Artificial Intelligence
Optimal Resource Utilization for Autonomous Laboratory Orchestrators
Austin McDannald, Julia Tisaranni and Howie Joress
In autonomous laboratories, AI
agents suggest the next batch of experiments to do. However, planning and executing those tasks taking full advantage of the available resources is a completely different question. This can be challenging when dealing with real-world hardware constraints, especially so when there are multiple instruments with different capacities and throughputs. Here we demonstrate a 2-step method to address resource utilization for our autonomous platform for metal-organic framework synthesis. First, we use constraint programming to find optimal schedules. This finds schedules that minimizes the total time while still satisfying the limitations and capacities of the hardware. Secondly, we use a system of status dependencies for each task, which allows for the robust execution of the optimal schedules.
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Artificial Intelligence · Cross-listed from cs.LG
Right in the Right Way: LM Training with Verifiable Rewards and Human Demonstrations
Mehul Damani, Isha Puri, Idan Shenfeld et al. (+1 more)
RL with verifiable rewards (RLVR) has emerged as a powerful paradigm for training LMs on tasks with well-defined success metrics, such as code generation and mathematical
reasoning. However, current RLVR methods optimize only what can be objectively scored, often neglecting subjective, non-verifiable aspects of human-like outputs, such as style and structure. This limitation leads to well-documented failure modes such as diversity collapse, unnatural-sounding responses, and reward hacking. We propose an
adversarial generator-discriminator framework that augments verifiable rewards with a learned signal from human demonstrations. A generator model is trained using RL to maximize both task accuracy and an
adversarial reward derived from a discriminator. The discriminator, trained alongside the generator policy, learns to distinguish human-written outputs from model-generated ones. The discriminator serves as a learned proxy for the human output distribution, providing feedback on aspects of generation that are difficult to formalize as scalar rewards. Across diverse domains, including bug fixing and open-ended generation, our approach consistently improves non-verifiable properties while preserving the accuracy gains of RLVR. In bug fixing, our method produces solutions with significantly lower edit distance compared to RLVR baselines while matching end performance. In story generation, our method significantly improves win rate while producing stories that are diverse and more human-like. And in a simple reward hacking
benchmark, our method nearly eliminates model misbehavior while maintaining high
benchmark scores. Together, these results show that our approach bridges RL and SFT, offering a scalable path toward jointly optimizing the verifiable and non-verifiable properties of a task.
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All authors Mehul Damani, Isha Puri, Idan Shenfeld, Jacob Andreas
Artificial Intelligence · Cross-listed from cs.IR
Zhuoxuan Zhang, Kangqi Ni, Yuhang Chen et al. (+11 more)
Generative
reasoning re-rankers achieve strong recommendation accuracy by emitting a chain-of-thought before re-ordering a candidate list, but they are slow at inference: an autoregressive (AR) decoder spends one sequential forward pass per
reasoning token, and the
reasoning trace far exceeds the ranking it produces. To reduce this cost, block-
diffusion language models decode many positions in parallel over a few denoising steps and are substantially faster, yet naively converting an AR re-ranker into one opens two accuracy gaps: (1) a structural gap: answer positions are denoised in parallel and scored independently, so the decoder emits invalid rankings (duplicated, dropped, or out-of-set identifiers) that AR avoids through left-to-right masking; and (2) a distributional gap: fine-tuning the converted model on fixed teacher trajectories is off-policy relative to its own decoding at inference, leaving a residual accuracy gap. To close both gaps while keeping the speedup, we propose
Diffusion-GR2, a recipe that converts our AR
reasoning re-ranker (GR2) into a block-
diffusion re-ranker. First, conversion fine-tuning (CFT) adapts the AR-initialized
diffusion model to denoise the answer into a valid permutation on its own, without an external constrained decoder. Next, on-policy
distillation (OPD) then supervises the model on its own decoded trajectories with dense per-
token targets from the AR teacher. Finally, we apply a reinforcement-learning (RL) stage against a re-ranking reward on top of OPD's on-policy policy. Experiments on Amazon Beauty demonstrate that
Diffusion-GR2 recovers to near-parity with the AR re-ranker, while block-parallel decoding raises decode throughput by $2.4$–$3.5\times$ at the model's
reasoning output length. Ablations show that CFT recovers most of the conversion gap, and that on-policy
distillation further closes it to the AR reference.
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All authors Zhuoxuan Zhang, Kangqi Ni, Yuhang Chen, Mingfu Liang, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Frank Shyu, Adam Song, Sandeep Pandey, Luke Simon, Tianlong Chen, Xi Liu
Artificial Intelligence · Cross-listed from cs.CL
Adversarial Pragmatics for AI Safety Evaluation: A Diagnostic Framework and Seed Benchmark for Language-Mediated Control
Brett Reynolds
Safety evaluations for
language models increasingly depend on judgments about ambiguous natural-
language behaviour: whether a model followed an instruction, refused appropriately, complied with a policy, or misreported progress in an agentic task. Existing
benchmarks compress these into pass/fail labels, obscuring whether failures reflect capability limits, policy ambiguity, instruction conflict, scaffold failure, or unstable evaluator judgments.
