existing RAG systems often struggle with complex, multi-step
reasoning that requires adaptive
retrieval and continuous revision of intermediate contexts. Recent reinforcement learning (RL)-based agentic RAG methods partially alleviate this issue, but typically rely on coarse-grained action spaces and trajectory-level rewards, resulting in weak reward assignment and a bias toward short-horizon, stereotyped
reasoning template. To address, we propose AgenticRag-R1, a RL framework that deeply integrates
reasoning,
retrieval, and
memory via a
memory stack and fine-grained action space, supported by hierarchical action-aware rewards and an information-aware trajectory rejection strategy to enable effective long-horizon learning. Experiments across a diverse set of multi-hop, open-domain, and agentic
reasoning benchmarks, spanning multiple backbone model sizes, demonstrate that AgenticRag-R1 consistently outperforms strong baselines. Moreover, AgenticRag-R1 learns more robust, interpretable, and
memory-aware
reasoning behaviors, highlighting the effect of fine-grained action modeling and information-aware optimization for long-horizon
reasoning. Our code is anonymous available at https://github.com/jiangxinke/Harness-RL/tree/AgenticRAG-R1-Whitebox.
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All authors Xinke Jiang, Yue Fang, Zhibang Yang, Jiaran Gao, Zhixin Zhang, Tao Feng, Rihong Qiu, Wentao Zhang, Hongxin Ding, Ruizhe Zhang, Yongxin Xu, Yuheng Huang, Xu Chu, Junfeng Zhao, Yasha Wang
Artificial Intelligence · Cross-listed from cs.CV
CineForge: Self-Improving Agents for Long-Horizon Video Generation
Junxiang Liu, Lin Wang, Haiyu Shi et al. (+10 more)
Long-horizon story-driven
video generation requires a production
agent to coordinate narrative decomposition, state tracking, shot design,
prompt construction, rendering, and revision across interdependent scenes. Existing adaptive
video systems primarily refine requests or reusable skills, leaving recurring production failures disconnected from persistent, stage-targeted improvements across stories. We introduce CineForge, a self-evolving
video-production
agent framework that couples CineForge-Produce for
video generation with CineForge-Evolve for cross-story policy
evolution. CineForge-Produce organizes each source story into typed narrative, character, spatial, and cinematic states, uses them to coordinate asset and clip generation, and records the process as a canonical production trajectory. CineForge-Evolve applies Case-to-Pattern-to-Policy
Evolution (CPPE) to review trajectory evidence, consolidate recurrent findings into bounded stage-local patches, and deploy validated updates through structural replay and confidence-controlled paired evaluation. To measure complete story realization, we introduce CineScope, which combines a 100-script CineScope-Data suite with a human-aligned, multiscale CineScope-Metric spanning causal state, directorial orchestration, pacing and resource allocation, and character arc. Across CineScope-Data and two public
benchmarks, the evolved CineForge policy improves CineScope-Metric from 4.024 to 4.380, outperforms three long-
video baselines with consistent gains under ScriptAgent, and reduces review LLM calls by 37.0% on new stories. These results establish production trajectories as actionable experience for
video agents that improve cumulatively across long-form storytelling tasks.
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All authors Junxiang Liu, Lin Wang, Haiyu Shi, Hongxu Ma, Xiaoyu Yang, Chunjie Chen, Xiaoxiao Xu, Kaiqiao Zhan, Boao Wang, Shuizhou Shi, Tianyun Zhu, Jie Li, Jiangtong Li
Artificial Intelligence · Cross-listed from cs.CL
Memory-First Fact-Checking: A Knowledge-Graph-Grounded Multi-Agent System for Misinformation Detection
Amelia Petrenciuc, Alexandru Lecu and Adrian Groza
This paper introduces a hybrid fact-checking framework that integrates Knowledge Graph-based semantic
memory with
adversarial multi-
agent reasoning for explainable misinformation detection. The proposed system follows a
memory-first, web-fallback architecture, in which input claims are initially evaluated against a dual-index Knowledge Graph through Sentence-BERT-based semantic
retrieval and Natural
Language Inference. When the evidence retrieved from the graph is insufficient to support a reliable decision, the framework collects information from trusted web sources and assesses it using an
adversarial tribunal composed of support, contradiction, and judging
agents. A graph-aware confidence mechanism combines semantic similarity, NLI confidence, and structural graph evidence to determine whether internal knowledge is sufficient, thereby reducing unnecessary web
retrieval. Following verification, validated information is transformed into structured triples and incorporated into the Knowledge Graph, supporting the incremental expansion of the system's semantic
memory. Experimental evaluation on a curated COVID-19 misinformation
benchmark demonstrates that the proposed framework achieves an accuracy of 97.4% and a macro-averaged F1-score of 92.6% on resolved claims, outperforming a Llama~3.3~70B baseline, which obtains an accuracy of 87.7% and a macro-averaged F1-score of 86.3%.
