Artificial Intelligence
Fathom-Vaidya: Advancing Medical Reasoning with Rubric-Based Rewards
Kalash Shah, Kunal Singh, Snehan J et al. (+1 more)
Deploying Large
Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic
reasoning: the convergent, evidence-driven task of inferring a patient's condition from clinical data to produce a diagnosis, and clinical healthcare
reasoning: the broader, navigational judgment required to communicate, plan, and adapt across multi-turn clinical interactions where a single correct answer may not exist. Recent
benchmarks such as HealthBench and MedXpertQA reveal persistent weaknesses in both areas, exposing failures in complex diagnostic scenarios and limitations in contextual, patient-centered dialogue. We introduce a sequential training framework that targets these facets using synthetic data and rubric-based reinforcement learning. First, we improve diagnostic
reasoning using MedBullets-derived questions with rule- and rubric-guided Reinforcement Learning (RL). We then shift to clinical
reasoning by generating 5.3k synthetic multi-turn scenarios, each paired with multi-dimensional rubrics to comprehensively assess the response. This approach yields over 10% improvement on MedXpertQA, and our 30B model achieves 50.1% accuracy on HealthBench-Hard, surpassing proprietary baselines including GPT-5 (thinking). Our results show that targeted synthetic
datasets and rubric-based training can systematically improve both diagnostic and interactive clinical
reasoning in medical LLMs.
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All authors Kalash Shah, Kunal Singh, Snehan J, Shreyas Singh
Artificial Intelligence · Cross-listed from cs.DC
Conduit: An Experience Data Plane for Distributed Reinforcement Learning
Sitong Zhang, Tuo Shi, Mario Di Francesco et al. (+2 more)
Distributed reinforcement learning (RL) scales training by parallelizing actors and learners around an Experience Buffer. As RL workloads grow, however, the buffer becomes more than a replay queue: it is the storage substrate of a large-capacity, latency-critical experience path that every iteration traverses to move, transform, sample, and batch experiences before learner updates can begin. Existing RL systems embed this path inside framework control flow or expose it as a request-driven buffer service, leaving experience placement fixed and experience-path work difficult to schedule independently as a runtime-level optimization target. We present Conduit, a framework-agnostic runtime that exposes RL experience management as an explicit systems optimization problem. At its core is the Experience Data Plane (EDP), a runtime abstraction that separates RL experience-handling semantics from framework-specific execution logic by exposing experience ingestion, experience placement, and experience delivery as explicit control points. Built on EDP, Conduit introduces capacity-constrained, bandwidth-aware placement, which distributes experience state across CPU/GPU
memory tiers and nodes under heterogeneous interconnect and device-
memory constraints, and latency-aware scheduling, which controls when experience-path handling runs to reduce exposed experience-path latency while preserving RL semantics. Integrated with RLlib without changing its framework execution logic, Conduit reduces exposed experience-path latency by up to 97% and end-to-end iteration latency by up to 38%, scales to 1,024 GPUs, and preserves convergence.
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All authors Sitong Zhang, Tuo Shi, Mario Di Francesco, Zeke Wang, Bo Zhao
Artificial Intelligence
Predicting Postprandial Glycemic Response from Meal Images, Clinical Variables, and Gut Microbiome Information
Varvara Kondratyeva, Kamilia Zaripova, Nassir Navab et al. (+1 more)
Predicting postprandial glycemic response (PPGR) is fundamental to personalized nutrition and type 2 diabetes management, yet existing approaches typically rely on manually reported dietary intake, limiting their scalability in free-living settings. We propose a
multimodal framework that replaces manual dietary logging with image-derived macronutrient estimates and integrates them with clinical variables and gut microbiome information for personalized PPGR prediction. The framework jointly performs image-based macronutrient estimation and glucose prediction, while an
attention-based prediction module models interactions between dietary and host-specific information. We evaluate the proposed approach on a real-world
dataset comprising meal images, continuous glucose monitoring, clinical variables, and gut microbiome profiles. The proposed model outperforms existing PPGR baselines using image-derived nutritional inputs and approaches the performance of methods that rely on manually reported macronutrients despite using automatically estimated nutritional information. These results demonstrate that combining image-derived nutrition with complementary clinical and gut microbiome information provides a practical foundation for scalable personalized PPGR prediction.
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All authors Varvara Kondratyeva, Kamilia Zaripova, Nassir Navab, Azade Farshad
Artificial Intelligence · Cross-listed from cs.CV
Do LiDAR Language Models Really Understand Spatio-temporal Relationships?
