Artificial Intelligence · Cross-listed from cs.LG
NanoProof: Open and Efficient Automated Theorem Proving in Lean 4
Matěj Kripner and Milan Straka
We introduce NanoProof, to our knowledge the first factorized execution-guided
theorem prover in Lean 4 whose training data, extraction tooling, training pipeline, and weights are all released, making it end-to-end reproducible using open-source resources. To this end, we build and release a
dataset of structured proof trees, as well as a tool for programmatic interaction and data extraction within the Lean 4 formal verifier. To support sustainable research, we focus on compute efficiency to facilitate accessible training and evaluation. NanoProof achieves 50.8% pass@16 on MiniF2F-Test, exceeding the two closest systems of its class, HyperTree Proof Search and ABEL, at roughly 90x and 7x less compute, and using more than four orders of magnitude less compute than AlphaProof. Stronger open-weight provers exist, but they are fine-tuned from large pretrained
language models and release neither training data nor pipeline; NanoProof shows that the factorized execution-guided class of provers can be rebuilt from scratch with modest resources.
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Artificial Intelligence
Error-Propagation Modeling for Failure Attribution in LLM-Based Multi-Agent Systems
Jiaqi Liao, Yuanzhao Zhai, Huanxi Liu et al. (+5 more)
LLM-based multi-
agent systems (MASs) are increasingly used to solve complex tasks through coordinated
reasoning, tool use, and interaction with external resources. However, attributing failures in such systems remains challenging because the observed outcome often does not directly reveal the error responsible for the failed execution. In this work, the attribution target is the decisive error, defined as the
agent–step pair whose correction would recover the failed execution. Existing approaches largely identify suspicious steps without explicitly modeling how errors propagate across interactions or persist in unresolved loops, making decisive errors difficult to distinguish from downstream failure symptoms. We propose Error-Propagation Modeling for Failure Attribution (EMFA). EMFA constructs a structured representation of the failed trajectory, models both cascading propagation and persistent interaction loops, and uses propagation-aware candidate screening followed by counterfactual verification to identify the decisive
agent–step pair. On the Who&When
benchmark, EMFA achieves state-of-the-art step-level attribution accuracy and remains competitive at the
agent level. It improves the previous best step-level results by 3.45 and 4.40 percentage points on the Hand-Crafted and
Algorithm-Generated subsets, respectively.
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All authors Jiaqi Liao, Yuanzhao Zhai, Huanxi Liu, Xu Zhang, Zheming Zhuang, Dawei Feng, Bo Ding, Huaimin Wang
Artificial Intelligence · Cross-listed from cs.CL
Large Language Model Turnover Undermines Screening for Artificial Intelligence-Assisted Scientific Writing
Kazuki Nakajima and Takayuki Mizuno
Journals and conferences have begun to screen submitted manuscripts for text written using large
language models (LLMs). The reliability of this screening rests on
benchmark evaluations against a fixed set of LLM versions, while the versions in actual use keep changing. Here we quantify how this LLM turnover affects the screening of scientific manuscripts. We paired 4,000 pre-ChatGPT abstracts from the Proceedings of the National Academy of Sciences with their rewrites by 23 LLM versions from three vendors, released between June 2023 and August 2026. We then trained detectors under maintenance scenarios ranging from a detector retrained on every new version to one trained once and never updated. Detectors trained only on a vendor's past versions can collapse at the boundaries between model generations: calibrated to falsely flag 1% of human-written abstracts, they catch above 99% of rewrites just before the sharpest boundary and 3.8% just after it. Detectors trained on later versions can also miss rewrites of earlier ones. Vocabulary differences between versions largely track where detection transfers and where it fails. In the two screening scenarios we simulated, screens covering all 23 versions either flagged one in eight human-written abstracts or missed one in three rewrites of the newest version. Indeed, a commercial detector missed most rewrites of the version just after the sharpest boundary while flagging almost no human-written abstracts. Research-integrity policy should therefore treat the
benchmark accuracy of a detector as provisional, to be re-verified with every LLM release, including earlier versions.
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Artificial Intelligence · Cross-listed from cs.SE
Runnable Commit Untangling for Coding Agents
Jinfeng Jiang, Dongsun Kim, Dayi Lin et al. (+1 more)
Coding
agents produce large, tangled patches that mix multiple development purposes, making the code hard to review and maintain. Commit untangling offers the promise of organizing such large patches into untangled, manageable commits. This paper emphasizes two important limitations in existing commit untangling studies. First, they do not consider that untangled commits are ordered and should leave the code runnable. In practice, maintainers are unlikely to accept commits that prevent the code from running. Second, existing studies claim that commit untangling helps software maintenance. However, they conduct syntactic comparisons between the untangled commits and developers' original commits without directly showing the claimed maintenance benefits. To address these gaps, this paper makes two novel contributions: (1) RucTangle, the first agentic method that untangles commits while keeping the code runnable after each commit; and (2) TangleEval, the first evaluation framework that quantifies how untangled, manageable commit histories help coding
agents repair bugs. We compare RucTangle against four untangling methods on 131
agent-generated patches. All histories produced by RucTangle are runnable, while baselines produce 20.6%-37.4% unrunnable commit histories. We further collect 453
agent-generated patches that introduce regressions (i.e., causing previously passing tests to fail) and ask two other coding
agents to repair regressions. Augmenting
agent context with RucTangle-produced histories yields 5.2% absolute improvement in pass@1. We also analyze
agent trajectories to learn how they use untangled commits to navigate and fix bugs. Our findings demonstrate the value of adopting established software engineering practices in the era of coding
agents, which broaden the future research agenda: how can
agents actively use software history to make better development decisions?