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Artificial Intelligence · Cross-listed from cs.MA
Forward-Deployed Full-Stack Engineering for Autonomous Cloud MLOps
Sagar Srinivas Sakhinana and Venkataramana Runkana
Across industries, machine-learning systems support applications ranging from prediction and anomaly detection to
forecasting, optimization, and scheduling, yet operationalizing these systems requires coordinating application development, model pipelines, cloud infrastructure, security, deployment, monitoring, retraining, recovery, and rollback. We present an evidence-gated multi-
agent framework for transforming a natural-
language MLOps cloud engineering task into a verified repository and operational cloud deployment. The framework combines graph engineering, loop engineering, and
agent harness engineering. A stateful Graph Orchestrator coordinates specialized
agents for repository generation, review, execution, verification, release, and monitoring while governing workflow dependencies, evidence gates, retry bounds, recovery paths, and termination. Consequential lifecycle transitions proceed only when their required predicates are supported by verifiable execution or runtime evidence. Verification failures activate bounded reflection, repair, and re-verification, while runtime evidence of failure, drift, degradation, or policy violation can trigger bounded adaptation, recovery, or rollback.
Agent harness engineering constrains repository generation, review, and repair, artifact execution, and cloud operations through controlled capabilities and isolated execution environments. We realize the framework on Google Cloud Platform and evaluate repository completeness, controlled execution, evidence-gated transitions, cloud promotion, and bounded recovery. Our experimental results show that the framework prevents unsupported lifecycle transitions and drives each run toward either a verified operational deployment or an auditable terminal failure.
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Artificial Intelligence
LLMs Interpret, Embeddings Organize, Graphs Emerge: Agent-Driven Compilation of Scientific Knowledge
Shi-Ju Ran, Kun Zhang, Xi Wu et al. (+2 more)
Sustained scientific work requires a knowledge substrate that carries interpretation across tasks and preserves paths to source evidence. We call this process scientific knowledge compilation and implement it in ASKS, the
Agent-Driven Scientific Knowledge System. For each source, an LLM produces a readable Wiki view and machine-facing semantics. Deterministic checks convert the latter into a document-local GraphDelta, and
embedding geometry together with explicit graph rules integrates the proposed changes into persistent state. Each ingest is an inspectable state transition over accumulated knowledge, with compiled Wiki and graph views linked to the preserved source record. We examine this process by chronologically compiling 56 published papers from one research program. Branch survival, cross-paper support, lineage, coverage, and churn yield a source-traceable author research portrait centered on tensor-network methods, with branches into
quantum many-body research, tensor-network machine learning, and
quantum-AI-oriented directions. In this run, higher-level Hub organization remains stable and low-churn. Canonical-node growth is predominantly additive. Graph-level measurements and
navigation paths retain links to the source records from which they were compiled.
