Artificial Intelligence · Cross-listed from cs.LG
Proxy OPD: On-Policy Distillation with Transferable Relative Proxy Update
Daocheng Fu, Rong Wu, Yu Yang et al. (+7 more)
Post-training for large
language models typically couples policy exploration with model optimization, hindering the reuse of high-reward behaviors from policy exploration. While on-policy
distillation alleviates this by consolidating independently optimized experts, its reliance on matching absolute expert distributions can yield suboptimal supervision, especially when the target model possesses a different prior or already surpasses the expert's capabilities. To alleviate this, we introduce Proxy OPD (P-OPD), an asynchronous post-training framework that transfers reward-induced policy improvements rather than absolute policy distributions. P-OPD first optimizes a proxy policy via reward feedback. It then extracts the relative distributional changes between the proxy's initial and optimized states, transferring these directional updates through the target model's own on-policy trajectories while retaining the target policy as the reference. This decoupled formulation requires the proxy to provide merely a useful direction of improvement rather than superior absolute capability, enabling update signals from older or weaker proxies to remain highly effective. Systematic experiments on Qwen3-family models across mathematical
reasoning and code generation demonstrate that P-OPD consistently enhances already strong target models. Furthermore, transfer intensity can be dynamically modulated through signal scaling, making the extracted update signals seamlessly reusable across diverse model variants and training configurations. These results establish relative policy updates as highly reusable, adjustable assets for scalable, reward-based post-training.
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All authors Daocheng Fu, Rong Wu, Yu Yang, Jianbiao Mei, Licheng Wen, Pinlong Cai, Xuemeng Yang, Yong Liu, Botian Shi, Yu Qiao
Artificial Intelligence
Comparative Analysis of GAT and BERT for Human-Like Playtesting
Kleio Fragkedaki, Theodoros Panagiotakopoulos, Matteo Biasielli et al. (+1 more)
Accurately modeling and understanding player experience is crucial for designing engaging puzzle games. To achieve this, a common approach involves collecting diverse user data to train predictive playtesting models that mimic player behavior. However, existing data-driven methods often lack the ability to capture the full range of player strategies and require extensive feature engineering and network architecture modeling. This limitation becomes particularly evident when new game mechanics or features are introduced, which necessitate continual adjustments to the models. To addrss these challenges, we propose a more generalized representation that reduces - or even eliminates - the need for ongoing feature-engineering maintenance. Specifically, we investigate two general-purpose network architectures: (a) a
transformer-based model (BERT) and (b) a graph
attention model (GAT), both of which are designed to effectively capture the relational structure of Candy Crush Saga (CCS) game boards. Our experiments compare these approaches to Convolutional Neural Networks (CNN) baselines, revealing better performance on challenging board configurations and underscoring the benefits of our generalizable representation.
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All authors Kleio Fragkedaki, Theodoros Panagiotakopoulos, Matteo Biasielli, Hui Wang
Artificial Intelligence · Cross-listed from cs.RO
See like a Robot: Robot-Centric Pointmaps for VLA Models
Byungkun Lee, Dongyoon Hwang, Dongjin Kim et al. (+4 more)
Vision-
language-action (VLA) models require 3D spatial
reasoning, yet RGB observations encode
robot-object geometry only implicitly. Lifting depth with camera intrinsics makes this geometry explicit as dense, image-aligned pointmaps, but their camera-frame coordinates depend on camera placement. We propose SeeR-VLA, which transforms pointmaps into a
robot-centric frame with an end-effector origin and
robot-base-aligned axes. An encoder initialized from pretrained RGB weights extracts pointmap features, which are added to corresponding RGB
tokens without increasing the
token count. Across 24 RoboCasa tasks and four real-world tasks, SeeR-VLA improves average success over RGB-only $\pi_{0.5}$ by 6.4 and 32.5 percentage points, respectively. It also exceeds the strongest evaluated 3D-augmented baseline, PointVLA, by 3.5 and 23.7 percentage points, respectively. Beyond these gains, our ablations clarify how coordinate choices affect VLA performance, showing that end-effector centering is most effective with
robot-base-aligned axes. The benefits grow as training viewpoints diversify, highlighting the importance of using
robot-frame pointmaps when learning from diverse camera configurations.
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All authors Byungkun Lee, Dongyoon Hwang, Dongjin Kim, Hojoon Lee, Hyunseung Kim, Jaegul Choo, Minho Park
Artificial Intelligence · Cross-listed from cs.LG
IG-GAN: A Generative Adversarial Network for Aerodynamic Data Generation Based on Intrinsic Geometry
Ying Yan, Liwei Hu and Xiaoming Zhang
Existing generative models learn data distributions in flat Euclidean space. However, most data in our real world are
manifolds embedded in high dimensional Euclidean space. Therefore, we propose an intrinsic-geometry-based generative
adversarial network (IG-GAN) for data generation in the field of aerodynamics. The generator of the IG-GAN represents aerodynamic data as a piecewise smooth
manifold constructed by Bézier surfaces, and the generator tries to learn the coefficients of each Bézier surface to further combine multiple Bézier surfaces into a smooth
manifold automatically. The discriminator in the IG-GAN is a radial-basis-function based discriminator (RBF-D). Experimental results show that IG-GAN achieves lower predicted Mean Squared Errors (MSEs) than those of three baselines. Specifically, on the Burgers' equation
dataset, IG-GAN reduces the predicted MSE of velocity u by 97.41% compared with state of the art SSL-
Transformer. Additionally, on the ONERA M6 aircraft
dataset, IG-GAN reduces the overall MSE of nine aerodynamic coefficients by 82.95% compared with SSL-
Transformer.