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All authors Jinfeng Jiang, Dongsun Kim, Dayi Lin, Zhou Yang
Artificial Intelligence · Cross-listed from cs.CL
Adapting English Quality Classifiers for Multilingual LLM Pretraining Data Selection
Vinko Sabolčec, Bettina Messmer, Yassine Turki et al. (+1 more)
Recent advances in large
language model (LLM) pretraining highlight the role of high-quality training data in improving performance. While model-based filtering has proven effective in selecting high-quality subsets from web-scale corpora, especially for high-resource
languages, low-resource
languages face challenges due to limited availability of annotated data. This work explores extending quality filtering to over 100
languages by proposing a multilingual adaptation approach that converts an existing English quality classifier into a multilingual variant. Our approach proposes training a small multi-layer perceptron on top of
Transformer encoder-only model
embeddings, using multilingual text as input and scores obtained from English classifiers applied to machine-translated text as labels. Our 1B, 3B and 8B scale experiments show that our approach maintains the downstream LLM
benchmark performance of existing multilingual model-based filtering baselines, without harming regional and cultural knowledge
benchmarks. To further evaluate cross-lingual generalization, we compare classifier scores of high-quality synthetic data and web samples, and the correlation of classifier scores with LLM-based ones, revealing that the classifier can learn the scoring criteria of its original English variant, even for
languages not included in its training data.
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All authors Vinko Sabolčec, Bettina Messmer, Yassine Turki, Martin Jaggi
Artificial Intelligence · Cross-listed from cs.RO
SDPAD: A Fully Spike-Driven Pipeline for End-to-End Autonomous Driving
Chengjun Zhang, Yuhao Zhang, Jie Yang et al. (+1 more)
End-to-end autonomous driving demands trajectory planners that are both highly accurate and cheap enough for edge deployment. State-of-the-art artificial neural network (ANN) planners meet the accuracy requirement at the cost of heavy dense computation, while spiking neural networks (SNNs)—though promising orders-of-magnitude energy savings through sparse, event-driven arithmetic—still lag far behind in planning accuracy. We present SDPAD, a fully spike-driven end-to-end planning pipeline that closes this gap. SDPAD converts a pre-trained ANN perception stack into integer-spike form via quantized ANN2SNN conversion, lifts multi-view images into the bird's-eye-view (BEV) space with a spike-driven-max (SDM) depth distribution (Spike-3D-Lift), and plans through the Spike-QFormer, a spiking query
transformer in which ego,
agent, and map queries distilled from the BEV scene are fused by learnable waypoint queries via cross-
attention, followed by deformable spike-cross-
attention refinement. Every operation is gated by integer spikes and inference is a single feed-forward pass without temporal simulation loops. On the nuScenes open-loop
benchmark, SDPAD achieves an average $L_2$ error of 0.40\,m and a collision rate of 0.12%, on par with strong ANN planners while consuming 69.9\,mJ—less than 2% of recent ANN baselines. In closed-loop evaluation on the NAVSIM navtest split, SDPAD reaches 86.3 PDMS, surpassing the previous SNN planner SAD by 4.3 points and matching mainstream ANN planners at a fraction of their energy. To our knowledge, SDPAD is the first fully spike-driven planner evaluated in end-to-end autonomous driving, demonstrating that SNNs can rival dense ANNs in complex driving tasks.
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All authors Chengjun Zhang, Yuhao Zhang, Jie Yang, Mohamad Sawan
Artificial Intelligence
Memory Type Varies: Empowering LLM Agents for Long-Term Memory with Diverse Strategies
Yi Wen, Derong Xu, Pengyue Jia et al. (+8 more)
The
memory capabilities of Large
Language Models (LLMs) have garnered increasing
attention recently. Despite great success achieved, existing
retrieval-based
memory approaches typically overlook the differences between
memories and employ a unified strategy to process all
memories, leading to suboptimal performance. Thus, an intuitive question arises: can we categorize
memory into different types and select appropriate strategies? However, given the topic-rich, scenario-complex, and boundary-blurred nature of
memory scenarios, achieving precise classification of
memories is not easy. To address this challenge, we propose a
memory multi-class
dataset in this paper, termed TriMEM, which provides precise annotations for
memory types across diverse scenarios. Building upon this foundation, we propose a novel
memory framework, named MemoType, which can adaptively recognize each
memory and query type with the learned router model. With the
memory and query routing, MemoType can retrieve the
memory with corresponding query types and design tailored
retrieval strategies, thereby enhancing the
retrieval performance. Moreover, we theoretically prove that any single
retrieval strategy is subject to a fundamental upper bound on its expected
retrieval precision in multi-class corpora, leading to systematic precision degradation. Extensive experiments on three
datasets demonstrate that MemoType consistently outperforms existing methods, achieving up to 16.18% improvement in Recall@1.
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All authors Yi Wen, Derong Xu, Pengyue Jia, Yichao Wang, Yingyi Zhang, Maolin Wang, Junyi Li, Wenlin Zhang, Xiaopeng Li, Yong Liu, Xiangyu Zhao
Artificial Intelligence
Scaling to Tens of Thousands of Test-Time Iterations with Loop-Native Attention Residuals
Pengxiang Li, Dilxat Muhtar, Di He et al. (+3 more)
In this paper, we argue that looped
Transformers need their own residual connections to prevent performance degradation as the number of iterations grows. We observe that increasing loop iterations can reduce
reasoning accuracy: noisy state updates overwrite correct intermediate deductions and even undo completed solutions. This leaves subsequent iterations to recover lost information from an already degraded representation: once an error arises in an earlier loop, often as a result of long-range propagation through the recurrence, later loops find it difficult to correct. In this paper, we introduce InfiLoop, a loop-native residual connection that learns which past computations to retain and how much to accept from each new update. InfiLoop combines content-based weighting with learned temporal decay to maintain a running summary of recurrent states. An exact streaming recurrence keeps its persistent aggregation
memory constant as the loop count grows. The resulting adaptive update suppresses unreliable proposals and preserves useful intermediate states. Across extensive
reasoning tasks, a 7M-parameter InfiLoop model outperforms existing recursive architectures, reaching 97.9% exact accuracy on Sudoku-Extreme, and 13.6% pass@2 on ARC-AGI-2. Notably, on Sudoku-Extreme, InfiLoop continues to improve with test-time looping beyond 20,000 effective steps, showing that added depth translates directly into stronger
reasoning. Our code is available at https://github.com/pixeli99/InfiLoop.