Adversarial pragmatics is safety-relevant model behaviour under instruction conflict, embedded commands, quotation, scope ambiguity, deixis, and indirect
speech acts. It's designed to extend to multi-turn
agent transcripts, but the seed set represents that family with a single-turn tool-result contrast. This paper introduces a diagnostic framework, an 18-item seed
benchmark, a 54-row pilot, and a six-
cell LLM-judge assessment, with a protocol keeping task success, policy compliance, risk, refusal, attribution, and confidence analytically separate. The
benchmark separates four inference targets a single label can conceal: the regime-relative reference, configured-system behaviour, evaluator-output interpretation, and taxonomic assignment. Its intended use is diagnosis, not deployment certification, vendor ranking, or a general safety score. A first LLM judge that graded its own outputs with the expected answer visible missed the safety-relevant minority classes. Item-clustered intervals leave four of six chance-corrected agreement statistics unable to rule out a constant labeller, and hierarchical pooling shrinks the one eye-catching rubric effect toward the group mean and widens its interval through zero. Rejudging the objects across three judge models and two information conditions leaves the pattern intact: no
cell recovers more than two of eleven partial successes, and the strongest
cell's edge comes partly from never using that label.
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Artificial Intelligence · Cross-listed from cs.LG
Sampling Meets Interaction: Sequentially-Controlled Multi-Particle Flow-Maps for Efficient Inference-time Search
Binglin Ji, Anindya Sarkar, Hengchang Lu et al. (+2 more)
While generative models have enabled training-free reward
alignment, existing particle-based methods are fundamentally constrained by their propensity to local exploration within narrow regions of the underlying distribution, severely restricting sample diversity. This limitation becomes especially acute under tight reward-feedback budgets, where effective search demands broad, strategic exploration to uncover high-utility regions. To address this, we propose Sequentially-Controlled Interactive Multi-Particle Flow-Maps (IMPFM), a framework for feedback-efficient search. IMPFM progressively transports a group of interactive particles toward the target distribution, maintaining the broad coverage essential for heterogeneous preference
alignment. IMPFM leverages a principled and efficient posterior sample-sharing mechanism across particles powered by flow maps. By correcting individual particle drift with the collective value gradient from the entire ensemble's posterior samples at each correction step, the framework maximizes sample utility to enable global exploration while actively mitigating reward over-optimization, typical of standard control frameworks. Paired with a principled exploration-exploitation reweighting mechanism involving multi-particle interaction, this sequentially corrected multi-particle dynamics explicitly preserves structural diversity and overcomes the weight degeneracy inherent to standard Sequential Monte Carlo (SMC) samplers. Crucially, we prove that the resulting sampling framework yields a multi-particle interaction-aware Feynman-Kac corrector that progressively steers the multi-particle system toward a KL-tilted target distribution, facilitating global exploration and preventing mode collapse. Extensive empirical evaluations and ablations across diverse search and
alignment tasks confirm the efficacy of IMPFM over existing baselines.
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All authors Binglin Ji, Anindya Sarkar, Hengchang Lu, Jens Sjölund, Yevgeniy Vorobeychik
Artificial Intelligence · Cross-listed from cs.SE
Skills Are Not Islands: Measuring Dependency and Risk in Agent Skill Supply Chains
Changguo Jia, Tianqi Zhao, Runzhi He et al. (+1 more)
Agent skills package reusable operational knowledge for Large
Language Model (LLM)
agents, yet as they grow in scope, they become dependency-bearing artifacts whose identities, versions, and provenance remain implicit. This opacity already causes duplicated dependencies and inconsistent installations, exposing a gap that dependency management has yet to close. We introduce
Agent Skill Supply Chains (ASSCs) to characterize mixed skill-package-service dependency graphs and help close this gap. Borrowing from Software Bill of Materials (SBOMs), we design SkillDepAnalyzer to capture natural-
language dependency evidence and model skills as dependency-bearing artifacts. On the SKILL-DEP
benchmark, SkillDepAnalyzer recovers skill metadata and dependency graphs accurately and comprehensively, substantially outperforming an LLM-based baseline and package-centric SBOM tools. Applying SkillDepAnalyzer to over 1.43 million skills, we obtain ASSCs and explore their structural diversity and security signals. We find four structural patterns: skill metadata is activation-ready but governance-poor; dependency graphs span skill, package, and service dependencies with concentrated reuse; recursive skill reuse expands dependency graphs and creates hidden package inventory; and skill dependency clusters form around related workflows. We also find that inspecting a skill alone misses security-relevant signals hiding in its dependencies. By analyzing ASSCs, we identify and report known malicious skills persisting in ASSCs to their developers. Based on these findings, we recommend typed dependency manifests, first-class dependency-cluster management, risk-warning audit commands for skill infrastructure maintainers, and lockfile-like records for skill developers.
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All authors Changguo Jia, Tianqi Zhao, Runzhi He, Minghui Zhou
Artificial Intelligence · Cross-listed from cs.CV
Autonomous Scientific Discovery via Iterative Meta-Reflection
Bingchen Zhao, Sara Beery and Oisin Mac Aodha
Autonomous scientific discovery systems offer the potential to accelerate research by automating the process of hypothesis generation and validation. However, current systems operate within constrained search spaces or require predefined research questions, limiting their capacity for true open-ended inquiry. Furthermore, while they generate hypotheses iteratively, they largely lack the ability to explicitly synthesize their own accumulated findings to uncover complex, interconnected phenomena. We introduce DiscoPER, an autonomous large
language model-powered framework that conducts open-ended research by dynamically generating and executing code to explore
datasets without pre-specified research objectives. To ensure rigorous scientific validity, every proposed discovery must pass statistical testing. To overcome the limitations of isolated search, our framework introduces a second-order
reasoning mechanism that periodically analyzes its own accumulated discoveries. By treating prior discoveries as empirical data, DiscoPER identifies structural patterns, confounds, and epistemic gaps, actively redirecting hypothesis exploration toward uncharted regions of the search space. The search space is further expanded by incorporating tool use, enabling the system to explore hypotheses beyond structured metadata by seamlessly processing and extracting useful information from
multimodal sources like images. Evaluated on iNatDisco, a new
multimodal ecological knowledge
benchmark with pattern-level ground truth obtained from peer-reviewed literature, DiscoPER recovers 8 of 9 known patterns with a 72.7% hypothesis support rate, outperforming both classical causal discovery and LLM-guided baselines. Ablations show that DiscoPER scales with more data, and confirms the benefits of second-order meta-reflection.