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All authors Shi-Ju Ran, Kun Zhang, Xi Wu, Liu-Si Yang, Wen-Jun Li
Artificial Intelligence · Cross-listed from cs.LG
Wide Learning: Learning to Reach Evidence
Junzhou Chen
Machine learning is usually evaluated after an evidence interface has been fixed. A
dataset, sensor suite, query
language, action set, or experimental protocol determines which observations can be obtained, and learning is judged by what it extracts from them. We study a complementary capability. A learner's state can determine which evidence-generating experiments it can reliably realise under bounded resources, even when primitive affordances remain fixed. We call this learner-relative experiment family its effective epistemic reach, and use Wide Learning for task-relevant learning-induced changes in that family.We formalise effective reach relative to learner state, deployment budget, reliability threshold, and evaluation distribution. In a controlled construction, two hidden worlds have exactly the same public observation law. An informative diagnostic exists in a fixed five-primitive substrate. Before calibration, one address attempt realises it with probability at most $2^{-10} = 1/1024$, below a pre-specified 0.95 threshold; after calibration, held-out realisation is 1. Public-channel total variation is 0, whereas the realised diagnostic has total variation 1, and sealed binary risk moves from approximately 1/2 to 0. The construction establishes that learning can change effective epistemic reach even when primitive affordances and deployment resources are held fixed. It opens a complementary evaluation question for learning systems: not only what they infer from available evidence, but what informative evidence experience teaches them to bring within reach.
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Artificial Intelligence
A Systematic Survey of Agentic Skills: Architecture, Lifecycle, and Security
Sanket Badhe, Deep Shah, Priyanka Tiwari et al. (+1 more)
Autonomous large
language model (LLM)
agents increasingly face reliability, context consumption, and execution stability bottlenecks when deployed on complex, long-horizon tasks. While monolithic
prompt engineering and stateless tool-calling paradigms struggle to scale, the field is rapidly converging toward agentic skills: modular procedural abstractions that externalize execution knowledge into reusable, executable, and portable artifacts. This paper establishes a unified systems foundation and reference architecture for the agentic skills ecosystem. We formalize skills as externalized procedural knowledge bridging high-level cognitive planning with deterministic execution environments, and systematically delineate the architecture across a nine-stage lifecycle: autonomous discovery, authoring and representation formats,
memory storage, dynamic
retrieval and routing, composition and orchestration, execution and repair, lifelong adaptation, empirical evaluation, and security governance. We further examine marketplace dynamics, public registries, and emerging
adversarial threat vectors, alongside runtime verification and defense mechanisms. Finally, we categorize system implementations across software engineering, operating system
navigation,
embodied robotics, and scientific discovery, while highlighting critical open challenges in continual learning and
benchmark realism. This work establishes agentic skills as a foundational paradigm for building scalable, robust, and verifiable autonomous
language agents.
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All authors Sanket Badhe, Deep Shah, Priyanka Tiwari, Nehal Kathrotia
Artificial Intelligence
Not Safe for All: Auditing the Dialect Penalty in Text-to-Image Safety Pipelines
Minkyu Kim, Juhwan Choi and YoungBin Kim
Text-to-image (T2I) safety guardrails fail to generalize equitably to non-standard dialects. Evaluating 23,080 paired
prompts across five English dialects, we formalize this failure as the dialect penalty, where filters trigger based on linguistic surface features rather than semantic intent. Text-level filters fail in opposing directions: NSFW-T over-flags benign dialect
prompts and LatentGuard over-flags toxic ones (bias gaps up to +28.29 pp), while the OpenAI Moderation API under-detects them. A controlled typo ablation confirms this penalty originates from flagging dialectal features, not generic out-of-distribution sensitivity. The pixel-level generator is largely dialect-agnostic; the penalty enters at text processing and cascades unevenly to post-hoc guardrails. We show this bias tracks training data imbalance and is mitigable via group-balanced retraining, with an ablation attributing the gain to balanced exposure rather than to the worst-group objective of GroupDRO (group distributionally robust optimization). Current pipelines systematically fail dialect speakers, an equity failure masked by mean accuracy
benchmarks. Our official code and
dataset are publicly available at https://github.com/minguinho26/dialect-penalty-t2i. Content Warning: This paper contains offensive, toxic, or disturbing text
prompts and generated images.