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All authors Pengxiang Li, Dilxat Muhtar, Di He, Guinan Su, Lu Yin, Shiwei Liu
Artificial Intelligence · Cross-listed from cs.LG
Sera: Semantic Representation Aggregation with Knowledge and LLM for Reliable and Interpretable Battery Health Forecasting
Jiawei Li, Fang Liu, Wei Zhang et al. (+3 more)
Battery state of health (SoH) forecasting is important for battery management, but remains challenging due to nonlinear degradation and heterogeneity across batteries. Existing data-driven approaches primarily use temporal models to learn from numerical battery time series, and higher-level degradation characteristics are often not explicitly represented. These characteristics, however, can provide degradation guidance to support reliable forecasting and make the influence of degradation more interpretable. In this paper, we propose Sera, a semantic representation aggregation framework that complements temporal modelling with degradation semantics. Guided by battery domain expertise, Sera extracts degradation semantics from time series and constructs two complementary representations using rule-based knowledge and LLM-based interpretation. The representations are independently encoded and integrated with the representation learned by temporal models through gated aggregations. Experiments on the mainstream
benchmark across multiple prediction horizons and different temporal models show that Sera consistently improves forecasting performance, achieving up to a 37.3% reduction in prediction error over the temporal baseline and enhanced generalizability. Counterfactual analysis examines how forecasts respond to changes in degradation semantics to assess interpretability. The results show that prediction responses are consistent with the meanings of key degradation descriptors across tested horizons. Together, these findings demonstrate that structured degradation semantics and effective aggregation can improve forecasting accuracy and support reliable and interpretable battery health forecasting for advanced battery management.
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All authors Jiawei Li, Fang Liu, Wei Zhang, Zuming Liu, Man-Fai Ng, Zhi Wei Seh
Artificial Intelligence
Workerville: Towards an Organizational Behavior Account of Agent Safety
Hanjun Luo, Junting Mao, Yuhan Lu et al. (+5 more)
LLM-based
agents now interact with their environments continuously, shaped by such organizational channels as user instructions, peer messages, and long-term
memory. Existing safety research has examined these influences, but largely as separate
agent components. How such factors jointly shape an
agent's safety behavior from a unified perspective remains unmeasured. To bridge this gap, we advocate organizational behavior (OB) as a framework for studying the safety of advanced
agents, reorganizing the objects of study, theoretical foundations, and experimental design around the relational structure in which
agents operate. We present the first systematic formalization of counterproductive work behavior (CWB), a canonical safety-relevant subfield of OB, as Agentic Counterproductive Behavior (ACB). ACB specifies three organizational antecedents (vertical supervisor relations, horizontal peer norms, and internal cognitive structures) and maps them onto three counterproductive outcome dimensions (unauthorized disclosure, destructive operations, and production deviation). To operationalize ACB, we introduce Workerville, a controlled
benchmark that manipulates organizational conditions over shared tasks, applying 16 organizational configurations to 210 tasks to yield 3,360 challenges, evaluated by human-validated agentic judges. Benchmarking 6 frontier LLMs, we find that (I) negative organizational antecedents exhibit non-monotonic amplification when combined, with the unauthorized-disclosure rate rising from 16.5% under no negative antecedent to 60.1% under two and falling back to 50.3% under three; (II)
agents reproduce typical behavioral patterns predicted by human CWB research; (III) these results establish OB as a systematic framework for
agent safety research, pointing toward a new research agenda.
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All authors Hanjun Luo, Junting Mao, Yuhan Lu, Haobo Zhang, Zhimu Huang, Yankai Chen, Hanan Salam, Xue Liu
Artificial Intelligence · Cross-listed from cs.CL
SWE-Journey: Towards More Realistic Evaluation of Coding Assistants through Long-Horizon, Multi-Turn Interaction
Hexuan Deng, Yue Wang, Wenyu Jiang et al. (+13 more)
Coding assistants such as Claude Code and Codex have become a major application of LLM
agents, yet existing
benchmarks remain far from real-world use, particularly in task horizon and interaction length. Code assistants require completing long chains of development work in continuously evolving repositories, while repeatedly clarifying requirements and adapting implementations through multi-turn interaction. To address these gaps, we introduce SWE-Journey, a
benchmark for more realistic evaluation of coding assistants. To address the task-horizon gap, we propose a weak-to-strong synthesis pipeline that automatically constructs long-horizon coding tasks. To address the interaction gap, we mine four representative user personas from real interaction data and build a user-simulation
agent to reproduce realistic code-assistance interactions. On average, models pass over 75% of tests for requested functionality with software architects, but fewer than 25% with non-coders. These results show that current coding assistants still fall short of enabling reliable coding for non-coders. We further analyze the reasons for this gap and identify asking right, finding right, and fixing right as key capabilities during interaction.
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All authors Hexuan Deng, Yue Wang, Wenyu Jiang, Cheng Yang, Haolin Yang, Zhaohua Zhang, Chenchen Zhao, Beiduo Chen, Muxi Chen, Sa Zhu, Geyuan Zhu, Jianhuan Zhuo, Qiuyong Xiao, Tianwen Jiang, Jihong Zhang, Xuebo Liu
Artificial Intelligence
Safe, Persistent, and Evolving Agent Harness for Understanding Partially Observable Worlds
Yisen Gao, Yue Guo, Qing Zong et al. (+2 more)
Large
language model
agents can invoke tools fluently, but enterprise workflows demand more than selecting the right tools: actions must strictly comply with organizational policies, tool feedback often conceals hidden side effects under partial observability, and long-horizon tasks require persistent state tracking across multiple records. To address these challenges, we introduce E-Ledger, a multi-
agent harness for safe and persistent execution. E-Ledger employs a code approval layer that checks every proposed action against policy before execution, and maintains a world ledger of verified hidden rules alongside evidence-backed dynamic state. Because hidden rules are typically unknown a priori, we further propose WorldAbduct, an abductive, world-model-driven harness
evolution framework. WorldAbduct diagnoses execution trajectories across four complementary views (state consistency, world-observation gap, policy-gate correctness, and goal judgment) to hypothesize latent rules, and verifies them through targeted abductive interactions before integrating them into the ledger. On the enterprise
benchmark World of Workflows, E-Ledger with WorldAbduct improves safe task completion across four LLM backbones, outperforming the strongest
evolution baseline by 5–15 percentage points. Experiments in ScienceWorld and DiscoveryWorld further show that abductive harness
evolution carries over to scientific environments. Our code is available at https://github.com/HKUST-KnowComp/E-LEDGER-WorldAbduct.