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Artificial Intelligence · Cross-listed from cs.LG
Agentic Skill Optimization over Lie Algebroids
Sridhar Mahadevan
Agentic systems increasingly improve themselves by editing skills:
prompts, rubrics, plans, tool contracts, examples, validators, and traces. Skill edits are not independent coordinates in a vector space: they are local repairs to structured artifacts whose effects are observed only after rollout, validation, and critique. Distinct edits can have the same immediate visible effect while differing in routing context, template state, guardrail scope, or future composability. The order of edits can matter as well: repairing a schema before a normalization rule need not be equivalent to applying the same edits in the reverse order. This paper introduces a new framework for skill optimization called LASKO, for Lie Algebroid SKill Optimization. LASKO models typed, anchored Markdown skills as the base category and available edit policies as sections of a controlled Lie algebroid with anchor $\rho$. The anchor maps an edit policy to its visible Markdown effect; the kernel $\ker(\rho)$ represents latent template, routing, or implementation structure; and the algebroid bracket measures noncommuting edit composition. As shown in the paper, LASKO achieves order-of-magnitude speedups in skill optimization in our preliminary
benchmark results, primarily because it substitutes inexpensive Lie-bracket screening tests that run in microseconds, before investing in expensive validations that require running large
language models. On a causal extraction from natural
language task, LASKO achieved a speedup of almost $15 \times$ compared to a brute-force approach that validated all edits by running them through a DeepSeek V3.1 4-bit model with 671B parameters.
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Artificial Intelligence
Enhancing Query Efficiency for d-DNNF Representations Through Preprocessing
Jean Marie Lagniez and Emmanuel Lonca
In this paper, we investigate preprocessing techniques aimed at improving the efficiency of accessing models of propositional formulas represented in conjunctive normal form (CNF). We focus on three fundamental tasks: uniform sampling, direct model access, and model enumeration. Our analysis reveals that most state-of-the-art preprocessors, when they do not preserve formula equivalence, are generally unsuitable for these tasks. In contrast, we demonstrate that preprocessors which preserve model counts can be effectively leveraged, provided relevant preprocessing information is maintained. To validate our approach, we perform extensive experiments on a diverse suite of
benchmarks from multiple domains. The experimental results show that our preprocessing methods are both efficient and robust, yielding significant performance improvements for model access queries when CNF formulas are compiled into d-DNNF representations.
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Artificial Intelligence · Cross-listed from cs.CL
LightMem-Ego: Your AI Memory for Everyday Life
Yijun Chen, Boyi Xiao, Yixian Zhao et al. (+10 more)
Personal AI assistants on mobile and wearable devices continuously perceive users' daily lives through visual and audio streams. However, answering queries about past experiences requires lightweight
multimodal memory that can continuously accumulate, organize, and retrieve long-term experiences, which remains challenging. To address this challenge, we present LightMem-Ego, a lightweight streaming
multimodal memory system for everyday-life assistance. The system continuously captures
egocentric visual and audio streams, aligns them on a shared timeline, and organizes them into a hierarchical
memory consisting of current, short-term, and long-term
memory. Given a user query, LightMem-Ego dynamically routes
retrieval to the appropriate
memory level and generates answers grounded in
multimodal evidence. The demonstration can be deployed on smartphones and AI glasses, supporting object finding, conversation recall, life summarization, routine discovery, and personalized assistance. Code is available at https://github.com/zjunlp/LightMem-Ego.
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All authors Yijun Chen, Boyi Xiao, Yixian Zhao, Haoting Xia, Buqiang Xu, Jizhan Fang, Yanya Li, Yaqi Zheng, Xuehai Wang, Zirui Xue, Liuxin Zhang, Hui Li, Ningyu Zhang
Artificial Intelligence · Cross-listed from cs.IR
FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis
Marlena Flüh, Soo-Yon Kim, Carolin Victoria Schneider et al. (+1 more)
Retrieval-Augmented Generation (RAG) addresses the limitations of Large
Language Models (LLMs) when providing responses to domain-specific questions. Graph-based RAG approaches, such as GraphRAG, enhance
retrieval by capturing semantic relationships within knowledge graphs (KGs). While the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) are becoming prevalent for scientific data management, especially in complex domains such as medicine, existing RAG approaches lack a structured FAIRification of the underlying knowledge resources. This lack limits their potential for FAIR information
retrieval in these domains. To address this gap, we introduce FAIR GraphRAG, a novel framework that integrates FAIR Digital Objects (FDOs) as the fundamental units of a graph-based
retrieval system. Each graph node represents an FDO that incorporates core data, metadata, persistent identifiers, and semantic links. We leverage LLMs to support schema construction and automated extraction of content and metadata from data sources. The framework was co-designed by physicians and computer scientists to ensure technical and clinical relevance. We apply FAIR GraphRAG to a biomedical
dataset in gastroenterology, demonstrating its applicability to RNA-sequencing data. Beyond ensuring adherence to the FAIR principles, FAIR GraphRAG significantly improves question answering accuracy, coverage, and explainability, particularly for complex queries involving metadata and ontology links. This work shows the feasibility of combining FAIR data practices with graph-based
retrieval techniques. We see potential for applying our approach to other specialized fields such as education and business.