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Artificial Intelligence · Cross-listed from cs.LG
Muon as a Residual Connection
Hao Huang
Muon has recently emerged as one of the most effective optimizers for training large neural networks, yet its empirical success has been explained from several different perspectives. In this paper, we propose a simple mechanistic interpretation:
Muon can be understood as an implicit residual connection during training. Specifically, orthogonalizing the update can sacrifice some immediate gradient fidelity while improving representation preservation for downstream layers. We study this trade-off in controlled linear optimization settings, where
Muon can learn representations that are slower to fit a local target but easier for downstream layers to exploit. Our results suggest a conceptual explanation for
Muon and a design perspective for optimizers that balance local descent with downstream usability.
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Artificial Intelligence · Cross-listed from cs.CL
Towards Developing a Multimodal Chat Assistant for University Stakeholders: RAG-based Approach
Md Abu Hanif Shaikh and Abdullah Al Shafi
University stakeholders often face difficulties in accessing timely and reliable information, especially in developing countries, where there are very few intelligent support systems. Existing rule-based chatbots are unable to handle complex, domain-specific queries and are not well-equipped to adapt to evolving institutional policies. As a fill-in-the-gap solution, we present the
multimodal university chatbot with
retrieval-augmented generation. The system combines the large
language model with semantic
retrieval to produce context-based responses from institution-centric resources, such as the university handbook. The system accepts text and image queries through the
vision-
language model and applies quantized inference for rapid deployment on constrained hardware. A scalable backend built with FastAPI, adjoined with a responsive frontend developed with Next.js, ensures real-time usability. Our
multimodal evaluation demonstrates that the system maintains strong satisfaction scores across both text and image queries, despite increased response time for visual inputs. Furthermore, quantitative evaluation shows that
hallucination is reduced from 31.7% to 6.6% in our proposed RAG-based system, confirming the effectiveness of
retrieval grounding.
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Artificial Intelligence · Cross-listed from cs.RO
FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement
Haoran Hao, Shahram Najam Syed, Jeffrey Ichnowski et al. (+1 more)
Robot policies inevitably encounter failures when deployed in real environments. Naive retries often repeat the same mistakes, while many existing recovery methods rely on human intervention. In this paper, we propose Failure-Aware Retry (FAR), a framework that enables
robots to learn from previous failures at test time, adapt their behavior accordingly, and eventually complete the task autonomously. FAR combines Failure-Contrastive Preference Adaptation, which constructs preference learning data from failures to steer the policy away from previously unsuccessful behaviors, with lightweight action perturbations during retries to encourage local exploration. We further incorporate successful recovery trajectories into a training loop for continual policy improvement. Experiments in both simulation and real-world manipulation tasks show that FAR substantially improves success rates and robustness, with average gains of 17.6% over the standard
diffusion policy in simulation and 11.7% in the real world. In addition, FAR improves data efficiency under both reset and timestep budgets during continual policy improvement by exploiting informative failure cases.
Videos and code are available at https://hoar012.github.io/FAR-Project.
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All authors Haoran Hao, Shahram Najam Syed, Jeffrey Ichnowski, Jeff Schneider
Artificial Intelligence · Cross-listed from cs.LG
CausalMix: Data Mixture as Causal Inference for Language Model Training
Zinan Tang, Yukun Zhang, Shaomian Zheng et al. (+7 more)
In Large
Language Model (LLM) training, data mixing plays a pivotal role in determining model performance. Recent methods optimize mixture weights via proxy models, but they rely on the assumption of static data distributions. As a result, when the underlying data pool shifts, these methods require costly retraining from scratch. This limitation restricts their ability to scale seamlessly from small settings to larger data pools and model sizes. In this paper, we propose CausalMix to address this limitation by casting data mixture optimization as a causal inference problem. We formulate the statistical features of the data pool as
covariates and the domain mixture as the treatment. After fitting a causal model on 512 runs of Qwen2.5-0.5B to estimate the Conditional Average Treatment Effect (CATE), we extrapolate the optimal mixture for an 800K data pool and apply it to train a 7B model. Furthermore, we successfully generalize the framework to long chain-of-thought data on Qwen3-4B-Base. By leveraging causal modeling to isolate confounding biases, CausalMix infers state-dependent data mixtures offline, with the selected mixture fixed during each training run. Extensive experiments show that the mixture guided by CausalMix achieves competitive performance across downstream tasks compared with other baselines. The CATE Interpreter visualizes the learned mixing strategy. Overall, CausalMix offers a causal and interpretable framework for optimizing LLM data mixtures.