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All authors Yisen Gao, Yue Guo, Qing Zong, Yiwen Guo, Yangqiu Song
Artificial Intelligence · Cross-listed from cs.NE
Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating
Lechen Li, Rongye Shi and Wanhuan Zhou
Solving high-dimensional structural model updating problems requires an
algorithm capable of navigating complex, non-convex landscapes with correlated parameters. Existing hybrid evolutionary
algorithms typically rely on static architectures or fixed switching rules, resulting in disjointed search phases. To address this, this study proposes a Deep Reinforcement Learning-governed dynamic DE-CMAES Orchestration (DRL-DCO)
algorithm, in which a Deep Deterministic Policy Gradient (DDPG)-based actor-critic
agent continuously governs the evolutionary process as a single, unified system rather than a mechanical concatenation of
algorithms. Guided by a progression-aware state representation and a diversity-informed reward, the
agent fluidly reallocates computational resources between the differencevector-based exploration of Differential
Evolution (DE) and the covariance-guided exploitation of CMA-ES, while jointly regulating population size, elite preservation, and a restart mechanism to escape local optima. This allows DRL-DCO to autonomously transition between exploration-dominant, exploitation-dominant, and mixed-strategy regimes across generations. Beyond the training phase, the trained actor can operate in a supervision-free inference mode, where the internalized policy autonomously orchestrates DE and CMA-ES control from observed search states through forward inference alone, without critic evaluation or weight updates, enabling faster deployment while retaining full effectiveness. Validated on high-dimensional single-objective optimization
benchmarks and the IASC-ASCE structural health monitoring
benchmark, DRL-DCO achieves superior convergence accuracy and robustness compared to state-of-the-art adaptive and hybrid evolutionary
algorithms, as well as single-operator DRL-governed baselines.
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Artificial Intelligence · Cross-listed from cs.CL
Constitutional Gating and Deterministic Recovery for Multi-Agent LLM Negotiation: Ablations Against a Stateful Adversarial Gatekeeper
Masaaki Nakatsu and Reno Wang
Multi-
agent LLM systems negotiating with a stateful counterpart waste model calls in three ways: polite loops that never meet the counterpart's hidden acceptance condition, malformed outputs that trigger retries, and compliance deadlocks in which the counterpart demands something the
agent must refuse. We study a three-part control stack - a 5-Pillar runtime constitution, a 4-tier swarm (Director, three-
agent majority vote, Monitor, schema hard gate) and Cognitive Annealing (deterministic deadlock detection, atomic purge of the
agent-side context, a canonical recovery message) - against a released
adversarial Gatekeeper whose acceptance rules are fixed regular expressions and whose LLM only renders reply text. The testbed has a known solution: it measures whether the stack executes a constitution-aligned strategy against swarm drift and recovers from deadlock, not whether it discovers anything. In five runs per configuration (30 runs; Gemini 2.5 Pro
agents, Claude Haiku 4.5 Gatekeeper) we find: (i) the constitution and Director make an acceptable framing possible but not reliable - 0/5 baseline unlocks versus 1/5 and 2/5 with the constitution; when the swarm unlocks it does so in one turn with 7-8 calls and about 15k
tokens (67-73% below baseline); when it does not, it costs 17-38% more; (ii) the Monitor and hard gate do not reduce unlocks and leave an audit trail; (iii) under a honeytrap-to-compliance deadlock, LLM-only steering escapes 0 of 5 times while atomic purge plus a canonical strike escapes 5 of 5 (Fisher $p = 0.008$) at the same call budget, with zero calls for the strike. LLM-written strikes failed the deterministic pre-flight 5 of 5 times although an LLM Monitor had approved four. Pre-registered hypotheses on average call and
token reduction were not supported. Cost is bounded in every arm by deterministic stop rules; the stack adds recovery at no extra model cost.
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Artificial Intelligence · Cross-listed from cs.HC
Design Creativity Bench: Measuring creativity in LLM-Generated UI
Aman Rusia, Abhijit Bhole, Prashank Gupta et al. (+1 more)
As leading LLMs improve on capability evaluations, their limitations in producing creative outputs on design tasks remain insufficiently characterised. Our work introduces Design Creativity Bench, a
benchmark that evaluates diversity and appropriateness in UI designs. It measures distinctiveness among models on the same
prompt (originality), how much a model's designs change between two
prompts for the same UI goal in different product domains (creative range), and the share of a brief's acceptance criteria each design meets (appropriateness). Originality is 0.592 for same-
prompt design pairs from different models (95% CI [0.582, 0.602]), far below the 0.764 for same-
prompt human-model pairs (95% CI [0.751, 0.778]). Creative range is 0.581 across models (95% CI [0.567, 0.597]), against 0.902 for human designs (95% CI [0.884, 0.919]). Appropriateness is above 90% for every model in our test set, and the best model reaches 99.2%, slightly above the 98.0% for human designs. Our work shows that the default output of LLMs, though generally appropriate, is substantially more repetitive than the human baseline. This calls for strong measures to address the issue. A live leaderboard with newer models is maintained at https://kombai.com/
benchmark/.