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All authors Marlena Flüh, Soo-Yon Kim, Carolin Victoria Schneider, Sandra Geisler
Artificial Intelligence · Cross-listed from eess.SY
A Multimodal Dataset for Large Language Model Applications in the Energy Domain
Costas Mylonas and Magda Foti
This paper presents the mAIEnergy
dataset, an open-access,
multimodal corpus developed to support Large
Language Model (LLM) applications in the energy sector. The
dataset integrates approximately 50,000 textual documents, 20,000 images, 25 million numerical time series records, and 2 million geospatial and relational data entries. It includes policy and regulatory texts, scientific articles and news articles, satellite and contextual imagery, electricity system measurements, weather observations, statistical indicators, and geospatial representations of energy infrastructure and related entities. All data have been harmonized into structured, ready-to-use formats, accompanied by consistent metadata and reproducible data
retrieval and preparation workflows. The
dataset can serve as a foundational energy knowledge base, allowing energy stakeholders to integrate additional open-source or proprietary data. The mAIEnergy
dataset adheres to Findable, Accessible, Interoperable, and Reusable (FAIR) principles, enhancing its applicability for AI-driven energy research, modeling, and decision-making.
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Artificial Intelligence
The Ebb and Flow of Multimodal Focus: Scheduling Visual Relay Windows for Grounded VLM Reasoning
Wencheng Ye, Yi Bin, Yujuan Ding et al. (+7 more)
Vision-
language models increasingly succeed on
multimodal reasoning benchmarks, yet their visual evidence often becomes unstable once it enters the
language stack, weakening evidence-grounded
reasoning. To understand this fragility, we examine the internal dynamics of VLMs through a mechanistic lens and uncover a stable three-stage redistribution of
multimodal attention focus across depth: an early question-conditioned organization, a critical middle visual-dominant relay, and a late return to answer formation. We operationalize the middle phase as the Visual Relay Window (VRW), and show that its geometry varies with task demand, is causally tied to grounded generation, and distinguishes unsupported answers from stronger
reasoning trajectories. Guided by this internal rhythm, we propose TRACE, a task-adaptive inference-time control framework with lightweight trained modules. It reshapes relay allocation during prefill and preserves assembled visual support after handoff during decoding. Across four open-weight VLM backbones and seven
benchmarks, TRACE delivers large gains on grounding-sensitive settings, improving them by 4.33 points on average and by up to 6.6 points, while also improving
reasoning-heavy tasks. These results show that explicitly controlling
multimodal focus across depth offers a unified and effective mechanism for strengthening evidence-grounded
multimodal reasoning.
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All authors Wencheng Ye, Yi Bin, Yujuan Ding, Hongye Fang, Zheng Wang, Xing Xu, Jingkuan Song, Yun Zhang, Sirui Da, Heng Tao Shen
Artificial Intelligence
Omni-Decision: Evidence-Ledger Planning for Omni-Modal Agents
Ming Ma, Yi Zhu, Yiran Zhong et al. (+6 more)
Omni-modal
agents must seek evidence across
video, audio, web pages, and computation to answer questions. Their main bottleneck is planning: noisy
multimodal observations accumulate in conversation history and disrupt later decisions, while
multimodal models have limited capacity for multi-step planning. Controlled backend replacements support this diagnosis: replacing the planner causes a much larger performance loss than replacing the perception backend. We present Omni-Decision, an omni-modal
agent built on evidence-ledger planning: it replaces the growing dialogue history with an explicit evidence ledger that records what evidence is still missing, what has been confirmed, and where records conflict. A critic reads each noisy observation and passes only the usable content to the ledger, discarding the rest, so the planner works from a compact context throughout the task. Each run records the state, action, and verdict at every step, and supervised fine-tuning and decision-level reinforcement learning on these trajectories further improve the planner. Omni-Decision achieves state-of-the-art accuracy of 81.4% on OmniGAIA at approximately 43% of Gemini-3.1-Pro's cost per question, and 65.0% on WorldSense long-
video understanding, level with the strongest end-to-end model.