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All authors Zinan Tang, Yukun Zhang, Shaomian Zheng, Zhuoshi Pan, Qizhi Pei, Dingnan Jin, Qing Cui, Jun Zhou, Yujun Wang, Biqing Huang
Artificial Intelligence · Cross-listed from cs.SE
Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering
James C. Davis, Paschal C. Amusuo, Tanmay Singla et al. (+2 more)
Generative AI is shifting software engineering from a practice organized around scarce implementation effort toward one organized around abundant, low-cost code production. This shift changes the central engineering problem: not whether AI can generate useful code, but how engineers organize architectures, tools, evidence, and feedback loops so that AI-mediated development remains inspectable, correctable, and maintainable. We study this problem through a first-person case study: a 12-week development effort in which a single expert software engineer used frontier AI coding
agents to build a document accessibility remediation system. The empirical record comprises 88 contemporaneous field notes, 420 KLOC of production code, and 1.16 MLOC of tests, lints, supporting documentation, and
agent tooling. From this record, we develop a candidate middle-range theory of governance conversion, expressed as a process model explaining how high-velocity agentic implementation becomes governable. The model explains how agentic implementation velocity surfaces recurring structural failure classes, and how engineering judgment sustains velocity by converting those failures into durable governance mechanisms. In contrast to existing governance models that derive controls from known obligations, governance conversion explains how controls are discovered from failures that become visible only during agentic work. We use our model to make testable predictions and to describe implications for software engineering research and practice.
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All authors James C. Davis, Paschal C. Amusuo, Tanmay Singla, Berk Çakar, Kirsten A. Davis
Artificial Intelligence · Cross-listed from cs.CV
LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models
Arpita Nema, Hanwei Zhu, Xi Zhang et al. (+1 more)
The evaluation of long-term
video quality understanding remains an open challenge for large
vision-
language models (LVLMs). Existing
video quality
benchmarks predominantly focus on short clips and isolated distortions, overlooking the temporal continuity, cumulative degradation, and
reasoning complexity inherent in long-duration content. To address these limitations, we present LongVQUBench, a comprehensive
benchmark for long-term
video quality understanding. LongVQUBench contains over 1200 diverse
videos spanning movies, documentaries, surveillance footage,
egocentric recordings, and animated content, accompanied by 1500 multiple-choice and open-ended questions for validation and testing. To assess perceptual
reasoning across different temporal scopes, we introduce three progressively complex evaluation levels: (i) local event quality understanding (LQU) for analyzing localized distortions; (ii) cross-event quality
reasoning (CQR) for integrating multiple degraded events; and (iii) global quality understanding (GQU) for holistic perceptual evaluation over extended durations. Furthermore, a needle distortion question-answering (NDQA) paradigm is embedded across all three levels, where spatial or temporal artifacts are sparsely inserted to probe fine-grained detection and
reasoning capabilities. Extensive experiments on 14 state-of-the-art LVLMs reveal significant performance degradation with increasing
video length and
reasoning depth, highlighting their limited capacity for long-range temporal integration and perceptual attribution. We envision LongVQUBench as a foundational step toward the systematic, hierarchical, and explainable evaluation of LVLMs' long-term
video quality understanding.
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All authors Arpita Nema, Hanwei Zhu, Xi Zhang, Weisi Lin
Artificial Intelligence
Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
Song-Lin Lv, Weiming Wu, Rui Zhu et al. (+2 more)
While Large
Language Model (LLM)
agents demonstrate proficiency in static
benchmarks, their deployment in real-world scenarios is hindered by the dynamic nature of user queries, tool sets, and interaction dynamics. To address this generalization gap, we formalize OpenAgent (Tool-Use
Agent in Open-World), a problem setting characterized by distributional shifts across query, action, observation, and domain dimensions. To systematically diagnose its impact, we construct a controlled sandbox environment where we define fine-grained environmental shifts across a four-tier hierarchy, Perception, Interaction,
Reasoning, and Internalization, and conduct a comprehensive series of experiments. Our analysis yields a series of key insights, demonstrating that
agents trained via both Supervised Fine-Tuning(SFT) and Reinforcement Learning suffer from varying degrees of performance degradation when confronting open environmental shifts. Building on these insights, we propose Perturbation-Augmented Fine-Tuning, a disturbance-based intervention strategy for SFT that lays the foundation for enhancing
agent robustness and utility in realistic environments. Our code will be released at: https://github. com/LAMDA-NeSy/OpenAgent.
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All authors Song-Lin Lv, Weiming Wu, Rui Zhu, Zi-Jian Cheng, Lan-Zhe Guo
Artificial Intelligence · Cross-listed from cs.LG
Scaling Laws for Collapse in Asynchronous GRPO
Jingwei Song, Haofeng Xu, Jie Xiao et al. (+7 more)
Asynchronous reinforcement learning improves the throughput of large
language model post-training by decoupling rollout generation from policy optimization, but introduces a mismatch between the behavior and learner policies. How the resulting policy staleness couples with the learning rate to govern training stability and collapse time remains poorly understood. We investigate this coupling in vanilla GRPO through controlled sweeps of the synchronization interval $S$ and constant learning rate $\eta$ on Llama-3.2-1B/3B, complemented by experiments on Qwen3-8B. We identify two empirical scaling laws: (i) Stability-boundary scaling: the largest stable learning rate scales approximately as $S^{-1}$, yielding a stability boundary characterized by an approximately constant product $S\eta$. (ii) Collapse-time scaling: among collapsing runs, estimated collapse times scale approximately as $\eta^{-1}$, corresponding to a model- and setup-dependent cumulative learning-rate budget that aligns across synchronization intervals in the Llama sweeps. We interpret these laws through a local analysis of the behavior-dependent GRPO surrogate and a complementary mean-field model. Under local regularity conditions, the analysis yields an $O(S\eta)$ upper bound on the staleness-induced update bias that resets at synchronization. The mean-field model shows how sufficiently strong positive feedback can sustain directional drift when update directions persist across synchronization cycles. When drift speed saturates under optimizer normalization, this mechanism predicts exit from a local surrogate-validity region after an approximately fixed cumulative learning rate. Together, these findings motivate a practical calibration rule: estimate the stability threshold and collapse budget from a coarse sweep, then jointly select $S$ and $\eta$ for the intended training horizon.