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All authors Aman Rusia, Abhijit Bhole, Prashank Gupta, Dipanjan Dey
Artificial Intelligence
ReTeach: Building a Self-Teacher through Multi-Round Reflection and Retry
Yafeng Tang, Hao Li, Hongsheng Yu et al. (+1 more)
Self-
distillation can improve
reasoning without a separately trained, more capable teacher, but its effectiveness depends on how the self-teacher gains an advantage over the student. Conditioning the teacher on reference answers or solutions can provide such an advantage, but this information may be unavailable. Reflection offers a way to derive explicit error diagnoses and revision guidance from self-generated attempts, yet existing reflection-based methods often combine it with reference information, rich task feedback, or persistent
memory. We introduce ReTeach, a Reflective self-
distillation framework that constructs its self-Teacher through multi-round reflection and retry using only self-generated attempts and outcome-level verification. Starting from an unsuccessful student rollout, the teacher alternates explicit reflection with renewed attempts until success or the retry budget is exhausted, without reference answers or solutions, external diagnostic feedback, or cross-example
memory. Each failed retry informs subsequent reflection, while successful correction provides outcome-level evidence for the potential utility of the resulting teacher context. An outcome-aware selection and weighting strategy distinguishes initially correct, reflection-corrected, and unresolved examples, assigning separate weights to their category-normalized
distillation losses. Through on-policy
distillation, the student matches the teacher's context-conditioned
token-level predictive distributions at prefixes of its own rollouts, transferring the benefits of iterative correction while retaining single-pass inference. Across six
benchmarks spanning mathematical
reasoning, science question answering, and tool use, ReTeach improves average accuracy over GRPO by 1.39 percentage points.
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All authors Yafeng Tang, Hao Li, Hongsheng Yu, Qiang Fu
Artificial Intelligence
AtomWorld-Mirror: Macro-Step World Modeling of Critical Evolution Backbones for Materials Dynamics
Ziming Pan, Ruge Zhang, Haozhi Han et al. (+7 more)
Atomistic simulation is a fundamental tool for studying long-term materials
evolution, from
diffusion and defect dynamics to interfacial reactions and fracture. Yet conventional simulators typically advance at microscopic resolution, spending substantial computation on low-impact local updates before reaching structurally consequential states, an evolutionary-resolution bottleneck that limits long-horizon simulation. We propose AtomWorld-Mirror, a time-aware macro-step world model for the critical
evolution backbone of atomic systems. For Step-Wise atomistic simulation, AtomWorld-Mirror distills short micro-event segments into physically reachable transitions between key states, jointly predicting sparse structural edits and accumulated physical time through latent macro-step dynamics. Local reachability, inventory conservation, and continuous-time consistency constrain each transition. By amortizing local atomic physics into a reusable latent macro model and replacing explicit micro-event replay with macro-step inference, this formulation provides a path toward substantially faster prediction of long-term materials
evolution while preserving structural validity and time semantics. Across five atomic systems, spanning Cu-rich RPV steel irradiation aging, Cu-Zr metallic glass, and Li$_3$N-based anti-perovskite solid electrolyte, macro-step inference delivers a speed up of $10^3$ to $10^4$ times over event-by-event simulation.
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All authors Ziming Pan, Ruge Zhang, Haozhi Han, Junkai Zhou, Xingyuan Chen, Yifeng Chen, Yunquan Zhang, Ting Cao, Yunxin Liu, Kun Li
Artificial Intelligence · Cross-listed from cs.CV
MSGAT: Multi-Head Spiking Graph Attention with Similarity-Space Fusion for Image-Text Retrieval
Xintao Zong, Wenxuan Liu, Jianhao Ding et al. (+2 more)
Spiking neural networks (SNNs) offer an energy-efficient computing paradigm through sparse event-driven computation, showing great potential for efficient
multimodal learning. However, applying SNNs to high-level
multimodal tasks, such as image-text
retrieval (ITR), remains challenging, since sparse spike representations make it difficult to capture semantic structures required for cross-modal
alignment. Existing spiking ITR methods rely on local
alignment and additional soft-label supervision during training, while lacking awareness of structural and multi-granularity relationships. To address these issues, we propose a Multi-head Spiking Graph
Attention Network (MSGAT) for structural modeling and equip it with dynamic
attention heads to capture complementary relational patterns and enable spike-driven graph
reasoning and aggregation. However, within a two-branch multi-granularity fusion framework, the fine-grained spike representations generated by MSGAT are sparse and discrete, whereas the global representations are continuous, making conventional feature-level fusion susceptible to interference across heterogeneous representations. Therefore, we introduce Sim-Fuse, a similarity-space fusion
alignment strategy integrating coarse- and fine-grained matching relations while avoiding direct fusion of heterogeneous representations. Experiments on Flickr30K and MSCOCO show our method outperforms ANN methods under matched settings and existing SNN
retrieval baselines. Moreover, with only two time steps, our SNN achieves comparable or superior performance to its ANN counterpart while reducing theoretical module-level energy by 55%. The code is provided in the Supplementary Materials.
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All authors Xintao Zong, Wenxuan Liu, Jianhao Ding, Zhaofei Yu, Tiejun Huang
Artificial Intelligence · Cross-listed from cs.LG
Compactness and Consistency: A Conjoint Framework for Deep Graph Clustering
Wei Ju, Siyu Yi, Kangjie Zheng et al. (+6 more)
Graph clustering is a fundamental task in data analysis, aiming at grouping nodes with similar characteristics in the graph into clusters. This problem has been widely explored using graph neural networks (GNNs) due to their ability to leverage node attributes and graph
topology for effective cluster assignments. However, representations learned through GNNs typically struggle to capture global relationships between nodes via local message-passing mechanisms. Moreover, the redundancy and noise inherently present in graph data may easily result in node representations lacking compactness and robustness. To address these issues, we propose a conjoint framework CoCo, which captures compactness and consistency in the learned node representations for deep graph clustering. Technically, our CoCo leverages graph convolutional filters to learn robust node representations from both local and global views, and then encodes them into low-rank compact
embeddings, thus effectively removing the redundancy and noise as well as uncovering the intrinsic underlying structure. To further enrich the node semantics, we develop a consistency learning strategy based on compact
embeddings to facilitate knowledge transfer from the two perspectives. Our experimental results indicate that our CoCo outperforms state-of-the-art counterparts on various
datasets.