Runyi Yang, Murat Akkoyun, Di Wen et al. (+8 more)
Recent 4D
LiDAR language models aim to reason about objects and their evolving spatial relationships. Yet, in our evaluation, always selecting the same option nearly matches the multiple-choice accuracy of two B4DL-derived configurations. We introduce
LiDAR-Hallu, a geometry-referenced
benchmark and diagnostic protocol with 10,000 questions across 150 nuScenes scenes. It covers object existence, ego-relative position, distance ordering, relative motion, and temporal localization, with explicit rules for selecting objects, comparing times, and determining reference answers. Our protocol combines fixed-answer and candidate-content controls, cross-scene pairs with identical
prompts but opposite reference answers, and relation-specific recall. Analysis of 100,000 recorded responses reveals failures hidden by aggregate accuracy. Candidate duration alone makes temporal answers predictable without observing
LiDAR. On paired questions, the models frequently give the same answer to scenes requiring opposite answers. Relation-specific analysis further shows that both configurations miss every positive lateral-motion case across all tested conditions. Temporal-shuffle contrastive decoding provides little net improvement, as repairs are largely offset by new errors and the main failures persist. These results show that evaluating spatio-temporal
reasoning requires testing whether models distinguish the queried physical relationships, rather than relying on individual-answer accuracy alone. The source code, checkpoints, and data are released at https://github.com/Awesome4D/4DMLLM_
Hallucination_Bench.
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All authors Runyi Yang, Murat Akkoyun, Di Wen, Ruiping Liu, Yufan Chen, Junwei Zheng, Xiaoye Wang, Kailun Yang, Danda Pani Paudel, Luc Van Gool, Kunyu Peng
Artificial Intelligence · Cross-listed from cs.CR
ActGov: Governing LLM Agent Actions via Policy-Constrained Validation
Kaiyuan Zhang, Yuke Peng, Ke Jiang et al. (+1 more)
Large
language model (LLM)
agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence subsequent actions and exceed user authorization. Existing defenses isolate injected content or constrain execution with predefined plans and static policies, but these approaches are brittle under dynamic workflows and scale poorly across extensible tool ecosystems. In this work, we present ActGov, a runtime enforcement framework that validates each LLM-proposed tool action before it causes external effects. Built on a unified semantic model of authorization, actions, runtime context, and security constraints, the ActGov-Policy component iteratively constructs a policy set from tool specifications, benign tasks, and observed failure traces, with each update verified through SMT-based counterexample checking. At runtime, ActGov-Runtime abstracts each tool call into finite policy records and permits it only if it remains within the task-scoped authorization boundary and satisfies all applicable policies. This per-action enforcement preserves authorization throughout long-horizon, dynamically branching workflows. We evaluate ActGov on the AgentDojo and AgentDyn
benchmarks across multiple models and attack configurations. It shows that ActGov consistently reduces the success rate of indirect
prompt-injection attacks while preserving task utility, significantly outperforming existing defenses. These results demonstrate that ActGov can enforce fine-grained authorization over dynamic
agent executions without relying on the underlying LLM to correctly identify malicious instructions.
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All authors Kaiyuan Zhang, Yuke Peng, Ke Jiang, Yinqian Zhang
Artificial Intelligence · Cross-listed from cs.LG
WPBench: A Comprehensive Benchmark for Wind Power Forecasting
Yuhan Zhu, Jilin Hu, Xinying Cai et al. (+7 more)
Accurate, reliable, and deployable wind power
forecasting is critical for power system dispatch, renewable energy integration, and electricity
market operations. Progress in this field hinges on the ability to empirically and comprehensively
benchmark forecasting methods. Yet existing
benchmarks fall short of supporting systematic evaluation in four key aspects: 1) limited coverage of wind power scenarios across turbine scale, variable composition, and spatial structure; 2) incomplete coverage of
forecasting model families; 3) evaluation metrics misaligned with wind power requirements; and 4) limited structure-aware diagnostics beyond individual temporal patterns. To address these limitations, we propose WPBench, a comprehensive, fair, and extensible
benchmark for wind power
forecasting. WPBench integrates 26 public
datasets organized by turbine scale and variable composition, spanning single-turbine, multi-turbine, univariate, and multivariate settings. Under unified processing, training, and evaluation protocols, it
benchmarks 19 representative models covering traditional methods, deep temporal models, spatio-temporal models, and foundation models. Beyond point-wise errors, WPBench assesses forecast-curve fidelity and computational efficiency, and delivers structure-aware diagnostics across temporal, variable-dependency, and spatial-dependency perspectives. Together, these capabilities enable systematic model comparison across diverse wind scenarios and provide a reusable platform for future research.
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All authors Yuhan Zhu, Jilin Hu, Xinying Cai, Yingshan Li, Li Ma, Xiangfei Qiu Linsen Li, Kai Zhang, Yao Fu, Weihao Jiang, Bin Yang
Artificial Intelligence · Cross-listed from cs.RO
FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding
Hung T. Ho, Khanh D. Nguyen, Quang D. Nguyen et al. (+5 more)
Low-bit
vision-
language-action inference must reduce observation-to-action latency while preserving
robot behavior. We present FoldQuantVLA, a post-training quantization framework that carries a consistent activation representation through calibration, weight rounding, and native integer execution. It combines channel scaling and block Hadamard transforms with dynamic per-
token quantization, without policy retraining. Custom TensorRT plugins execute projections in both the
language backbone and iterative action expert with four-bit weights and activations (W4A4) on Ada GPUs and Jetson AGX Orin. Evaluation spans LIBERO, SimplerEnv, and two
robot platforms. Across three GR00T checkpoints and $\pi_{0.5}$, W4A4 achieves $1.20$ to $1.33\times$ speedups over floating-point TensorRT on Orin and $1.25$ to $1.52\times$ on desktop. Retaining
language attention-output and feed-forward down projections at eight bits (W8A8) improves held-out action fidelity on all four checkpoints. Across four real-
robot tasks, this configuration raises observed GR00T N1.7 success from $80.0\%$ with uniform W4A4 to $92.5\%$ over 80 trials per configuration, with a measured additional Orin latency of 1 ms.