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All authors Jingwei Song, Haofeng Xu, Jie Xiao, Chengke Bao, Jingwei Shi, Pengbin Feng, Yuhang Han, Weixun Wang, Eric Yang, Tianyu Shi
Artificial Intelligence · Cross-listed from cs.IR
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
Zhishang Xiang, Zerui Chen, Yunbo Tang et al. (+5 more)
Memory has emerged as a cornerstone of modern LLM-based
agents, supporting their
evolution from single-turn assistants to long-term collaborators. However,
memory is not always beneficial: retrieved
memories often induce a critical issue of sycophancy, causing
agents to over-align with the user at the cost of factual accuracy or objective
reasoning. Despite this emerging risk, existing
memory benchmarks primarily evaluate whether
memories are correctly stored, retrieved, or updated, while overlooking how retrieved
memories influence downstream
reasoning and decision-making. To bridge this gap, we propose MemSyco-Bench, a comprehensive
benchmark for evaluating
memory-induced sycophancy in
agent systems. MemSyco-Bench measures when
memory should influence a decision and how valid
memory should be used. Specifically, it covers five tasks that assess whether
agents can reject
memory as factual evidence, respect its applicable scope, resolve conflicts between
memory and objective evidence, track
memory updates, and use valid
memory for personalization. All related resources are collected for the community at https://github.com/XMUDeepLIT/MemSyco-Bench.
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All authors Zhishang Xiang, Zerui Chen, Yunbo Tang, Zhimin Wei, Ruqin Ning, Yujie Lin, Qinggang Zhang, Jinsong Su
Artificial Intelligence
Agentic generation of verifiable rules for deterministic, self-expanding reaction classification
Daniel Armstrong, Maarten Dobbelaere, Valentas Olikauskas et al. (+4 more)
Computer-assisted synthesis planning breaks target molecules into accessible precursors using large libraries of reaction rules that assign each transformation a deterministic, interpretable label. But chemistry is long-tailed, making manual encoding intractable, and existing tools rely on fixed rulesets that cannot adapt to new chemistries. Here we present a fully automated pipeline in which a multi-
agent framework of large
language models (LLMs) classifies reactions and writes the rules themselves across 665,901 US patent reactions, generating each rule under a verification loop that tests it against the corpus. It expands a standard taxonomy from 68 to 14,073 classes without human curation. With a lightweight fingerprint classifier, it classifies 97.7% of unseen reactions, matching a leading proprietary classifier while resolving chemistry more finely and extending on demand to chemistry outside its training distribution. The result is a living reactivity database and a general route to turning generative models into reliable, self-expanding symbolic systems.
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All authors Daniel Armstrong, Maarten Dobbelaere, Valentas Olikauskas, Helena Avila, Octavian Susanu, Jérôme Waser, Philippe Schwaller
Artificial Intelligence · Cross-listed from cs.RO
DART-VLN: Test-Time Memory Decay and Anti-Loop Regularization for Discrete Vision-Language Navigation
Shaoheng Zhang, Zhichen Li, Guangfu Ma et al. (+1 more)
Memory-based
agents for discrete
vision-
language navigation (VLN) operate under partial observability and may exhibit systematic inference-time failures even with strong pretrained backbones. This paper addresses two recurring problems: stale historical evidence during
memory readout and inefficient local backtracking during action selection. We present DART-VLN, a training-free inference-time framework that combines Test-Time
Memory Decay, which reweights stale and redundant
memory slots without modifying their stored content, with Anti-Loop Regularization, a lightweight next-hop penalty that discourages immediate reversals. DART-VLN introduces no learnable parameters and leaves the
navigation backbone unchanged. Experiments on R2R and REVERIE showed that
memory decay preserved or improved task performance while reducing runtime. The addition of anti-loop regularization further shortened trajectories, reduced local backtracking, and achieved the best overall balance between
navigation quality and efficiency among the evaluated GridMM variants. These results indicate that lightweight inference-time control can improve the reliability and efficiency of
memory-based discrete VLN without retraining.
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All authors Shaoheng Zhang, Zhichen Li, Guangfu Ma, Jie Mei
Artificial Intelligence · Cross-listed from cs.CV
EchoRisk: A Multicentre Echocardiography Dataset and Benchmark for Cardio-Oncology
Grigorios Kalliatakis, Georgia Karanasiou, Georgios Manikis et al. (+10 more)
Therapy-induced cardiotoxicity is the leading non-oncological cause of treatment interruption in breast cancer patients, yet early, automated risk stratification from routine cardiac imaging remains an unsolved problem. We present EchoRisk, the first curated, multicentre, longitudinal echocardiography
dataset with explicit cardiotoxicity labels, released as the primary technical reference for the EchoRisk-MICCAI 2026 challenge. The
dataset comprises 422 patients enrolled in the EU-funded CARDIOCARE prospective study across five European sites, yielding 2,159 echocardiography
videos across 1,123 clinical exams acquired at up to five longitudinal timepoints, alongside a dedicated cohort of 280 patients with baseline imaging for early cardiotoxicity prediction. Three clinically grounded tasks are defined: automated estimation of left ventricular ejection fraction from cine
video (Task 1), classification of LV dysfunction from longitudinal imaging (Task 2), and early prediction of therapy-induced cardiotoxicity from pre-therapy baseline echocardiography alone (Task 3). For each task we specify the evaluation protocol, primary and secondary metrics, and ranking procedure. We establish baseline performance using an R(2+1)D
video backbone with LSTM aggregation trained from Kinetics-400 pretrained weights, demonstrating strong discriminative performance for cardiac functional assessment and LV dysfunction classification, while early cardiotoxicity prediction from a single pre-therapy
video remains a significant open problem for the community. The
dataset, evaluation code, and baseline implementations are publicly available to serve as a
benchmark for further collaboration, comparison, and the creation of task-specific architectures in cardio-oncology.