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All authors Wei Ju, Siyu Yi, Kangjie Zheng, Yifan Wang, Ziyue Qiao, Li Shen, Yongdao Zhou, Xiaochun Cao, Jiancheng Lv
Artificial Intelligence
The Operator Mismatch Problem: Deploying BEV Perception with Portable GPU Compute
Rohit Verma and Anand V Bodas
Modern autonomous driving systems rely on bird's-eye-view (BEV) perception models that fuse camera and
LiDAR inputs to detect objects in 3D space. These models are accurate, but they cannot be deployed through standard inference runtimes. The reason is an operator mismatch between dense convolutions (which runtimes handle well), sparse 3D convolutions (which runtimes cannot represent), and
geometric scatter operations (which runtimes have no vocabulary for). Today, every sparse convolution library is CUDA-only and PyTorch-coupled, locking BEV deployment to a single vendor's hardware and a single execution framework. We present BEVPIPE, a framework for deploying
multimodal BEV perception pipelines using portable GPU compute APIs and integrating them with production inference runtimes. BEVPIPE partitions the model into runtime-managed dense subgraphs and three external operator extensions (voxelizer, sparse encoder, BEV projector), connected through a shared GPU
memory space. BEVPIPE achieves a 19.5x end-to-end speedup over conventional deployments while retaining 98.5% of reference mAP. We also showcase that BEVPIPE is portable across different GPU backends.
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Artificial Intelligence
Fed-GRPO: Reward-Signal-Driven Federated Group Relative Policy Optimization
Pengxin Guo, Shuang Zeng, Zonggen Li et al. (+3 more)
Large
Language Models (LLMs) have shown strong
reasoning capabilities when fine-tuned with reinforcement learning (RL), particularly through Group Relative Policy Optimization (GRPO). However, existing GRPO methods assume centralized access to training data, which may not hold in practice due to
privacy or regulatory constraints. To this end, we propose Fed-GRPO, a
federated GRPO training framework that addresses these
privacy constraints by enabling collaborative
reasoning training without sharing raw data, which leverages the reward statistics naturally produced during GRPO training as zero-cost signals to guide aggregation, local training, and communication. Fed-GRPO contains three reward-signal-driven mechanisms: (i) signal-weighted aggregation that weights clients by their reward standard deviation, prioritizing clients with stronger learning signals; (ii) global reward calibration that re-weights per-
prompt objectives based on the local-global reward gap, steering each client toward its relative weaknesses; and (iii) adaptive sparse communication that allocates bandwidth based on the informativeness of each client's update. Extensive experiments on mathematical
reasoning tasks demonstrate that Fed-GRPO achieves the best performance among all
federated methods, clearly outperforms FedAvg and approaches centralized training performance, while losslessly reducing communication by $32\times$ and supporting up to $621\times$ compression under tight bandwidth budgets with only graceful accuracy degradation. Our code is available at https://github.com/HKU-HealthAI/Fed-GRPO.
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All authors Pengxin Guo, Shuang Zeng, Zonggen Li, Weiying Zheng, Mengting Liu, Liangqiong Qu
Artificial Intelligence
BridgeGuard: Explicit Safety Drift for Diffusion-based Autonomous Driving
Zhenjun Qiu, Jianing Huang, Dongang Liu et al. (+6 more)
Diffusion-based driving planners capture diverse behaviors but can generate unsafe trajectories under distribution shift. We propose BridgeGuard, a safety-constrained
diffusion planning method that progressively strengthens a constraint term during denoising to drive intermediate trajectories toward a scene-dependent safety domain. Corrections operate in a low-dimensional curve space, promoting
geometric coherence. A learned module, DistanceFieldNet, predicts a time-dependent distance field from bird's-eye-view features. Value and spatial-gradient supervision at queries sampled beyond expert trajectories teaches this field about both safe and unsafe regions. The learned field supplies the constraint term through safety injection while the pretrained perception backbone and planner remain frozen. We further establish sufficient conditions for terminal safety in an idealized continuous-time bridge. On Bench2Drive, BridgeGuard improves driving score/success rate from 87.99/74.99% to 90.88/76.36% for BridgeDrive and from 80.79/58.18% to 90.46/74.09% for $\text{DiffusionDrive}^{\text{geo}}$, demonstrating cross-model generalization.
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All authors Zhenjun Qiu, Jianing Huang, Dongang Liu, Baiyu Du, Yixun Niu, Hao Yang, Xinyu Huang, Chuan Hu, Shu Liu
Artificial Intelligence · Cross-listed from cs.SE
Evaluating Local Language Model Agents for Reproducible Data Engineering: An Empirical Software Engineering Study of Mobility Workflows
Jorge García-Carrasco, Javier Sanchis, Alejandro Reina-Reina et al. (+2 more)
Context: Large
language model (LLM)
agents are increasingly used as software and data-engineering assistants, yet evidence about locally deployable open-weight
agents remains limited. Existing evaluations often emphasize textual responses or isolated code generation rather than the validity of complete engineering artifacts. Objectives: We evaluate whether local LLM
agents can produce correct and reproducible data-engineering artifacts, quantify the effect of a closed-loop workspace condition, and examine trade-offs in model scale, architecture, quantization, runtime, tool use, and failure. Methods: We introduce a
benchmark of fifteen mobility-workflow tasks covering data discovery, connectors, transport-feed processing, semantic enrichment, feature engineering, validation, visualization, and reporting. Deterministic checkers assess generated scripts, tables, structured files, figures, and reports. Ten local configurations are evaluated in one-shot and closed-loop conditions, with five repetitions per model, mode, and task, yielding 1,500 scored attempts on a consumer-grade GPU. Results: Among models larger than two billion parameters, the workspace condition increases pass rates by 26.7-52.0 percentage points over one-shot generation. The strongest configuration reaches 85.3% artifact-level success, and a quantized 9-billion-parameter model reaches 69.3% with an approximately 6.5 GB
memory footprint. Gains are largest when intermediate artifacts expose errors the
agent can inspect and repair. Conclusion: Local open-weight
agents can support a meaningful subset of software-intensive data-engineering work, but reliability depends on model capability, task verifiability, and deterministic validation. The
benchmark provides a reproducible method for evaluating complete
agent configurations before adoption in engineering workflows.