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All authors Hung T. Ho, Khanh D. Nguyen, Quang D. Nguyen, Thanh Q. Duong, Ngan Le, Meng Guo, Vien A. Ngo, An T. Le
Artificial Intelligence · Cross-listed from cs.CV
Estimating Accurate Hand Pose in Camera Space with Vision Transformer
Kaiwen Ren, Yiran Jiang, Yongjing Ye et al. (+1 more)
Monocular RGB-based hand pose estimation has emerged as a critical research frontier in computer
vision. The local hand pose estimation methods predict hand poses relative to the wrist, while global hand pose estimation also requires estimating the wrist's position in the camera coordinate system. However, this camera-space estimation confronts two fundamental challenges: (1) depth ambiguity in
monocular settings, and (2) the coupling effect of hand local poses and global wrist positions in the perspective projections. In particular, this coupling reflects that the projections are jointly determined by local hand poses, wrist positions, and camera intrinsics. To overcome these challenges, our framework proposes two key innovations: Transformation-Isomorphism Supervision for hand-depth information extraction and Perspective Information
Embedding for resolving above coupling effect of local pose and wrist position, both integrated within the mainstream encoder-decoder architecture. Besides, we propose a novel framerate-aware multi-
dataset training strategy for sequential pose refinement. Our fully integrated approach achieves at most 37.1% superiority in CS-MJE over SOTA on HO3D. Project page: https://github.com/Mine268/CS-ViT.
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All authors Kaiwen Ren, Yiran Jiang, Yongjing Ye, Shihong Xia
Artificial Intelligence · Cross-listed from cs.LG
ARM: Attention with Routed-Memory for Learnable Sparse Control
Qiuhao Zeng, Jerry Huang, Peng Lu et al. (+7 more)
Despite advances in long-context inference, large
language models (LLMs) remain fundamentally limited by the key-value (KV) caching mechanisms that are necessary for stable computation. Techniques such as selective
token eviction and
pruning have vastly mitigated these issues, but often discard core information to manage the growing cache. In this paper, we propose
Attention with Routed
Memory (ARM) a novel KV caching structure that introduces a fully differentiable, fixed-size
memory system organized as a hierarchical router. Via a Gumbel-Softmax, ARM learns to select
memory slots and perform sigmoid-gated updates that softly combine new and stored information, avoiding hard eviction and reducing information loss. By further training a policy to dynamically select varying amounts of
memory at inference, ARM adapts its accesses for both simple contexts and inputs that require deeper
reasoning, enabling more scalable and effective
retrieval on both short- and long-contexts. Experimental results on standard commonsense and long-context
reasoning benchmarks demonstrate that ARM achieves superior performance and efficiency compared to fixed KV-caching approaches, while remaining efficient and scalable in terms of both
memory and generation latency.
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All authors Qiuhao Zeng, Jerry Huang, Peng Lu, Ruiyi Fang, Gezheng Xu, Zihao Jing, Yufei Cui, Charles Ling, Gang Niu, Boyu Wang
Artificial Intelligence · Cross-listed from cs.LG
Artificial Structure Function Search: Preserving Artificial Functional Connectivity for Structured Pruning
Mindula Illeperuma, Rafael Pina, Charuka Herath et al. (+2 more)
Structured
pruning is a model compression technique that is used to reduce the computational cost of deploying deep neural networks on resource-constrained devices. Popular methods of
pruning rely on opaque heuristics or weight-based criteria that give no indication as to the structural dependencies in the network. To address these limitations we present Artificial Structure Function Search (ASF-S): a novel structured
pruning framework. ASF-S utilizes Principle Gradient Importance (PGI): a novel prune-candidate selection criteria that is inspired by structure-function relationships in the
brain. By ensuring the pruned structure of the model respects topographical organization of the output layer, we define Artificial Functional Connectivity (AFC) for artificial neural networks. AFC provides evidence to demonstrate that accurate smaller networks can be found using careful prune candidate selection criteria. We present results for PGI as a selection criterion and for ASF-S as a
pruning framework against recent
benchmarks, demonstrating that our method yields model variants with 70% parameter reduction, that can recover baseline accuracy without re-training the pruned layers.