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All authors Grigorios Kalliatakis, Georgia Karanasiou, Georgios Manikis, Manolis Tsiknakis, Dimitrios Fotiadis, Dorothea Tsekoura, Kalliopi Keramida, Vasileios Bouratzis, Lampros Lakkas, Katerina Naka, Andri Papakonstantinou, Anastasia Constantinidou, Kostas Marias
Artificial Intelligence · Cross-listed from cs.CL
Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework
Hasibur Rahman and Smit Desai
Large
language model (LLM)-based conversational
agents (CAs) are now ubiquitous, creating new opportunities for AI-mediated behavior change. Their capacity to project nuanced personalities and adopt diverse metaphorical roles raises a design question: how should an
agent's persona and personality be calibrated to the moment? Recent evidence suggests that (i) moderate personality expression outperforms low or high extremes on trust, enjoyment, and intention to adopt in goal-oriented tasks, and (ii) context-appropriate metaphors outperform static one-note assistants on user experience and uptake. Yet most CAs still fix both persona and style, risking misalignment when dynamics, urgency, and formality vary, for example in medical information seeking, fitness coaching, and reflective learning. We propose a Fluid Personality Framework that jointly adapts (1) the
agent's metaphorical persona, such as coach, tutor, librarian, or tool, and (2) its personality expression intensity, low, medium, or high, as a function of task context, user goals and traits, and situational urgency. We sketch the framework and its core design dimensions.
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Artificial Intelligence
PedNStream: Scalable Network Flow Simulation for Pedestrian Traffic Management
Weiming Mai, Dorine Duives and Serge Hoogendoorn
Evaluating operational crowd management at network scale requires simulations that can be run repeatedly while adapting interventions to changing conditions. Microscopic models can represent detailed individual movement, but their computational cost may limit their use in such repeated, network-scale evaluations. This paper presents PedNStream (Pedestrian Network Flow Simulation), an open-source, Python-native simulator for macroscopic pedestrian network simulation based on the Link Transmission Model (LTM). PedNStream extends LTM-based pedestrian models with
stochastic link dynamics that represent local variation in pedestrian flow. It uses a utility-based route-choice model to capture how pedestrians adjust their route choices in response to congestion and control interventions as conditions change over time. The modular framework provides controller interfaces for gating, flow separation, and route guidance. We evaluate PedNStream in a staged manner. Synthetic scenarios verify key crowd-dynamics mechanisms, including queue formation, spillback, congestion dissipation, and adaptive rerouting. Real-network experiments assess large-scale behavior against observed pedestrian counts. A closed-loop case study demonstrates controller integration, and a runtime analysis quantifies scalability. These results position PedNStream as an efficient and practical testbed for large-scale pedestrian network simulation and crowd management research.
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Artificial Intelligence · Cross-listed from cs.CL
Reading Order Inference for Complex Document Layouts
Iddo Hakim, Sharva Gogawale, Omer Ventura et al. (+4 more)
Reading order inference remains a critical bottleneck in the digitization of complex historical manuscripts, where pages contain multiple spatially interleaved reading streams, the canonical example being the Glossa Ordinaria layout, in which a central text is surrounded by commentaries that wrap around it in non-rectangular, non-convex regions. We present a training-free, graph-based framework: each OCR text line becomes a node in a directed candidate-transition graph, edges are scored by a weighted additive ensemble of two lightweight
language-model signals (causal
language model conditional likelihood and BERT next-sentence prediction, NSP; a third sentence-
embedding signal was evaluated but did not improve reading order), and the global reading order is recovered as a degree-constrained directed path cover. To avoid the cascading "edge-theft" failures of greedy edge selection, we propose a max-regret inference rule that prioritizes commitments with high opportunity cost. We evaluate on synthetic Glossa Ordinaria grid layouts, on 23 ALTO page geometries (10 historical source pages plus mirrored and flipped variants), and on a 140-page multi-column English subset of OmniDocBench, comparing our method against the canonical recursive XY-cut (PaddleOCR PP-StructureV3) and two LayoutReader variants (layout-only and text+layout) on identical inputs. On wrap-around Glossa layouts our method recovers 95% of ground-truth successor edges on average vs. XY-cut's 50%; on the OmniDocBench multi-column subset it reaches 88% macro edge accuracy versus XY-cut's 75% and LayoutReader's 25%. The LayoutReader baselines transfer poorly due to a word-level vs. line-level granularity mismatch. We additionally verify mirror-invariance under horizontal and vertical page reflections: Our method changes by less than 1 percentage point, classical XY-cut by 2 points, and LayoutReader-T by up to 8 points.
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All authors Iddo Hakim, Sharva Gogawale, Omer Ventura, Gal Grudka, Daria Vasyutinsky-Shapira, Berat Kurar-Barakat, Nachum Dershowitz
Artificial Intelligence · Cross-listed from cs.CL
Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads
Aryo Pradipta Gema, Beatrice Alex and Pasquale Minervini
In long-context use, large
language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pasting them. Identifying which
attention heads perform this synthesis matters for interpreting long-context model behavior. Yet existing detectors miss these heads by construction: they reward heads whose attended
token matches the generated
token, a literal-copy criterion that captures where a head reads but not what it writes through its output-value (OV)
circuit, the very mechanism that carries non-literal
retrieval. We introduce Logit-Contribution Scoring (LOCOS), a write-aware detector that scores each head by the projection of its OV-
circuit output onto the answer-
token unembedding direction, contrasting needle and off-needle source positions in a single forward pass. Across three model families (Qwen3, Gemma-3, OLMo-3.1), mean-ablating the top LOCOS heads on the NoLiMa non-literal
retrieval benchmark collapses ROUGE-L at lower head counts than prior
attention-based detections; on Qwen3-8B, ablating 50 heads drives ROUGE-L from 0.401 to 0.000 while the strongest baseline still retains 0.292. The selected heads are
retrieval-specific: parametric recall and arithmetic
reasoning stay at baseline under the same ablation. On Qwen3-8B, the same ablation also drops MuSiQue from 0.55 to 0.08 and BABI-Long from 0.62 to 0.20, while a random-heads control stays within 0.05 of baseline.