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All authors Ming Ma, Yi Zhu, Yiran Zhong, Feida Zhu, Yuhao Wang, Junhan Shi, Lingrui Mei, Tianming Yang, Steven Hoi
Artificial Intelligence · Cross-listed from cs.CV
Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation
Ritu Yadav, Andrea Nascetti and Yifang Ban
Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring, and
climate policy, where both accuracy and reliability are critical. Yet most deep learning models yield only deterministic predictions, providing no indication of per-pixel reliability. These regression tasks are inherently challenging due to heterogeneous land surfaces, skewed target distributions, sensor noise, and signal saturation at high target values, making uncertainty (UC) estimation essential for reliable inference. We address this gap by modeling aleatoric uncertainty using year-long Sentinel-1 SAR and Sentinel-2 MSI time series, proposing two complementary approaches: (i) Gaussian UC, which jointly predicts mean and standard deviation under a Gaussian assumption, and (ii)
Quantile UC, which estimates the 10th, 50th, and 90th
quantiles to capture asymmetric and heteroscedastic error distributions. Both models are evaluated on three representative EO regression tasks at 10 m spatial resolution. Results show that both approaches match or surpass deterministic
benchmarks and existing global products, while delivering well-calibrated, interpretable, and operationally useful confidence estimates. Notably, both models outperform the current 10 m state-of-the-art uncertainty-aware model for canopy height estimation. Our implementation will be available at: https://github.com/RituYadav92/EO-Regression-Uncertainty-Estimation
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Artificial Intelligence · Cross-listed from cs.CL
Agentic Routing: The Harness-Native Data Flywheel
Xinchen Liu, Hang Zhou, Yingjie Zong et al. (+12 more)
Large
language model
agents are increasingly executed not by a single model call, but by an execution harness that manages observation, context, control, action, state, and verification. At the same time, frontier and open models are becoming structurally specialized: a model that is strong at code editing, long-context recovery, tool use, mathematical
reasoning, or low-latency response may not dominate on the other axes. This makes model selection inside an
agent a core systems problem rather than a per-query serving trick. Existing routing methods mostly optimize single-turn cost-quality trade-offs and therefore miss the execution state, intermediate failures, and feedback loops that make
agents different from chat completion. We propose Harness-Native agentic routing, a step-level routing paradigm that selects either a single best-fit model for cost-effective execution or multiple complementary models for ensemble-style accuracy improvement, conditioned on the full harness state. The key insight is that every routing decision naturally produces a structured data record – consisting of the query, harness state, model choice or model set, execution trace, outcome, and cost – whose labels are supplied by the environment rather than by the router itself. These records form a harness-native data flywheel: execution traces train better routers and harness-native models, which improve cost-quality trade-offs and generate more traces under the same budget. We instantiate this idea in OpenSquilla with a four-layer routing stack, an open LightGBM cold-start ranker, and a staged router-model path that turns logged arena records into progressively stronger routing policies. The report studies singleton and multi-model routing on agentic
benchmarks including DRACO and PinchBench, and argues that agentic routing is not merely cost control, but a data engine for
agent-native training.
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All authors Xinchen Liu, Hang Zhou, Yingjie Zong, Yuchuan Tian, Liuyang Song, Shuo Zhang, Yulong Li, Wei He, Mengyu Zheng, Runke Liu, Siyang Cheng, Xiang Kuang, Hailin Hu, Kai Han, Yunhe Wang
Artificial Intelligence
StructAgent: Harness Long-horizon Digital Agents with Unified Causal Structure
Wenyi Wu, Sibo Zhu, Kun Zhou et al. (+3 more)
Recent advances in large
language models (LLMs) and
vision-
language models (VLMs) have enabled increasingly capable digital
agents for computer use. However, real-world tasks are often long-horizon and involve evolving contexts containing accumulated observations, intermediate edits, failed attempts, and partially completed executions. Existing
agents typically operate over raw interaction history, making task progress difficult to interpret, verify, and recover, which ultimately limits reliable long-horizon execution. In this paper, we argue that addressing this challenge requires explicitly structuring both the
agent's state and workflow around a unified causal representation of task progress. We present StructAgent, a state-centered framework that introduces a unified state for maintaining compact, verifiable task progress and a structured workflow that regulates progress through verifier-backed state transitions. Building on this design, StructAgent further enables explicit progress checkpointing, evidence-driven task completion, targeted failure recovery, and tool-supported execution, while ensuring that all progress updates remain grounded in verification. Extensive experiments demonstrate that StructAgent consistently improves a wide range of LLM and VLM backbones on long-horizon computer-use tasks. On OSWorld-Verified, it improves Qwen3.5-9B from 27.0% to 46.9% success rate and Qwen3.5-27B from 31.6% to 62.2%, while achieving a new open-source state of the art of 78.9% with MiniMax-M3. Moreover, the same framework generalizes beyond desktop environments to Minecraft, demonstrating the generality of our design.
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All authors Wenyi Wu, Sibo Zhu, Kun Zhou, Aayush Salvi, Zixuan Song, Biwei Huang
Artificial Intelligence · Cross-listed from cs.RO
A Glimpse into Long-term Physical Coexistence with Intelligent Robots
Weiqi Jin, Peijun Tang, Kuncheng Luo et al. (+5 more)
Long-term physical coexistence with intelligent
robots requires more than capable
robot policies. A persistent robotic assistant must support diverse user-facing interfaces, maintain long-horizon
memory of people and preferences, coordinate across
robot embodiments, and translate human intent into safe physical execution. We introduce PHILIA, a multi-
robot agent built around a
robot gateway abstraction. PHILIA retains the rich interaction and tool ecosystem of OpenClaw while exposing
robot-local runtimes, onboard perception,
navigation, speaker, and
robot policies through a unified capability interface. This design decouples low-frequency, high-semantic
agent reasoning from high-frequency, low-level
robot execution, enabling plug-and-play integration of user interfaces,
robot embodiments, and policy backends. As a result, the user experience becomes compositional: advances in user interfaces,
robot embodiments,
robot policies,
navigation, or interaction
algorithms can improve the overall experience without redesigning the system. We validate the architecture on Astribot S1
robots while designing the
robot gateway contract to support future heterogeneous
robot platforms through a shared capability interface for observation, task execution,
navigation,
speech playback, status monitoring, and task cancellation. We present representative use cases in which
agent memory and scene understanding are grounded in
robot actions. These span interactive household scenarios, ranging from simple organization to challenging long-horizon and dexterous service tasks, such as packing a backpack and lifting a garbage bag. We highlight the human-
robot interaction flow, where contextual understanding of user intent and preferences, together with human-in-the-loop confirmation or adjustment during execution, is essential for effective assistance.