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Artificial Intelligence
Call Neighbours Yourself: Graph Walks with Destination-Conditioned On-Policy Self-Distillation
Yilun Liu, Boyu Luo, Yanran Tang et al. (+2 more)
Reasoning over text-attributed graphs (TAGs) requires large
language models (LLMs) to combine a node's text with evidence distributed across its neighbourhood. Existing methods fix the set of accessible neighbours before generation, forcing
reasoning to operate over a static context and preventing the model from acquiring missing evidence during inference. We argue that neighbour selection should itself be part of the
reasoning process. To this end, we propose Call Neighbours Yourself (CNY), a framework that enables LLMs to proactively explore graph neighbourhoods through
topology-constrained graph-walk actions. Instead of
reasoning over a pre-selected neighbour set, CNY exposes lightweight neighbour previews and learns when to expand candidate neighbours for additional evidence. To address the delayed-credit challenge of neighbour exploration, we introduce destination-conditioned on-policy self-
distillation, which retrospectively evaluates a selected neighbour after its content is revealed and converts the resulting change in action preference into an action-level training signal. Experiments on standard TAG
reasoning benchmarks under a unified raw-text setting show that CNY consistently outperforms fixed-context post-training baselines. Furthermore, the learned exploration policy transfers to unseen graphs and to a graph-level task not encountered during training. Code is available at https://github.com/superallen13/CNY.
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All authors Yilun Liu, Boyu Luo, Yanran Tang, Ruihong Qiu, Zi Huang
Artificial Intelligence · Cross-listed from cs.CL
SUP-MIMIC: A Multi-Task Clinical Diagnosis Benchmark for Evaluating LLMs' Robustness to Contradictory Evidence
Yi Yu, Bo Wang, Chong Feng et al. (+4 more)
Current evaluations of large
language models (LLMs) primarily focus on factual knowledge
retrieval, overlooking the fundamental challenge of navigating the complex, non-bijective mappings between clinical indicators and diagnoses. Existing
benchmarks fail to assess whether large
language models truly possess the
reasoning capability required for diagnostic ambiguity scenarios, where identical clinical presentations may correspond to different etiologies, and diagnostic convergence scenarios, where heterogeneous symptoms ultimately indicate the same disease. To address this issue, we propose SUP-MIMIC, a multi-task framework utilizing MIMIC-IV-v3.1 that comprises Basic Assessment (BA), Diagnostic Divergence Task (DDT), and Diagnostic Convergence Task (DCT). Specifically, DDT is designed to evaluate the model's "one-to-many" disambiguation capability among phenotypically similar cases, while DCT assesses the model's ability to identify "many-to-one" diagnostic patterns across different pathophysiological pathways. Comprehensive evaluation of state-of-the-art LLMs reveals substantial performance degradation on DDT and DCT compared to baseline tasks, exposing a systemic reliance on statistical shortcuts over genuine causal
reasoning. Our findings further highlight a conservative bias toward "healthy" predictions, implying non-trivial risks for missed diagnoses in realistic medical settings. This work establishes a rigorous methodology for quantifying clinical
reasoning robustness and provides a roadmap for enhancing the safety of
language models in clinical medicine.
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All authors Yi Yu, Bo Wang, Chong Feng, Ge Shi, Xia Liu, Ziyi Yang, Xuewen Shi
Artificial Intelligence · Cross-listed from cs.LG
HoopMind: A Real-Time Neural Game-Tree System for Opponent-Aware Possession Planning
Yibo Gong, Cong Guo and Jiacheng Ding
School coaches prepare for opponents with game film and intuition. The analytics tools of professional teams stay out of reach. We ask how far public data can close this gap. Professional basketball is our case study, chosen for its data rather than the league. We fuse five public sources into one per-shot
dataset of 4.23M shots over 21 seasons. The sources are shot locations, two play-by-play feeds, official matchup tracking, and player biometrics.
Alignment across them is 99.5% to 100%. We also report two data pitfalls that are easy to miss. We then model a half-court possession as a sequential game. Shot values come from ShotNet, an
embedding multilayer perceptron (MLP). On a held-out season it beats a zone-rate baseline and a logistic baseline, and its probabilities are well calibrated. A depth-limited expectimax search then solves the offensive decision tree, with branch-and-bound
pruning to keep it real time. All training runs offline, so the online system stays light. A scouting planner and a playable simulator both run in a single browser page.