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All authors Mindula Illeperuma, Rafael Pina, Charuka Herath, Sharmarke A. Gabayre, Varuna De Silva
Artificial Intelligence · Cross-listed from cs.RO
Tactile-JEPA: Topology-Aware Self-Supervised Representation Learning for Distributed Tactile Sensors
Elizaveta Kovtun, Matvey Konovalov, Andrey Sakhovskiy et al. (+1 more)
Tactile sensing is an essential modality for
robots performing contact-rich, dexterous manipulation, particularly under visual occlusion. While pre-trained image encoders are standard in
robot learning pipelines, tactile encoders are still commonly trained from scratch from raw, noisy signals, which might limit their expressivity. Existing self-supervised learning (SSL) approaches focus predominantly on
vision-based tactile sensors, leaving distributed electronic skins largely unaddressed. These sensors, however, have a distinctive property: their sensing elements are sparse and irregularly arranged over the surface they cover, which makes direct reuse of visual SSL methods suboptimal. We present Tactile-JEPA, an efficient self-supervised pre-training method that uses the spatial arrangement of tactile sensors to learn
topology-aware representations. Specifically, it is trained to predict the
embeddings of masked sensing elements from the unmasked remainder, using the sensor connectivity graph to guide spatial masking. Our analysis shows that effective tactile representations require capturing both local contact details and the global state of the tactile surface, which we achieve through dual-scale masking. Across three diverse
datasets spanning
magnetic and piezoresistive sensors, different
robot embodiments, and single- and paired-sensor configurations, Tactile-JEPA reduces force estimation error by 6.3% and in-hand orientation error by 20.8% over the prior state-of-the-art, with consistent gains in other downstream applications, including policy learning. Overall, our results demonstrate that the benefit of tactile sensing depends critically on the quality of encoder pre-training, a problem which Tactile-JEPA addresses directly. Code is available at https://github.com/E-Kovtun/tactile.
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All authors Elizaveta Kovtun, Matvey Konovalov, Andrey Sakhovskiy, Semen Budennyy
Artificial Intelligence · Cross-listed from cs.LG
Information-Time Proximal Policy Optimization
Yongcheng Zeng, Xinyu Cui, Yan Song et al. (+9 more)
RLVR has substantially improved the
reasoning capabilities of LLMs. However, existing methods typically parameterize temporal progression in the Markov Decision Process by
token-by-
token generation, despite the highly non-uniform information flow along autoregressive trajectories. In this paper, we propose InfoPPO, which reparameterizes temporal progression using information density rather than raw
token count. This reparameterization induces a common state-dependent structure for both temporal credit propagation and policy updates. InfoPPO restores the effectiveness of non-trivial discounting in long-horizon
reasoning, retaining effective-horizon contraction while avoiding excessive attenuation of terminal supervision over long
token sequences. Moreover, the information-time policy-improvement analysis naturally leads to a state-dependent update constraint, which we implement through adaptive clipping. By adapting the clipping threshold at each
token position to the information density of its corresponding state, this mechanism enables more targeted policy updates while preserving proximal control. Theoretically, we extend performance-difference and policy-improvement analyses to the information-time MDP, deriving a policy-improvement lower bound when policy changes are regulated by information density. We further connect the general information-time analysis to practical LLM policy optimization by relating state-wise information density to local policy movement, while also providing theoretical grounding for the adaptive update mechanism. Experiments on Qwen3 models demonstrate consistent gains over competitive baselines across five challenging competition-style mathematical
reasoning benchmarks. InfoPPO also maintains stable accuracy and response length across non-trivial discount settings under which
token-time PPO deteriorates.
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All authors Yongcheng Zeng, Xinyu Cui, Yan Song, Guoqing Liu, Hongsheng Xin, Kaike Zhang, Cheng Deng, Kun Zhan, Jian Ying, Jian Zhao, Haifeng Zhang, Jun Wang
Artificial Intelligence · Cross-listed from cs.CL
URA-NER: A Unified Retrieval-Augmented Framework with Retrieval Alignment and Uncertainty Reduction for Low-Resource NER
Jingyu Wang, Shijie Wu and Fusheng Jin
In-context learning (ICL) based on large
language models (LLMs) has shown promising potential in alleviating performance bottlenecks caused by the limited availability of annotated data in Named Entity Recognition (NER). However, existing methods still face issues of
retrieval misalignment and generation uncertainty, making their performance heavily dependent on the LLM's capabilities. As the parameter scale of LLMs decreases, their performance in few-shot settings deteriorates significantly. In this paper, we propose a novel unified
retrieval-augmented framework, URA-NER, including three key components: Progressive Granularity
Retrieval (PGR), Model-aware Representation Enhancement (MaRE), and Reason-aware Knowledge Verification. PGR is a two-stage
retrieval mechanism that achieves stage
alignment. It first retrieves demonstrations for span detection based on the query's global semantics, and then for type classification based on the specific entity context, providing fine-grained local information. Moreover, MaRE employs entity pre-recognition to guide the construction of representations, ensuring the query and demonstrations are aligned within the LLM's semantic space and
attention pattern. In addition, to mitigate generation uncertainty, we propose RaKV, a closed-loop "generation-
retrieval-verification" process. It explicates the LLM's
reasoning paths, leverages them for the
retrieval of external knowledge, and reorganizes the knowledge into verification evidence aligned with the original
reasoning paths. We conduct extensive experiments on multiple low-resource NER
datasets. Results demonstrate that URA-NER significantly enhances the performance of LLMs under low-resource settings, with particularly pronounced gains for smaller LLMs, achieving new state-of-the-art results on several
benchmarks.