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All authors Weiqi Jin, Peijun Tang, Kuncheng Luo, Baifu Huang, Binyan Sun, Haotian Yang, Shangjin Xie, Jianan Wang
Artificial Intelligence · Cross-listed from cs.LG
BackgroundMellow: A Multi-Modal Cohesive Framework for Narrative-Driven Rich Cinematic Soundscape Generation
Ajitesh Jamulkar and Aritra Hazra
Generating immersive, synchronized and cinematic audio for long-form textual narratives remains a significant challenge in multi-modal AI. While current Text-to-Audio (TTA) frameworks successfully synthesize isolated sound effects, they struggle with narrative cohesion, temporal
alignment, and cinematic emotional depth. We present BackgroundMellow, a framework that treats story-to-audio generation as a precise orchestration and signal processing problem. This framework is enabled without ground-truth through a master-specialist
agent architecture that decomposes text into precise and multi-layered audio cues, generates each category of sounds with suitable specialist model, and superimposes the soundscapes to create a unified and aligned audio segment. Our pipeline is built over Tango2 latent
diffusion model for environmental synthesis alongside a novel Cinematic BGM Retriever mined from professional soundtracks. To automate the sound mixing process, we use an NLP based module that predicts precise audio parameters, like start time, duration, and relative loudness, based on the narrative timeline. We further empirically evaluate and show the efficacy of the proposed framework leveraging nearest-neighbor
retrieval against a curated
dataset of YouTube cinematic trailers to measure temporal synchronization, coverage, and spectral richness.
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Artificial Intelligence · Cross-listed from cs.CL
Beyond Sally-Anne: Evaluating Theory of Mind in LLMs using Epistemic Schelling Points
Roberta Rocca, Sami Boukortt, Geoff Keeling et al. (+1 more)
Text-based evaluations of Theory of Mind (ToM) in Large
Language Models (LLMs) often involve cognitive tests akin to the Sally-Anne task that can be gamed due to exposure to relevantly similar tasks in pre-training and do not obviously test models' functional ToM abilities in ways that generalize to naturalistic settings. To address these issues, we introduce the Epistemic Asymmetry Schelling Task (EAST), a two-player dialogue game designed to
benchmark robust and generalizable ToM abilities. By requiring LLM-LLM dyads to independently converge on semantic Schelling points under varying states of epistemic transparency, we evaluate whether models can robustly apply ToM to achieve coordination. Our results reveal a significant capability gap in functional social
reasoning, with only frontier models successfully navigating the varying epistemic demands of the tasks. Analysis of
reasoning traces shows that coordination failures are primarily driven by epistemic tracking errors, such as conflating private knowledge with mutual knowledge. Despite high performance on traditional static
benchmarks, our study shows that robust social
reasoning and epistemic tracking remain a critical bottleneck, providing concrete targets for future LLM evaluation and development.
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All authors Roberta Rocca, Sami Boukortt, Geoff Keeling, Winnie Street
Artificial Intelligence
OpsMem: Dual-Memory Reasoning with Cross-Memory Resonance for Failure Diagnosis
Yongqian Sun, Rongchen Gao, Yu Luo et al. (+6 more)
Failure diagnosis in modern software systems requires iterative evidence acquisition and hypothesis
reasoning guided by operational experience. Existing LLM-based methods improve diagnosis through agentic
reasoning or knowledge augmentation, but they often lack a mechanism to coordinate the evolving diagnostic state with operational experience during iterative diagnosis. We propose OpsMem, a dual-
memory framework that maintains a short-term
memory for the current diagnostic state and a long-term
memory for reusable operational experience. OpsMem uses cross-
memory resonance to activate state-relevant long-term
memory, conditions multi-
agent diagnosis on the short-term and activated long-term
memories, and consolidates reusable experience from solved incidents back into long-term
memory. Experiments on a real-world Huawei microservice failure diagnosis
dataset show that OpsMem outperforms representative agentic-
reasoning and knowledge-augmented baselines, improving Match and Relevant by up to 46.88% and 18.39% over the strongest baseline, respectively.
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All authors Yongqian Sun, Rongchen Gao, Yu Luo, Wenwei Gu, Shenglin Zhang, Qingyi Guo, Qiuai Fu, Yaoliang Wu, Dan Pei
Artificial Intelligence · Cross-listed from cs.CL
Characterising AI Models for Cataloguing
Miguel Arana-Catania and Neil Jefferies
The creation of digital collections involves not only the digitisation of content, but also the creation of catalogue records for it. This often-overlooked task requires slow and costly expert manual work. In this project, we have evaluated the application of AI models to this task, comparing different implementations and models. This work includes a qualitative and quantitative evaluation of the experiments carried out, as well as recommendations on the use of AI models that go beyond the specific use case.