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Artificial Intelligence · Cross-listed from cs.SE
SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests
Yaoqi Guo, Yang Liu, Jie M. Zhang et al. (+3 more)
Large
language model (LLM)-based software engineering
agents are increasingly developed to resolve software issues by generating patches from issue reports and code repositories. Bug reproduction tests (BRTs) are an important building block for such
agents and have been shown useful for patch validation. However, it remains unclear whether BRTs can also help the more central stage of patch generation. We first conduct a preliminary study and find that directly using advanced BRT generators to guide patch generation is not beneficial: fail-to-fail BRTs can mislead
agents, while even fail-to-pass BRTs bring limited or negative gains. Our analysis reveals two reasons: fail-to-pass BRTs may cover only one manifestation of the reported issue, leading to partial patches, whereas fail-to-fail BRTs are unreliable as direct patch-generation targets. Motivated by these insights, we propose SWE-Doctor, a software issue resolution
agent that guides patch generation with runtime diagnoses derived from multi-faceted BRT executions. SWE-Doctor first generates multi-faceted BRTs for different behavioral requirements stated in the issue, then executes and debugs these BRTs to construct runtime-grounded diagnosis records, and finally uses the diagnoses together with localization information inferred during BRT generation to guide patch generation and reduce partial patches. We evaluate SWE-Doctor on Python bug-fixing issues from the widely adopted SWE-bench Verified and SWE-bench Pro across five LLM backends. SWE-Doctor consistently outperforms existing
agents across all 10 LLM-
benchmark combinations, achieving average resolution rates of 75.7% on SWE-bench Verified and 59.4% on SWE-bench Pro. In particular, on the more challenging SWE-bench Pro, SWE-Doctor improves the average resolution rate by 8.0-8.9 percentage points over the baseline
agents.
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All authors Yaoqi Guo, Yang Liu, Jie M. Zhang, Yun Ma, Yiling Lou, Zhenpeng Chen
Artificial Intelligence · Cross-listed from cs.HC
SenseWalk: Agent-Based Semantic Trajectory Simulation Powered by Large Language Models in Zoned Environments
Ziyue Lin, Xinhang Xie, Kangyi Wang et al. (+1 more)
Semantic trajectory analysis has recently emerged as an approach for modeling human movement by capturing implicit patterns and behaviors through semantic information (e.g., visitors' profiles and goals) beyond raw spatial paths to better understand why people move in certain ways. However, analyzing semantic trajectories in real-world scenarios remains challenging, as collecting high-quality data is costly and often lacks rich semantic information. Meanwhile, existing simulation tools require substantial technical expertise, which makes them difficult for practitioners to adopt. To address these limitations, the paper proposes ${SenseWalk}$, an interactive system that supports simulating semantic trajectories by LLM-powered
agents. We develop a simulation workflow that combines LLMs and the social force model to balance physical plausibility and semantic coherence. A user-friendly interface is designed to facilitate users in customizing the simulation configuration and analyzing simulation outputs. We also conduct a quantitative experiment to evaluate the effectiveness of our simulation workflow, and a user study (n=12) to assess the usefulness and efficiency of our system.
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All authors Ziyue Lin, Xinhang Xie, Kangyi Wang, Siming Chen
Artificial Intelligence · Cross-listed from cs.CV
TRCGL-Net: A Long-Tailed Multi-Label Chest X-Ray Classification Framework with Generative Data Augmentation and Label Co-Occurrence Modeling
Tong Shao, Hongshun Ling, Li Zhang et al. (+4 more)
Chest X-ray multi-label classification is a core task in intelligent medical imaging diagnosis. However, real clinical data often exhibit extreme long-tailed distributions, leading to degraded performance on rare diseases in tail classes. This issue is not only driven by data scarcity but also by two intrinsic factors:1) attenuation of tail-class lesion representations under complex anatomical backgrounds, and 2) dominance of head classes in modeling label co-occurrence relationships. To address these challenges, we propose TRCGL-Net. First, a learnable text-guided conditional
diffusion model is employed to generate high-quality tail-class chest X-ray image samples under disease semantic constraints, improving data diversity and realism of rare disease patterns while alleviating class imbalance and preserving pathology-consistent semantics.Second, a channel reweighting mechanism is introduced to perform feature recalibration by emphasizing disease-relevant feature channels, thereby improving feature discriminability under long-tailed distributions.A class-aware
attention mechanism is further applied to generate class-specific
attention maps, enabling the model to localize disease-relevant regions and focus on fine-grained lesion areas.Finally, a graph convolution network based on label co occurrence is introduced to establish an information propagation mechanism among categories. Experiments on the PadChest
dataset show that the proposed method achieves a tail-class mAP of 0.4904, an overall mAP of 0.4408, and an mAUC of 0.8989, outperforming state-of-the-art methods. TRCGL-Net effectively improves recognition performance for rare diseases under long-tailed distributions and mitigates the impact of extreme class imbalance in chest X-ray multi-label classification.