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Artificial Intelligence · Cross-listed from cs.SD
PhysWave: Physics-Guided Latent Diffusion Models for Controllable Spatial Audio Generation
Lingfeng Yao, Chenpei Huang, Xingke Yang et al. (+5 more)
Text-to-spatial audio generation, such as text-to-First-Order Ambisonics (FOA), provides a convenient way to create spatial audio for billion-dollar gaming and film industries. However, existing text-to-FOA methods are largely data-driven and may produce audio that violates acoustic relations between source direction and distance. They also separate descriptive and parametric control, forcing users to trade usability for precision. In this paper, we present PhysWave, a physics-guided latent
diffusion model for controllable text-to-FOA generation. PhysWave unifies natural-
language and trajectory control through a shared waypoint-caption representation, and augments
diffusion training with two differentiable acoustic priors: spherical-harmonic direction consistency and inverse-square distance consistency. To support dynamic spatial generation, we further construct a 300K-clip FOA
dataset with diverse sound categories and source trajectories. Extensive results show that the proposed priors help PhysWave generate spatially consistent FOA audio while maintaining competitive audio quality. Further analyses show that these physics priors improve spatial consistency during training and can also be used as inference-time guidance for training-free spatial refinement.
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All authors Lingfeng Yao, Chenpei Huang, Xingke Yang, Ziye Geng, Changqing Luo, Hao Wang, Jiang Liu, Miao Pan
Artificial Intelligence · Cross-listed from cs.CL
Evaluating LLMs on Conversational Text-to-SQL under Chain Ambiguity and Intent Drift
Yujia Liu, Jiayan Lin, Zijin Hong et al. (+6 more)
Recent advances in large
language models (LLMs) have established conversational text-to-SQL as a practical interface between users and databases, often involving multiple turns of clarification and revision. However, existing
benchmarks primarily evaluate execution accuracy, leaving the unfolding and shifting of user intent across turns largely uncovered. To address this, we introduce TIDE-Bench, a
benchmark for conversational text-to-SQL under chain ambiguity and intent drift evaluation, targeting two recurring patterns: chain ambiguity, where an underspecified question triggers layered clarification with conditional dependencies, and intent drift, where the user retracts and replaces a previously committed request element. Built on 514 anchor SQLs from BIRD, TIDE-Bench comprises 1,542 samples and introduces dedicated metrics for chain identification and drift recognition-resolution beyond execution accuracy. Evaluating 12 advanced LLMs reveals a persistent chain identification bottleneck unaffected by clarification frequency, a wide drift recognition-resolution gap, and overlap between failure modes when jointly activated. The corresponding code of TIDE-Bench is released for further research.
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All authors Yujia Liu, Jiayan Lin, Zijin Hong, Zheng Yuan, Shengyuan Chen, Hao Chen, Qinggang Zhang, Xiao Huang, Feiran Huang
Artificial Intelligence · Cross-listed from cs.RO
AGM: Achievement-Grounded Memory for Closed-Loop Agents with Frozen VLA Policies
Hongbo Gao, Zeyu Ni, Xin Wen et al. (+4 more)
Frozen
vision-
language-action (VLA) policies offer broad manipulation skills but execute open-loop action chunks without tracking task progress, so the
agent cannot reliably decide whether to continue, retry, or terminate. External
memory is a natural remedy, yet we find it can be harmful when attempted actions are recorded as completed progress: transient execution failures become persistent task-state errors, and such a
memory can underperform no progress
memory at all. We propose Achievement-Grounded
Memory (AGM), a lightweight closed-loop framework for frozen VLA policies. AGM represents a task as a static subgoal sequence with a dynamic progress pointer and advances the pointer on physically verified achievement rather than on attempts. Proprioceptive gripper-load cues decide when to verify; coherent point tracking verifies grasps, and
language-conditioned cross-view comparison, read by a single trained 2.43M-parameter verification head, verifies placements. The policy, tracker, and encoder remain frozen, the head is the only trained component, and deployment needs no auxiliary
vision-
language model. On the RoboMME Counting
benchmark, AGM reaches 100.0% on PickXTimes and 84.0% on BinFill, surpassing the strongest
memory-augmented baseline by 7.7 points on the four-task average, and the gains carry over to a physical
robot, where AGM reaches 100.0% and 82.0%. These results suggest that reliable
embodied memory depends more on disciplined state updates than on
memory capacity.