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Artificial Intelligence · Cross-listed from cs.SE
Understanding the Impact of AI Code Assistants on Security API Usage: An Empirical Study
Zahra Mousavi, Chadni Islam, M. Ali Babar et al. (+2 more)
AI code assistants are transforming software development, but their implications for software security remain a major concern, particularly in the context of security APIs. These APIs are critical for safeguarding software systems, yet their complexity often leads to incorrect use and serious vulnerabilities. Developing an evidence-based understanding of how AI assistants influence developers' use of these APIs is therefore essential for informing effective mitigation strategies. While a few user studies have examined the broader impact of AI assistants on software vulnerabilities, the use of security APIs remains unexplored from a developer-centered perspective. This study addresses this gap by presenting the first empirical investigation into how AI code assistants affect professional developers' use of security APIs. We conducted a study with 44 developers who completed security API programming tasks with and without GitHub Copilot assistance. Our findings show that, while Copilot improves functional correctness and marginally reduces certain insecure patterns, it does not significantly improve secure API usage. We also found that developers rarely raised security concerns when engaging with Copilot, and many did not recognize that their final implementations remained insecure. Finally, we offer recommendations for enhancing security awareness among developers and propose future research directions to support safer AI-assisted software development.
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All authors Zahra Mousavi, Chadni Islam, M. Ali Babar, Alsharif Abuadbba, Kristen Moore
Artificial Intelligence
Do Neural Networks Preserve Case Structure? Case-Based Decomposition, Interpretation, and Decision Consistency
Manli Yan, Yaowen Yu, Yong Zhao et al. (+2 more)
Neural networks increasingly inform consequential decisions, making their reliability increasingly important. Yet their internal mechanisms provide little evidence of whether decisions remain grounded in the training cases and which cases ultimately support or oppose their outcomes. Without this connection between decisions and training cases, users cannot determine whether a model has learned reliable decision patterns from data. This motivates a fundamental question: do neural networks preserve case structure? We establish a connection between neural networks and Case-Based Decision Theory (CBDT), showing that trained neural networks can preserve a recoverable case structure through their learned representations. Such a structure allows fitted decision margins to be decomposed into individual case contributions. We identify the conditions under which this recovered case structure admits a CBDT interpretation. We further establish decision consistency between this interpretation and the decision selected by the original neural network. Experiments on a controlled CBDT setting and three decision tasks based on real-world data validate our approach. These results connect neural network decisions with the cases that shape them. This connection allows model choices to be traced back to supporting and opposing cases, providing a basis for assessing the reliability of neural network decisions.
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All authors Manli Yan, Yaowen Yu, Yong Zhao, Yuebin Lin, Shoudong Han
Artificial Intelligence
Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime for Procedural LLM Agents
Chenglin Yu, Li Yin, Qingxin Fan et al. (+3 more)
Enterprise
agents must follow long-horizon, conditional, safety-critical standard operating procedures (SOPs). We compile machine-readable SOP constraints into executable pseudo-code and run them with a program-guided (PG) stack machine that pages the active frame while an LLM performs semantic execution. A three-arm SOPBench study across six models separates representation from runtime: compiled text never significantly hurts and gains up to 16.0 points where official prose underperforms. Runtime guidance is capability-gated. Two strong models independently show positive seven-domain PG contrasts (58:19 and 75:31 discordant pairs), whereas weak models are harmed. A full-program cursor ablation (active frame first, complete program retained) recovers much of the strong-model refusal gain; selective visibility adds a smaller improvement. Paired probe and audit measurements track this divide to spontaneous state discipline rather than reconstruction ability. On Bank the three primary arms rise from 70.4 to 86.4 to 92.8, with 100% refusal correctness. Practical guidance: compile first; enable active-frame paging only after a model-level discipline check.
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All authors Chenglin Yu, Li Yin, Qingxin Fan, Ying Yu, RunyangRay Zhong, Ming Li
Artificial Intelligence · Cross-listed from cs.CV
Longitudinal Multi-View Breast Cancer Risk Prediction
Solveig Thrun, Zijun Sun, Suaiba A. Salahuddin et al. (+5 more)
Accurate breast cancer risk prediction from screening mammography is critical for enabling personalized screening intervals and early detection. Recent deep learning methods have shown the value of longitudinal data and explicit temporal
alignment. However, existing approaches either perform explicit
alignment using a single mammographic view or model multiple views without explicit longitudinal
alignment, limiting their ability to exploit the complementary spatial-temporal information used in clinical practice. To address this gap, we propose LMV-Net, a longitudinal multi-view breast cancer risk prediction model that jointly analyzes anatomically complementary CC and MLO views within an explicitly aligned longitudinal framework. We evaluate our approach on the public EMBED and CSAW-CC
datasets, comparing it to state-of-the-art breast cancer risk prediction methods. Our model consistently outperforms existing approaches in overall risk prediction performance and across different breast density and cancer subgroups. Importantly, these improvements highlight the potential of longitudinal multi-view modeling to enhance risk stratification, paving the way for future work on personalized screening, earlier identification of high-risk patients, and more efficient screening resource allocation. The code is available at https://github.com/sot176/LMV-Net.