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Artificial Intelligence · Cross-listed from cs.HC
DeceptionAnalyser: A Web-Based AI Tool for Performing Structured Deception Analysis with Argumentation Schemes and LLMs
Stefan Sarkadi, Xabier Garmendia, Jack Mumford et al. (+1 more)
Deception plays a central role in Intelligence operations, yet it remains difficult to analyse systematically without expert knowledge of
reasoning patterns and cognitive manipulation. In computational argumentation, for instance, no scheme-level ground-truth corpora currently exist to support statistical validation. In this paper, we address this by introducing a set of ten argument schemes designed to model distinct forms of deception, each accompanied by structured premises and critical questions. In doing so, we introduce the first dedicated library of argumentation schemes specifically designed for deception analysis, providing a structured foundation for systematically modelling and analysing deception in narrative text. We then present DeceptionAnalyser, a browser-based tool that implements these schemes through a two-stage methodology combining LLM-based premise extraction with critical-question-driven evaluation. Our aim is to provide a conceptual and methodological foundation for analysing deceptive
reasoning in narrative text. This is precisely what we address in this paper by demonstrating how structured argumentation theory and AI-assisted analysis can support transparent, explainable assessments of potential deception. Because the schemes are designed to flag claims for scrutiny rather than to output a deception verdict, we do not
benchmark classification accuracy; instead, we assess the reliability of the methodology by measuring the consistency of the tool's premise and conclusion assessments across ten contemporary large
language models and repeated runs. We find that scheme detection is highly stable for clear-cut deception and degrades gracefully, in interpretable ways, on more ambiguous intelligence-style narratives.
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All authors Stefan Sarkadi, Xabier Garmendia, Jack Mumford, Trevor Bench-Capon
Artificial Intelligence
VLM-in-Sandbox: Visual Workspaces for Agentic Visual Reasoning
Hexiong Yang, Mingrui Chen, Jie Cao et al. (+1 more)
Sandboxed computer environments support multi-step
reasoning with tools, executable programs, and persistent files, yet their extension from
language models to
vision-
language models (VLMs) introduces a distinct state-management problem. Visual
reasoning produces intermediate image-valued evidence—crops, masks, overlays, zoomed regions, and analytic renderings—that must remain addressable without accumulating unboundedly in
multimodal context. We introduce VLM-in-Sandbox, a training-free framework for agentic
multimodal reasoning in controlled computer environments. Its Visual Workspace registers generated artifacts in an image ledger, maintains a bounded active visual context, and lets the model explicitly promote selected evidence for subsequent inspection. This separates visual evidence generation, performed by sandbox tools, from visual evidence management. Across seven
benchmarks and four base VLMs, VLM-in-Sandbox achieves the highest sample-weighted average accuracy among Vanilla VLM, Append-only Sandbox, and the proposed method. A compiler-matched $2\times2$ study on 1,260 examples further separates model-directed visibility from bounded retention: VLM-in-Sandbox reaches 66.27% accuracy with 18.6% fewer total
tokens than the automatic, retain-all control. Over all 6,350 submitted GPT-4.1-mini examples, it produces 302 rescues and 142 regressions relative to Original Append-only. A local vLLM study with prefix caching confirms that the smaller request workload also reduces uncached
tokens, time to first
token, and end-to-end latency. These results identify explicit visual evidence state as a central abstraction for sandboxed VLM
agents.
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All authors Hexiong Yang, Mingrui Chen, Jie Cao, Ran He
Artificial Intelligence · Cross-listed from cs.CV
Dissecting Agentic Forensics: The Role of Triage, Prompting, and Evidence Arbitration in Open-World Fake Image Detection
Xianlong Li, Pietro Bongini, Niccoló Pancino et al. (+3 more)
Image forensics is increasingly an open-world problem: manipulations range from fully synthetic images to localized edits, splicing and swapping, while most forensic detectors remain specialized to a single manipulation family. Agentic AI has recently emerged as a promising solution. In principle, such systems can assess the reliability of individual detectors, identify out-of-scope evidence, and arbitrate conflicting reports. However, it remains unclear which components actually drive performance and whether their benefits persist under distribution shift. To answer these questions, we study a training-free agentic framework built around specialist detectors, per-detector triage, and conflict-aware evidence arbitration. Using six configurations and three
multimodal large
language model backbones, we dissect the role of triage, prompting, and
reasoning quality on both in-distribution and out-of-distribution data. Our results show that naive detector fusion suffers from severe false-positive rates on authentic images. Triage and prompting consistently improve performance by filtering unreliable evidence and exposing detector limitations. However, the dominant factor is represented by
reasoning itself: A stronger judge substantially outperforms a weaker one, particularly under distribution shift. Most notably, manipulation recall is nearly saturated across all configurations, indicating that the main challenge of open-world image forensics is not detecting manipulations, but calibrating trust in specialized forensic tools and arbitrating conflicting evidence.