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All authors Hongbo Gao, Zeyu Ni, Xin Wen, Siyu Xu, Ruifeng Li, Rui Xu, Yunke Wang
Artificial Intelligence · Cross-listed from physics.soc-ph
Integrating adaptive human behavior into epidemic models with large language models
Yicheng Mao, Haoyang Li, Rob Deardon et al. (+1 more)
Infectious disease transmission is shaped by patterns of human interaction, which adapt as epidemic conditions change. Capturing these context-dependent behaviors remains a fundamental challenge for epidemic models. Here, we recast this challenge by using large
language models (LLMs) to represent adaptive human behavior within mechanistic epidemic models. We operationalize this idea through Generative Adaptive Behavioral Layer for Epidemics (GABLE), which adapts LLMs to infer behavioral responses to epidemic and policy conditions and translates them into age-structured contact
matrices coupled to a mechanistic epidemic model. Applied to COVID-19 in France, GABLE reproduced responses in population mixing and age-specific contact structures that remained epidemiologically informative. In short-term
forecasting, LLM-generated contact
matrices outperformed mobility-driven
matrices derived from real-world mobility data, with the largest gains at longer horizons. GABLE also extends beyond
forecasting to prospective policy evaluation by projecting behavioral and epidemic responses to candidate interventions before implementation. When supplied with subsequently implemented policies, GABLE reproduced epidemic trajectories and generated distinct responses to alternative policy timing and composition. By leveraging LLMs as a flexible behavioral layer, GABLE provides a framework for coupling context-sensitive behavioral generation with epidemic dynamics.
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All authors Yicheng Mao, Haoyang Li, Rob Deardon, Hongru Du
Artificial Intelligence · Cross-listed from cs.CL
The Emergent Symbolic Structure of Artificial Neural Networks
R. Thomas McCoy, Paul Soulos, Tal Linzen et al. (+1 more)
Modern systems in artificial intelligence (AI) somehow excel in domains for which they seem poorly suited. Intelligence has traditionally been modeled as operating over structured combinations of symbols, such as logical formulas. However, the strongest modern AI systems are based on neural networks, which instead represent information in continuous vectors. Vectors seem inadequate for capturing the structure of
language, logic, and other cognitive domains, yet neural networks achieve impressive performance in these areas. How do they do it? In this work, we propose a potential answer: Despite appearances, perhaps the internal representations of neural networks implicitly realize symbolic structure. In support of this hypothesis, we show that the vector representations of a variety of neural networks can be closely approximated with symbolic structures: we can replace the network's entire representation-generating process with a closed-form equation instantiating a symbolic structure, and the network's behavior remains largely unchanged. This finding holds for both small-scale neural networks trained to manipulate lists as well as large
language models (LLMs) operating in four domains that are central in symbolic traditions: arithmetic, logic, computer code, and
language. Further, our symbolic approximation allows us to modify an LLM's behavior in targeted ways via precise interventions on its internal representations, showing that the LLM's behavior is reliant on the symbolic structures we have identified. This work provides a potential way to reconcile longstanding symbolic conceptions of intelligence with the vector-based nature of modern AI.
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All authors R. Thomas McCoy, Paul Soulos, Tal Linzen, Paul Smolensky
Artificial Intelligence · Cross-listed from cs.CL
Argument-Aware Semantic Alignment of Normative Texts: A Toulmin-Based Neuro-Symbolic Approach
William Schroeder
Semantic
alignment between specialized normative texts is challenging when equivalent requirements use different terms, syntax, and levels of abstraction. Lexical overlap, distributional
embeddings, and semantic similarity capture topical relatedness but often miss the argumentative structure by which normative claims are supported, qualified, and justified. This paper asks whether explicit argument structure adds information complementary to neural semantics for aligning requirements. We treat cross-standard control mapping as argument-aware semantic
alignment and build a neuro-symbolic pipeline that combines neural text representations with Toulmin features. An LLM explicitation step identifies claims, grounds, warrants, qualifiers, and backing and reconstructs enthymemes. These feed an
alignment model via argument-aware similarity and structural features. On a NERC-CIP to NIST-CSF mapping
benchmark, argument-derived features improve
alignment over a neuro-symbolic semantic baseline. Feature selection shows especially strong signal from warrant-related features, indicating that the link between a claim and its supporting
reasoning is not captured by conventional similarity alone. A compact claim–grounds–warrant subset remains competitive with the full Toulmin feature set. The results give preliminary evidence that argument structure is a useful intermediate representation for aligning specialized normative texts. Cybersecurity standards are used as a controlled testbed, not as proof of domain-independent generalization. The argument graphs produced by LLM explicitation may also support later work on
retrieval,
reasoning, and explanation over normative text.