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All authors Tong Shao, Hongshun Ling, Li Zhang, Jinjing Wu, Junke Wang, Yuan Gao, Fang Wang
Artificial Intelligence
Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering
Louis Donaldson, Connor Walker, Koorosh Aslansefat et al. (+1 more)
Trustworthy deployment of Agentic
Retrieval-Augmented Generation (RAG) systems requires mechanisms for estimating when multi-stage
reasoning pipelines may fail. This paper presents an uncertainty-aware Agentic
Retrieval-Augmented Generation (RAG) framework in which planner, evaluator and generator stages produce uncertainty signals derived from semantic divergence and generator self-evaluation. These signals are propagated through a
Bayesian Network (BN) to estimate system-level uncertainty and provide node-level indicators of potential failure points across the workflow. The approach is evaluated on StrategyQA and HotpotQA using GPT-3.5-Turbo and GPT-4.1-Nano, with Area Under the Receiver Operating Characteristic Curve (AUROC), Area Under the Accuracy-Rejection Curve (AUARC), Expected Calibration Error (ECE), and Brier Score used to assess discrimination, selective prediction and calibration. Results show that
Bayesian propagation is more effective on HotpotQA, where uncertainty accumulates across multi-hop
reasoning stages, while StrategyQA exposes limitations caused by miscalibration and unreliable upstream signals. The study positions
Bayesian uncertainty propagation as a promising but preliminary mechanism for monitoring Agentic RAG systems, with future validation required in industrial domains such as Offshore Wind (OSW) maintenance decision support.
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All authors Louis Donaldson, Connor Walker, Koorosh Aslansefat, Yiannis Papadopoulos
Artificial Intelligence · Cross-listed from cs.LG
Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations
Alexander Chemeris, Ming Jin and Randall Balestriero
Time-series models are often evaluated by what they can forecast or classify, but those scores do not show whether their representations preserve the process state a user may want to inspect: event timing, phase, amplitude, frequency, or regime variables. We introduce Aionoscope, a generator-based diagnostic tool for debugging latent-state accessibility in frozen time-series representations. Aionoscope separates process generation from observation rendering, producing seeded synthetic streams with exact categorical and dense labels across mixture complexity and nuisance variation. We instantiate Aionoscope as Primitive Process Mixtures and evaluate 37 model-plus-adapter systems with a common pooled linear-probe protocol. The main result is a mismatch between coarse and fine-grained accessibility. Most systems make component presence easy to recover, but expose dense process state much less reliably: the highest observed dense-probe row reaches 0.689 mean masked $R^2$, while a dense-feature oracle reaches 0.999. This is the failure mode Aionoscope is designed to surface: a representation can look informative at the level of "what kind of signal is present" while hiding the timing, phase, amplitude, frequency, or regime variables needed for debugging.
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Artificial Intelligence · Cross-listed from cs.CV
Learning Cardiac Motion Priors for Implicit Neural Representations
Andrew Bell, George Webber, Steffen E Petersen et al. (+3 more)
Implicit neural representations (INRs) are well suited to cardiac motion estimation, providing continuous, compact representations of motion fields. However, fitting an INR to each image sequence is time-consuming and sensitive to the optimisation trajectory. Learned priors can help guide optimisation towards plausible motion fields and enable faster adaptation, but learning priors for cardiac motion INRs remains under-explored. In this work, we compare four strategies for learning cardiac motion priors, including a population prior learned by joint optimisation, a consensus prior obtained by weight averaging, auto-decoders, and meta-learning. Using short-axis tagged cardiac
magnetic resonance images from the UK Biobank, we evaluate their impact on tracking accuracy, motion behaviour, and adaptation trajectory. All learned priors substantially improved early adaptation performance compared with random initialisation. While the simple consensus prior was effective, auto-decoders recovered large deformations faster during early adaptation. Meta-learning achieved strong early performance and maintained the best adaptation trajectory over 50 iterations. The code can be found at https://github.com/andrewjackbell/nvf_priors .
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All authors Andrew Bell, George Webber, Steffen E Petersen, Andrew P King, Muhummad Sohaib Nazir, Alistair Young
Artificial Intelligence · Cross-listed from cs.CV
Chengzhi Hu, Xuewen Liu, Jing Zhang et al. (+3 more)
Diffusion Transformers (DiTs) have demonstrated impressive performance in image generation but suffer from substantial computational overhead and resource consumption. Post-training
pruning offers a promising solution; however, due to DiTs' unique architectural design and parameter distribution, traditional
pruning methods are inapplicable, leading to significant performance degradation. Specifically, prior methods developed for LLMs, which derive metrics through a series of approximations, amplify the relative contribution of weights in the saliency metric. In addition, weights in DiTs exhibit significantly larger magnitudes than those in LLMs. Moreover, existing
pruning granularity overlooks variations in model structures. In this paper, we propose DiT-
Pruning, which improves
pruning performance by introducing customized saliency criteria and
pruning granularity. We design a novel metric that balances the contributions of weights and activations from an energy-based perspective, enabling more effective identification of important elements. Furthermore, we observe distinct clustering patterns in the two-dimensional weight space. Accordingly, we adopt a clustering-aware
pruning granularity, enabling effective sparse allocation. Extensive evaluations on various DiTs show that our method consistently preserves image quality, especially under high sparsity. For FLUX.1-dev at 512x512 resolution on MJHQ, DiT-
Pruning achieves only a 0.001 loss in CLIP score at 50% sparsity, dramatically outperforming recent
pruning methods.
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All authors Chengzhi Hu, Xuewen Liu, Jing Zhang, Mengjuan Chen, Zhikai Li, Qingyi Gu