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All authors Xianlong Li, Pietro Bongini, Niccoló Pancino, Marco Blanchini, Benedetta Tondi, Mauro Barni
Artificial Intelligence · Cross-listed from cs.CL
Mitigating Entity Type Confusion in Cross-Domain NER via Multidimensional Quantification and Reasoning Enhancement
Jingyu Wang, Shijie Wu and Fusheng Jin
Cross-domain Named Entity Recognition (CD-NER) aims to transfer the rich knowledge in the source domain to the target domain. Recent studies adopting decomposition or generation paradigms have achieved significant performance improvements, demonstrating high accuracy in entity span detection. However, during entity type classification, models severely suffer from entity type confusion, the erroneous tendency that models classify entities of one type in the text as another similar but incorrect type. To address this issue, we first propose a Multidimensional Confusion Quantification Model (MCQM) that quantifies a model's confusion extent between entity types from three dimensions: source-target hierarchy analysis, semantic similarity analysis, and explicit data evaluation. Moreover, we propose the Progressive Bidirectional
Reasoning Chain (PBRC). PBRC leverages the source-target hierarchy and confusion analysis from the MCQM to
prompt the LLM to generate two-stage
reasoning information. The two-stage
reasoning information is utilized to augment the knowledge of the model, significantly mitigating entity type confusion and improving the model's generalization performance. Experimental results demonstrate that our method achieves new state-of-the-art results on all domains of the CrossNER
dataset.
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Artificial Intelligence
Few-Shot Demonstrations Elicit the Use of In-Context World Representations in LLMs
Kohsei Matsutani, Gouki Minegishi, Core Francisco Park et al. (+3 more)
Large
language models (LLMs), when acting as
agents, are expected to take observed data in context, infer the latent state space underlying the world, and leverage it for downstream prediction. However, prior work demonstrated that LLMs struggle to use representations learned in context on a graph tracking task, where the model needs to construct a representation of the graph governing data generation process and use it for subsequent predictions. In this paper, we show that extending this to few-shot settings, where each demonstration is generated from a different world with either the same or different graph
topologies, enhances its prediction on 6 models from 4 model families. To understand this improvement, we linearly probe a low-dimensional world representation that encodes graph information in the hidden states. Notably, we find that few-shot demonstrations relocate the world representation and increase its predictive use. Specifically, for each model, these world representations shift in directions nearly orthogonal to their original subspace, and interventions on these representations selectively impair performance more than interventions on other subspaces. Consistent with this insight, we show that few-shot demonstrations with observations from different worlds improve performance on ARC-AGI-1&2, web
agent tasks, and Othello. Our findings elucidate the role and internal mechanisms of few-shot demonstrations in in-context world modeling. More broadly, our work advances our understanding of how LLM
agents learn from in-context observations and provides implications for their further improvement.
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All authors Kohsei Matsutani, Gouki Minegishi, Core Francisco Park, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo
Artificial Intelligence · Cross-listed from cs.SE
A Lean and Spec-Driven AI-Assisted Software Development Lifecycle for Applied AI Education: The AI-SDLC Approach
Andreas Martin and Sandro Schwander
AI coding
agents increasingly support software development beyond code completion, including planning, implementation, testing, and repository-level task execution. Their practical use, however, often remains only weakly connected to established software engineering practices. The aim of this work is to develop and evaluate a lightweight, spec-driven lifecycle for governed agentic software engineering. The lifecycle combines established software engineering practices with repository-local guidance through specifications,
AGENTS.md, and phase-specific
agent skill files. The approach was developed in the context of the FHNW course AI-assisted Software Development and applied by students to business-oriented software use cases. Its educational and practical applicability is explored through a student survey combining closed rating items with open-ended questions. The contribution of this work is a process-oriented framework that enables AI coding
agents to operate with bounded autonomy within an explicit, reviewable, and test-oriented software development lifecycle.