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Artificial Intelligence · Cross-listed from cs.LG
On the Plasticity Collapse in Continual Machine Unlearning
Yingdan Shi, Xiang Xu, Kaize Ding et al. (+2 more)
Machine unlearning enables deep neural networks to selectively remove the influence of specific data in response to
privacy and regulatory requirements. While prior work largely studies single-shot unlearning, real-world systems must accommodate continual unlearning, where multiple unlearning requests occur sequentially over time. In this work, we identify a fundamental limitation of this setting: plasticity collapse, a progressive breakdown in a model's ability to effectively forget. Through theoretical analysis of continual unlearning dynamics, we show that continual unlearning operations accumulate
geometric constraints in parameter space, leading to saturated subspaces that restrict future updates. This structural effect induces two distinct failure modes: (1) Forward failure – diminishing forgetting quality for subsequent tasks, and (2) Backward failure – spontaneous re-memorization of previously forgotten information. Extensive experiments across multiple architectures,
datasets, and methods in image classification confirm that plasticity collapse is not an artifact of specific implementations, but a pervasive phenomenon inherent to continual unlearning. Our findings reveal a critical barrier to the long-term reliability of machine unlearning systems and motivate the development of plasticity-preserving unlearning
algorithms. Our code is available at https://github.com/TIML-Group/Continual-Machine-Unlearning-Plasticity-Collapse
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All authors Yingdan Shi, Xiang Xu, Kaize Ding, Alfred O. Hero, Ren Wang
Artificial Intelligence · Cross-listed from cs.LG
Denoising as Projection: Constrained Optimization with Gradient-Guided Diffusion
Runyu Zhang, Jiawei Zhang, Gioele Zardini et al. (+2 more)
Diffusion models are increasingly used not only for sampling from learned data distributions, but also for generating samples that optimize task-specific objectives. A common approach is to guide the reverse
diffusion process using gradients of an external objective. However, when the data distribution is supported on a structured feasible set, such as a
manifold or a constraint set, gradient guidance can move samples away from the learned data geometry. In this paper, we study a simple projected-gradient-guided
diffusion update based on the observation that the Stein denoising operator can act as an approximate projection onto the data geometry. The proposed update incorporates the objective gradient inside the denoising step, yielding an inference-time method that uses only a pretrained denoiser and gradient evaluations. We analyze this update as an inexact projected-gradient method for constrained optimization over learned feasible geometries. Our theory covers three settings: linear
manifolds, compact convex feasible sets, and compact Riemannian submanifolds. In all these settings, we prove descent and finite-time convergence guarantees. Numerical experiments support the theoretical interpretation and illustrate how the proposed update balances objective descent with preservation of the learned geometry.
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All authors Runyu Zhang, Jiawei Zhang, Gioele Zardini, Saurabh Amin, Asuman Ozdaglar
Artificial Intelligence · Cross-listed from cs.MA
Generalizable Multi-Agent Planning from Signal Temporal Logic Specifications via Diffusion
Joe Eappen, Zikang Xiong, Shreyash S. Iyengar et al. (+1 more)
Multi-
agent systems in the real-world (e.g., drone swarms, autonomous cars, warehouse
robots) must satisfy rich, temporal tasks while avoiding collisions. Signal Temporal Logic (STL) elegantly encodes such objectives, but current STL planning methods face critical limitations. State-of-the-art optimization-based approaches can handle arbitrary STL specifications but struggle with scalability, becoming computationally impractical as the number of
agents grows. Learning-based methods efficiently handle a large number of
agents with rapid planning times but fare poorly when deployment-time objectives differ from those used during training, and do not support planning tasks that require different specifications to be ascribed to different
agents (i.e., heterogeneity) or team-level specifications requiring coordination of multiple
agents. This fundamental trade-off between generalizability and scalability presents a challenge for realizing multi-
agent STL planning
algorithms in practice. To overcome this challenge, we introduce a new
diffusion method for multi-
agent planning with STL specifications. Using a differentiable approximation of STL, we integrate the STL gradient in the denoising process, making our approach generalizable to novel formulas whose predicates are placed anywhere within the goal region covered during training, while achieving the same scalability as existing learning-based methods. Our method supports heterogeneous specifications, and by using
diffusion models, naturally enhances plan diversity, thereby significantly reducing safety-related violations (e.g., collisions) among
agents. A detailed evaluation study justifies the utility of STL-guided
diffusion-based multi-
agent planners for constructing generalizable, scalable, and diverse plans.