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All authors Jorge García-Carrasco, Javier Sanchis, Alejandro Reina-Reina, Alejandro Maté, Juan Trujillo
Artificial Intelligence · Cross-listed from cs.RO
RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes
Bohan Zhou, Xingbei Chen, Emily Huang et al. (+17 more)
Embodied coding
agents can combine modular
robot skills with frozen end-to-end policies, yet effective composition requires anticipating which policy family will succeed in the current physical state. We present RoboAware, which builds on coding
agents' skill orchestration by learning only a state-conditioned responsibility coordinator from counterfactual outcomes. Inspired by the success of REPL, we propose the $P^5$ schema and formulate a hierarchical MDP based on it. $P^5$ organizes skills uniformly into five semantic stages, defining where responsibility can be compared. To address the lack of counterfactual branch outcomes in existing work, we introduce State-Locked Counterfactual Branching (SCB), which restores the same training state to generate and execute a code block from each admissible family, exposing outcomes that selected-branch experience leaves unobserved. Building on this, we propose Execution-Aware Learning (EAL), which combines Monte Carlo tree search with Q-learning to distill these outcomes into family-conditioned values. At deployment, the coordinator selects the policy family according to observable context, and the frozen coding
agent generates the next local code block. Comprehensive single-episode evaluations on 100 tasks show that RoboAware reaches a 77.0% overall success rate, with SOTA averages of 90.0% on RoboSuite, 73.8% on diverse LIBERO-Pro task clusters, and 90.0% on challenging RoboTwin bimanual tasks, outperforming existing code-as-policy and VLA-harness baselines.
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All authors Bohan Zhou, Xingbei Chen, Emily Huang, Weilin Ruan, Haojian Huang, Yehang Zhang, Zexi Li, Wenqian Li, Qize Yu, Zetian Song, Leyi Wu, Jinghao Li, Mingxuan Song, Xinrun Xu, Zongyang Qiu, Yangkai Wei, Tianyi Zhang, Kaiwen Zhou, Yinchuan Li, James Cheng
Artificial Intelligence · Cross-listed from cs.CV
Conditional Residual Prediction: Improving Autoregressive Video Diffusion without a Bidirectional Teacher
Bowen Zheng, Zhiguang Liu, Jiarong Ou et al. (+2 more)
Causal
video diffusion models generate
video autoregressively, which suits streaming, interactive, and long-
video generation. Under standard training, however, they often yield lower generation quality than bidirectional models of the same size. Many existing approaches address this gap by initializing from or distilling a pretrained bidirectional teacher. We instead train a causal model from an image-model initialization, with no bidirectional
video model at any stage. Because this path requires neither a large bidirectional teacher nor a complex
distillation pipeline, it is simpler and more scalable. On this path, we find that a causal model trained on ground-truth history becomes strongly dependent on it, so that at inference errors in its own generated history propagate forward. We hypothesize that much of this dependence is unnecessary, because the current input already determines much of what the history provides. We propose Conditional Residual Prediction (CRP), a simple recipe for reducing a model's reliance on a condition: the model first predicts the target without the condition, and the condition may only add a residual on top of this prediction. Applied to history, CRP makes the model predict each chunk from the present as far as it can and use the past only for what the present cannot supply. In controlled experiments, CRP nearly closes the 6.14-point gap to a bidirectional model trained under the same setup. Scaling this recipe, we train Optica, a 2B-parameter causal
video model that autoregressively generates 5-second 480p
videos and reaches 82.78 on VBench with only about 15M training
videos.
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All authors Bowen Zheng, Zhiguang Liu, Jiarong Ou, Rui Chen, Tianyang Hu
Artificial Intelligence
From Chain-of-Thought to Loops: Non-Autoregressive Latent Reasoning via Looped Transformers
Gerard Grau García, Arnau Padrés Masdemont, Niccolò Grillo et al. (+3 more)
Chain-of-thought (CoT)
reasoning often improves
language-model performance by giving models additional computation before answering. However, explicit CoT expresses this computation as a sequence of autoregressively generated
tokens. Latent
reasoning replaces these
tokens with compact continuous states, but most autoregressive latent-
reasoning methods retain a left-to-right dependency among latent vectors. We introduce LLoCoT: a looped latent-
reasoning framework that replaces left-to-right latent generation with iterative refinement of a compact latent workspace. A shared
transformer is reapplied for a small number of refinement iterations, jointly updating the latent slots based on the
prompt and the evolving workspace state. Using the refined state, a probabilistic head predicts a distribution from which latent
tokens are sampled in parallel and used to condition an autoregressive decoder for answer generation. Training uses continuous representations derived from explicit CoT together with a final-answer prediction loss and likelihood-based supervision of the latent states. Across HumanEval and MBPP, LLoCoT achieves the highest mean among the evaluated methods, performing on par in accuracy with
Reasoning SFT, our explicit-CoT baseline, while outperforming the base model, answer-only SFT and NF-CoT. Relative to
Reasoning SFT, LLoCoT reduces time to the first answer
token by approximately $36\times$ and
reasoning-phase latency by approximately $42\times$, while increasing end-to-end throughput by $9.2\%$. This design replaces serial thought generation with parallel latent-slot refinement while retaining probabilistic latent modeling and autoregressive answer decoding.