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Artificial Intelligence
LADDER: Graph-Guided Diffusion Language Models for Efficient Multi-Hop Reasoning
Senlei Zhang, Linhao Luo, Qian-Wen Zhang et al. (+4 more)
Graph
Retrieval-Augmented Generation (GraphRAG) has remarkably enhanced large
language models on complex
reasoning by leveraging structured entity
topologies. However, existing frameworks heavily rely on standard autoregressive
language models where the nature of inherent sequential generation severely hinders overall inference efficiency. Inspired by
Diffusion Language Models (DLMs) that offer massive parallelism via continuous refine-in-parallel decoding, we aim to accelerate GraphRAG in the discrete space. However, it remains non-trivial for two challenges. First, partially denoised drafts are highly dynamic and uncertain, making dynamic graph grounding non-trivial. Second, raw denoising states are inherently noisy and unstable, making synchronous graph
retrieval and multi-hop aggregation computationally prohibitive. To this end, we present LADDER, a novel framework that bridges
diffusion language modeling with GraphRAG through graph-guided parallel decoding. Specifically, (i) we propose an event-driven self-clocking
retrieval, inspired by our key insight that 88% of target entities emerge early in the partially denoised state, leading final commitment by an average of 5.7-9.6 steps. This mechanism dynamically triggers graph
retrieval only when the set of graph-linkable entities expands, yielding an asynchronous self-clocking policy that bypasses learned gates or heuristic thresholds. (ii) An incomplete-query graph propagation module is designed to process the newly emerging entity queries using a specialized graph foundation model, continuously aggregating multi-hop evidence to sharpen parallel predictions and accelerate overall decoding convergence. Extensive experiments on three challenging multi-hop QA
benchmarks show that LADDER raises average exact match from 39.6% to 45.2% while achieving a 4.1x latency reduction.
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All authors Senlei Zhang, Linhao Luo, Qian-Wen Zhang, Siyu An, Junnan Dong, Shuhao Zhang, Xing Sun
Artificial Intelligence
Brain-Token Learning: Microstate-Based Tokenization and Multi-Scale Interaction for Long-Horizon EEG Sequence Modeling
Weishan Ye, Yue Pan, Li Zhang et al. (+2 more)
Electroencephalography (
EEG) provides a non-invasive window into dynamic
brain activity, yet modeling long-horizon
EEG sequences remains challenging due to their high temporal complexity, substantial variability across subjects, and the lack of biologically meaningful sequence representations. Existing tokenization strategies, such as fixed-window and patch-based representations, discretize
EEG signals according to artificial temporal boundaries, which may disrupt intrinsic
brain-state dynamics. In this work, we propose
Brain-
Token Learning, a neuroscience-inspired framework that introduces
Brain Tokenization for long-horizon
EEG sequence modeling. Instead of partitioning
EEG signals into predefined temporal segments,
Brain Tokenization represents
EEG as sequences of recurrent microstate-derived
brain tokens, where each
token corresponds to a quasi-stable large-scale
brain state with variable temporal duration. Based on these biologically grounded
tokens, we further develop a multi-scale
token interaction module consisting of Latent State Aggregation and State Transition Modeling to jointly capture global
brain-state context and local microstate transitions. We evaluate
Brain-
Token on five heterogeneous
EEG datasets, including the newly collected long-horizon NeuroLong
dataset and four affective or clinical
EEG datasets (SEED, DEAP, MDD, and NSSI). Extensive experiments demonstrate that
Brain-
Token consistently outperforms conventional CNN/LSTM architectures,
Transformer-based models, and domain adaptation methods across diverse
EEG scenarios. Further analysis verifies the effectiveness of microstate-based tokenization and multi-scale interaction for learning robust and interpretable
EEG representations. These results establish
Brain-
Token as a biologically grounded tokenization paradigm for long-horizon
EEG sequence modeling.
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All authors Weishan Ye, Yue Pan, Li Zhang, Gan Huang, Zhen Liang
Artificial Intelligence · Cross-listed from cs.LG
The Undetected Damage of Quantization on Retrieval and How to Fix It
Luca Zhou, Alessandro Zirilli, Daniele Solombrino et al. (+2 more)
We show that a quantized model that keeps its classification accuracy still changes $14$ to $46\%$ of its top-1
retrieval results, and that aggregate ranking metrics reveal only part of this damage. We tie this failure to the gap between the two highest model scores and use that gap to decide when to trust a quantized answer and where additional precision should be spent. We show that the top-1 result is guaranteed to survive quantization only when this gap exceeds twice the largest rounding error. In classification, the scores are logits, and training compares the correct class against every other class, which encourages this gap. In
retrieval, the scores are query-document similarities, and training compares each positive only against sampled negatives, so nothing separates the top-1 item from the second. This gap can be measured without labels. Before deployment, it predicts which models will break under quantization, and at deployment time it tells, per input, whether the quantized answer still matches the full-precision answer. Most classification inputs have a gap wide enough to trust the quantized answer, but few
retrieval queries do. That gap motivates a different fix in each task. In
retrieval, spending extra bit-width on the layers whose quantization moves the gap most recovers up to three-quarters of an extra bit's benefit for half its cost. In classification, routing the few low-gap inputs to full precision recovers most of the lost accuracy at a fraction of the cost.