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All authors Solveig Thrun, Zijun Sun, Suaiba A. Salahuddin, Kristoffer Wickstrøm, Elisabeth Wetzer, Stine Hansen, Robert Jenssen, Michael Kampffmeyer
Artificial Intelligence · Cross-listed from cs.SE
Fail-Aware and Explainable Test Oracle Prediction
Yue Zhao, Binish Tanveer and Jelena Zdravkovic
Despite their central role in fault detection, test oracles remain challenging to construct effectively. Recent learning based methods address this challenge by automatically generating test assertions, yet even if syntactically correct, they are often ineffective in revealing bugs. Rather than generating assertions, this study explores a different approach by training a model to directly predict whether a given test prefix passes or fails. We present FOCAL, an emerging code LLM-based discriminative oracle predictor. It learns from labeled pairs of test prefixes and methods under test, employs losses that emphasize failing cases during training, and grounds its predictions in statement level behavioral evidence. Compared with the baseline method SEER, we substantially improve performance on failing cases for unseen projects and provide richer explanations. A preliminary evaluation on fault-detection
benchmarks and automated test-generation artifacts shows that our approach is highly accurate within its training distribution and substantially improves failure detection on previously unseen projects where prior discriminative oracles collapse. Moreover, the highlighted statements are supported by behavioral explanation checks. These early results suggest that fail-aware discriminative oracle prediction can complement existing approaches such as fuzzing, search-based testing, and LLM-based test generation. These techniques produce test prefixes at scale but often lack fault oriented oracles. In future work, FOCAL could take generated test prefixes and attach fault-aware predicted oracles to them, turning high-volume input generation into executable tests that are more likely to expose semantic failures.
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Artificial Intelligence
AutoVSR: Automatic Visual-to-Symbolic Reasoning for Symbolic Expression Generation from Circuit Schematic
Zhe Xiao, Longfei Li, Xu He et al. (+3 more)
Symbolic expressions can effectively characterize and predict
circuit behavior, but deriving them directly from
circuit schematics is challenging. This process requires accurate visual-to-symbolic construction of
circuit structure from images and correct multi-step symbolic derivation, both of which impose strict correctness requirements. This work proposes AutoVSR, an automated framework for visual-to-symbolic generation of
circuit expressions using
Vision Language Models (VLMs). By reconstructing
circuit diagrams into an executable intermediate representation (Executable IR) and leveraging a symbolic solver for
reasoning, AutoVSR significantly improves the accuracy of symbolic expression generation. AutoVSR introduces two key innovations: an IR construction method guided by component rule
retrieval and verification-based feedback, and a symbolic solver implemented as a planning
agent equipped with a symbolic tool library for reliable multi-step derivation. Compared with end-to-end VLM approaches and specialized methods on the main symbolic expression generation task, AutoVSR achieves accuracy improvements of 30.01–59.45% and 41.96–51.84%, respectively. Moreover, AutoVSR surpasses closed-source state-of-the-art VLMs in inference cost and computational efficiency. Code is available at https://github.com/LongfeiLi1/AutoVSR.
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All authors Zhe Xiao, Longfei Li, Xu He, Haoying Wu, Zixing Zhang, Mingyu Liu
Artificial Intelligence
Verifier-Guided Twelve-Tone Composition: A Generate-Verify-Repair Harness for Symbolic Music Generation
Congren Dai, Danni Zhao, Enyang Liu et al. (+7 more)
Large
language models can produce superficially legal twelve-tone scores that collapse into degenerate textures. We introduce a neuro-symbolic harness that wraps a
language-model proposer in a generate-verify-repair-trace loop with symbolic verification. The complete pipeline improves event-local consistency without claiming whole-piece legality. Across 40 controlled tasks and four paired models, constraint-checked delivery rises from 13.3% under raw generation to 48.1% with the harness; it abstains on the remaining 51.9% of runs. The pass rate of a narrower collision and serialisation-consistency check rises from 33.5% to 58.3%, while degeneracy remains near 0.05, including under
adversarial prompting. A blinded evaluation by five experts also shows a descriptive aggregate preference for harness candidates over raw generation in adherence, perceived legality, coherence, and overall quality.
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All authors Congren Dai, Danni Zhao, Enyang Liu, Michael Ching Yam, Zhancheng Guo, Siyi Gu, Wentao Yang, Bo Dai, Xiaobing Li, Maosong Sun
Artificial Intelligence · Cross-listed from cs.LG
PRISM Edit: One Vector for All Temporal Answers
Chen Huang, Qi Zheng, Ruiqin Zheng et al. (+2 more)
Model editing keeps large
language models (LLMs) up to date without retraining, but temporal facts expose a limitation of the prevailing locate-and-edit paradigm: an update is not always a replacement. When a fact changes, the new answer should become current while the old answer may remain correct in historical time contexts. Building on this insight, we use causal tracing to show that LLMs already support this distinction via a two-stage internal computation: early MLP layers retrieve a time-agnostic subject representation, and later layers modulate it with temporal context to yield the time-correct answer. Motivated by this finding, we introduce PRISM Edit, which optimizes a single polysemous representation across temporal contexts and leverages the model's inherent modulation pathway to route it to temporally correct predictions without requiring any architectural modification. We evaluate on TimeConflict, a newly introduced temporal editing
benchmark, and on temporally augmented CounterFact. PRISM Edit improves multiple core metrics over the best baseline, most notably +23.3 Temporal Consistency (TC) and +33.7 Current Relative-time Score (CRS) on LLaMA-3, while being more than 2x faster. Code and data are publicly available at https://github.com/CheerCHuang/PRISM-Edit.