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All authors Gerard Grau García, Arnau Padrés Masdemont, Niccolò Grillo, Jordi Ros-Giralt, Arash Behboodi, Victor Conchello Vendrell
Artificial Intelligence · Cross-listed from cs.CR
GROB: A Multi-Agent Architecture for Public-Trace Investigation of Candidate Agentic Activity
Chiara Bonfanti and Cataldo Basile
We present GROB, a multi-
agent architecture for investigating candidate autonomous-
agent activity through public Internet traces when privileged telemetry is unavailable. The system performs controlled, read-only collection of public traces and preserves selected observations for later resolution. In a frozen September 2026 corpus, several collected traces became more informative as additional public evidence emerged. The strongest result concerns Census-labelled identifiers captured on 9 September. Public revision records later resolved these identifiers to specific Census requests from 16 - 17 June. Other results show weaker links between traces collected by GROB and evidence reconstructed or reported later. These links vary in strength, and only some can be tied to specific public records. The results show that sparse public traces can remain useful even before their significance is fully understood. Such evidence can support later reconstruction, but public traces alone do not establish organizational attribution. Execution identity presents a separate problem, as continuity of
agent identity remains an active research question for autonomous
language-model
agents.
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Artificial Intelligence
Who Verifies the Verifier? Co-Evolving Inspectable Graders with Self-Improving Agents
Xing Zhang, Guanghui Wang, Yanwei Cui et al. (+4 more)
We changed the
agent: did it actually get better? Every self-improving
agent loop answers this hundreds of times, and every answer comes from a verifier. On open-ended tasks none exists, so the loop is handed a hand-written rubric or a bare LLM judge grading output from a model like itself, inviting reward hacking and shared blind spots. We make the verifier the evolving object: an inspectable expression over small, mostly deterministic drawback detectors, synthesized from clustered failures, gated at birth, and selected for agreement with a ten-item anchored reference set plus consensus over unlabeled outputs, never for the
agent's score. On MBPP+ it gains +0.21 held-out agreement over the hand-authored seed composition, on every seed, and ends ahead of the bare LLM judge it contains. One finding should change how co-evolved verifiers are validated: removing the anchor guards collapses the verifier into a vacuous always-pass grader, yet that collapsed verifier trains skills just as well. Downstream task score cannot certify a self-evolved verifier. Score does answer sufficiency, and there an evolved verifier can substitute: Double Ratchet, pairing the verifier with a lifecycle-managed skill loop, retains 88-110% of the lift that ground truth or a rubric buys the same loop, across code generation, enterprise text-to-SQL, and reference-free report generation. When evolved skills gamed the report rubric, an outer judge caught it and one added detector repaired it; the judge itself was wrong until given the task contract.
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All authors Xing Zhang, Guanghui Wang, Yanwei Cui, Ziyuan Li, Wei Qiu, Bing Zhu, Peiyang He
Artificial Intelligence · Cross-listed from cs.CV
ProtoSemImage: Image-Valued Prototypes with Deformable Row Alignment for Interpretable Document Classification
Mohammad Zare and Pirooz Shamsinejadbabaki
Prototypes in classification models are almost always vectors, and a vector has no readable form. This paper asks what happens when a prototype is an image. Documents give the question a natural form, because a document can be rendered as a multi-channel image in which every
token becomes a pixel, so a class representative can take the same shape and the same channel semantics as the inputs it stands for. ProtoSemImage represents each class by one or more visual archetypes: prototype images in a four-channel HSV space whose channels carry named linguistic factors. A Skip-Gram objective learns that color space end to end through a four-dimensional bottleneck, discourse boundary rows become differentiable typed difference rows, and classification reduces to 2D visual template matching: a deformable row
alignment between a document image and the archetype bank, in the spirit of dynamic time warping. Because the match is a spatial pattern comparison rather than a linear readout, the model reports where an input departs from its archetype and along which channel, and a generative head decodes each archetype back into text. The image representation works: it beats an otherwise identical model with vector prototypes in all three paired seeds, by between 4.3 and 11.8 points on a ten-class task. The distance-based matching does not. A diagnostic that keeps the representation fixed and swaps only the classifier recovers the sequence baselines, which locates a 20.6-point shortfall in the matching rather than in the color compression, and a
benchmark built so that a pair of documents shares a bag of words and differs only in arrangement confirms the layout-preservation it was designed for. We report both directions, because for a representation whose whole purpose is inspect ability, the failure modes are as informative as the gains.
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Artificial Intelligence · Cross-listed from cs.LG
Kislay Aditya Oj, Nidhi Jain, Sri Surya Varma Datla et al. (+3 more)
AI research progress can be viewed as the interaction between two processes:
benchmark creation and method discovery. Historically, both were driven by human intelligence. However, recent advances in AI have accelerated automated method discovery, while automated
benchmark creation has received comparatively less
attention. To enable self-advancing systems, we propose Generative
Adversarial Loop (GAL), a generator-discriminator framework alternating between two agentic searches: (1) a discriminator that generates
adversarial data to expose weaknesses in current systems, and (2) a generator that discovers
algorithms to overcome them. We apply this framework to approximation
algorithms for efficient inference. Unlike existing auto research systems, which primarily focus on
algorithm discovery, GAL introduces a discriminator
agent that automates goalpost setting by continually searching for weaknesses in the current
algorithm. We demonstrate
adversarial data generation across four tasks: KV compression, sparse
video generation, sparse
attention, and context extension, where the discriminator identifies weaknesses in state of the art techniques. We further show that GAL enables autonomous improvement, with newly discovered
algorithms improving not only on adversarially generated data, but also on established
benchmarks. Specifically, GAL improves CompactorPress on KV compression with Qwen3-4B at 4x, raising performance on the discriminator
dataset from 0.35 to 0.97, while also outperforming RULER-HARD (+0.77 pts). For context extension, GAL boosts Dual Chunk
Attention from 0.20 to 0.90 on the discriminator
dataset, while yielding gains on standard
benchmarks(ScienceFiction (+6 pts) and PG19 32K (-0.33 PPL)). GAL thus provides a path toward autonomous goalpost setting and algorithmic improvement, where AI systems continually discover their own weaknesses and develop methods to overcome them.
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All authors Kislay Aditya Oj, Nidhi Jain, Sri Surya Varma Datla, Priyanka Jayaswal, Kumar Krishna Agrawal, Aditya Desai