Videos and code are available at https://www.jeappen.com/diff-ma-stl/ and https://github.com/jeappen/diff-ma-stl .
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All authors Joe Eappen, Zikang Xiong, Shreyash S. Iyengar, Suresh Jagannathan
Artificial Intelligence · Cross-listed from cs.CY
Applications of Risk Science to AI Fairness Evaluation: Principles, Challenges, and Best Practices
Kyra Wilson, Sabrina Kang, Saloni Dash et al. (+1 more)
Scholarly work which aims to describe potential societal impacts (e.g., risks) of proliferating technology (especially related to artificial intelligence or other algorithmic systems) is likely to have an impact beyond the scientific communities it was written for, given that general society itself is a primary object of study. However, it is an open question whether the current practices of AI evaluation scholarship follow the principles and best practices established by risk science, which aims to systematically generate knowledge related to understanding, assessing, communicating, managing, and governing risk. In this work, we examine this in depth by conducting a literature review of scholarly works purporting to evaluate the bias or
fairness of technological systems used for tasks related to hiring and employment. Through analysis of 22 common
fairness evaluation metrics and studies using them, we find that most characterize the severity of bias- or
fairness-related consequences but do not follow best practices to characterize the uncertainty around either the occurrence of these consequences or severity estimates. Next, we conduct a case study of
fairness evaluation for an AI-mediated resume screening task and demonstrate how principles of risk science can be incorporated into such an evaluation. Finally, we propose the AI Risk Report Card, which facilitates the reporting and communication of risk assessment results to stakeholders in positions to act based on the predicted risks. The outcomes of these activities suggest that further research at the convergence of risk science and AI evaluation can lead to advancements in AI assessments of societal impact by enabling shared frameworks to evaluate and discuss AI risks both within and outside of the scientific community.
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All authors Kyra Wilson, Sabrina Kang, Saloni Dash, Aylin Caliskan
Artificial Intelligence · Cross-listed from cs.CL
MUDDLE: Measuring Understanding of Documents under Distractor and Length Effects
Jason Luo, Saibilila Abudukelimu, Judy Song et al. (+4 more)
Document question-answering systems increasingly answer questions over collections of retrieved documents rather than one clean source, so robustness to distracting context matters as much as reading ability. When such systems fail, it is often unclear whether the context was too long or the distractors were too close to the topic, because prior work tends to conflate these two effects. We present MUDDLE, a controlled
benchmark that separates them. MUDDLE uses 270 human-annotated questions, each tied to a single source document, and instantiates every question in five conditions: the source alone, the source with two or four topically similar hard negatives, and the source with two or four random distractors. The random distractors are matched to the hard negatives in length and provenance, so an accuracy gap between the two arms reflects topical similarity rather than length. All five conditions are rendered in markdown, page images, and raw PDF, but the distractor sweep reported here is run in markdown, since a source plus its distractors exceeds current image and PDF input limits. We score answers with an LLM judge across three model families. In the complete markdown sweep, hard negatives lower accuracy more than length-matched random documents at both context sizes for gpt-5-mini, while random documents stay near the no-distractor baseline. The effect is small but directionally consistent, and for gpt-5-mini hard negatives significantly underperform length-matched random distractors when pooled across context sizes. We release the data and evaluation code for a reproducible study of context degradation.
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All authors Jason Luo, Saibilila Abudukelimu, Judy Song, Andrew Feng, Shivank Garg, Vasu Sharma, Kevin Zhu