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All authors Chen Huang, Qi Zheng, Ruiqin Zheng, Long Zeng, Yuantong Xu
Artificial Intelligence
Calibrated e-CUSUM Decoding for Quantized Reasoning Models: Why Token Log-Probability Is the Wrong Observable for Decoding Monitors
El Hassane Ettifouri, Ayoub Belfatmi, Mahaman Sanoussi Yahaya Alassan et al. (+1 more)
Low-bit quantization makes small
reasoning models inexpensive to deploy but can degrade their chains of thought. This motivates decoder-side monitors that intervene when generation becomes unreliable. We show that a natural candidate, the centered
token log-probability increment $\log p(w_t)+H_t$, is the wrong observable for this purpose. Under the model's own sampling law it is a mean-zero martingale by construction, so it measures sampling self-consistency rather than trajectory health and is nearly silent during confident repetition, where both $\log p(w_t)$ and
entropy are close to zero. We introduce a training-free decoding controller that combines (i) a degeneration-aware alarm score fusing
token uncertainty with explicit verbatim repetition and (ii) a calibrated e-process-inspired sequential detector. The raw product process is Ville-valid under a conditional-mean null, while the deployed CUSUM-floored statistic is treated as an empirical change detector because the score is history-dependent and autocorrelated. On GSM8K with DeepSeek-R1-Distill-Qwen-1.5B in FP16 and INT4, calibration turns a monitor that fires on 93–95% of generations into a selective detector of failing traces ($\phi \approx 0.3$, precision $\approx 0.6$ against a 0.38 base rate). In this pilot, the controller reduces measured verbatim-degeneration signals and yields a positive but statistically inconclusive INT4 accuracy change from 63% to 69% (paired McNemar $p=0.18$, $n=100$), at a 28%
token-budget cost. We also find that non-termination, rather than looping, is the dominant failure mode on GSM8K. The main contribution is methodological: an explanation of why centered
token log-probability is inadequate for decoder monitoring and a calibrated, cautiously evaluated replacement.
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All authors El Hassane Ettifouri, Ayoub Belfatmi, Mahaman Sanoussi Yahaya Alassan, Walid Dahhane
Artificial Intelligence · Cross-listed from cs.CY
Programming Language Policy as an AI Literacy Equity Problem: A 15-Nation Comparative Analysis
Adrian-Marius Dumitran and Iulia-Maria Popescu
The promise of AI literacy “for all” confronts a structural challenge embedded in how nations organise secondary computer science education. In most systems, a general-track subject – Digital Literacy, ICT, TIC, or SNT – bears the weight of universal AI literacy, while a specialist Informatics course serves STEM pathways separately. Yet the content and depth of the general track are shaped by governance decisions made largely with reference to the specialist one. This paper presents a comparative analysis of curricula and examination frameworks across fifteen countries, identifying two structural challenges. First, in several systems a significant portion of students completes secondary education without any formal programming exposure. Second, among those who do receive CS education, a Syntax Ceiling emerges: Python-based instruction reaches most students, while the algorithmic depth associated with C++ remains concentrated in elite STEM tracks. Drawing on reform cases spanning centralised mandates (France, China, Japan), assessment-driven systems (Poland, Romania, South Korea), and recent universal reforms (Switzerland, Kazakhstan), we show that governance structures and high-stakes examinations are the primary drivers of both challenges – and that specialist and general-track
language choices are rarely independent, linked through shared teacher pipelines that curriculum policy seldom acknowledges. Achieving genuine AI literacy for all requires confronting not just curriculum content, but the access architectures and resource constraints that determine who receives it – and at what depth.
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Artificial Intelligence
Efficient Test-Time Optimization for Multi-Agent Proof Autoformalization
Tian-Shuo Liu, Shiyuan Zhang, Zijie Geng et al. (+5 more)
Full-proof autoformalization bridges extensive mathematical proofs in natural
language with formally validated
reasoning, offering a pathway to elevate the ceiling of verifiable mathematical
reasoning. Unlike statement-level formalization, proof autoformalization is a long-horizon challenge requiring coordination of claims, contexts, and dependencies across many proof steps, yet has only recently come under focused study. Current approaches either rely on costly model training or apply excessive, unguided repair at inference time. To this end, we introduce ToMap, a multi-
agent framework that structures proof autoformalization as a Decomposer-Formalizer-Prover pipeline with efficient test-time optimization guided by formal verification and semantic rubrics for proof quality. Rather than distributing test-time compute across all
agents, we perform bottleneck analysis and identify the Decomposer as the critical bottleneck: the quality of its atomic, self-contained proof units directly determines whether downstream
agents can successfully formalize and prove each step. ToMap therefore treats the Formalizer and Prover as downstream executors and efficiently focuses test-time compute on Decomposer refinement. This refinement follows a loop inspired by GEPA, evolving
prompts over candidate decompositions and using formal verification progress together with semantic proof rubrics to define a Pareto frontier that guides the next decomposition update. Experiments on ProofFlowBench show that ToMap improves over the best previous method by 19.0% when evaluated by both syntactic correctness and semantic faithfulness, while requiring lower test-time cost. Scaling analysis shows that most gains emerge within a few iterations of decomposition
evolution, guiding test-time budget selection.
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All authors Tian-Shuo Liu, Shiyuan Zhang, Zijie Geng, Haoyu Liu, Runjie Xu, Pengyuan Wang, Lei Yuan, Yang Yu