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All authors Luca Zhou, Alessandro Zirilli, Daniele Solombrino, Roberto Dessì, Emanuele Rodolà
Artificial Intelligence · Cross-listed from q-bio.BM
Adapting Boltz-2 with limited experimental activity data improves early enrichment in virtual screening
Kairi Furui and Masahito Ohue
Virtual screening aims to prioritize active compounds from large chemical libraries within a limited experimental budget. When applying Boltz-2 to virtual screening, a key challenge is how to use limited experimental data from the target assay to improve the prioritization of active compounds. We investigated whether fine-tuning the Boltz-2 affinity heads with a small number of binary activity labels could improve early enrichment of active compounds in hit discovery. We compared fine-tuning with 40-300 labels in a retrospective evaluation on eight MF-PCBA targets. With 300 activity measurements, fine-tuning increased the number of actives in the top 1% by a
geometric mean of 1.77-fold across the eight targets and improved average precision (AP) by 2.14-fold relative to the control without fine-tuning. We also investigated whether rescoring a subset of candidates could retain the improvement in hit recovery by reranking only the top-ranked Boltz-2 candidates with the fine-tuned head. Restricting rescoring to approximately 10% of the evaluation set retained hit recovery comparable to full rescoring. These findings show that affinity-head fine-tuning with limited activity labels improves early enrichment with Boltz-2 and that this benefit can be retained when rescoring a restricted set of candidates.
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Artificial Intelligence · Cross-listed from cs.LG
KV-COBRA: KV Cache Compression via Co-Optimized Bit-Rank Allocation
Sihyeon Ha, Jaeho Lee and Yo-Seb Jeon
What limits KV-cache compression at extreme bit-rates? We argue that it is not the choice of compression scheme, but how its budget is allocated across
attention heads. Existing methods apply rank and bit-width uniformly, ignoring that each head has a different optimal mix of rank truncation and quantization. We show that co-optimizing rank and bit-width per head, using only standard low-rank projection and scalar quantization, dominates uniform allocation, with the largest gains at low bit-rates. Our method, KV-COBRA (Co-Optimized Bit-Rank Allocation), formalizes this as a resource-allocation problem: it balances rank-truncation loss against quantization loss within each head, then redistributes budget across heads to minimize total distortion. A fused Hadamard rotation equalizes per-channel variance, and reordering the SVD basis by
attention-KL importance makes the solver query-aware. The same allocator extends to joint $K{+}V$ compression. On perplexity, zero-shot, and long-context
benchmarks from $0.5$ to $4$ bits per dimension (bpd), KV-COBRA shows the smallest accuracy degradation among evaluated methods at low bpd, with no per-
token overhead.
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Artificial Intelligence
When and How Should an Agent Clarify? CIGAsk: Teaching LLMs to Clarify via Counterfactual Information Gain
Yunxiang Li, Xixin Wu and Helen Meng
Instruction-tuned LLMs faced with underspecified queries often commit to a single interpretation rather than ask for clarification, producing confidently wrong answers. In our experiments, prompting alone is insufficient: models either ask for clarification on every query or ask vague questions that fail to recover the missing information. Addressing this failure requires learning two coupled skills: when to ask rather than answer and how to ask a question that recovers the disambiguating information. Existing recipes either address only one of these skills or require a separately trained critic. We propose CIGAsk, an RL recipe that teaches both skills through two complementary reward signals within a multi-turn GRPO loop. Counterfactual Information Gain (CIG) compares the gold-answer log-likelihood under a frozen reference model with and without the user response, providing per-turn credit that guides how to ask. The Asymmetric Ambiguity Bonus assigns a signed reward at the terminal
token based on the gold ambiguity label, guiding when to ask. Across three clarification
benchmarks spanning table, passage, and open-domain QA, CIGAsk-7B outperforms the strongest external baseline despite using a smaller backbone. It also transfers across
datasets without per-
dataset tuning while preserving single-turn QA performance on out-of-distribution
benchmarks.
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Artificial Intelligence · Cross-listed from cs.LG
TTSE: A Two-Track Online Self-Evolution Framework
Ruimin Pei, Yongkang Wu, Shangyi Zheng et al. (+5 more)
As Large
Language Model (LLM)
agents are applied in continuously interactive environments, driving the
evolution of their own capabilities becomes a core problem for achieving long-term autonomy. Currently, environmental knowledge is typically treated as an external fixed input rather than as part of the
agent's ongoing
evolution. Reinforcement learning methods usually optimize policies through environmental interaction but tend to adapt only to fixed task distributions or single environments. This paper proposes TTSE (Two-Track Self-
Evolution), a dual-track online self-
evolution framework that separates evolving knowledge into FACT (environmental facts, whose reliability is continuously verified through interaction evidence) and TIP (task-conditioned implementation procedures). From a decision-theoretic perspective, we decompose the
agent's excess risk into environment-representation regret and conditional-execution regret, characterize the conditions under which environment-conditioned policies strictly outperform condition-agnostic policies, and bound the downstream risk in terms of FACT identification error and cross-condition mismatch cost. In practice, TTSE's ablation experiments on GDPevo validate the advantage of dual-track
evolution. On the classic
agent task
benchmarks ALFWorld and ScienceWorld, TTSE further demonstrates superior task adaptation. Moreover, TTSE is broadly compatible with existing skill self-
evolution methods; combined with the
Bayesian-
Agent algorithm, a single-track ablation validates the dual-track advantage, substantially improving the aggregate score across the five major domains of SOPBench over three independent repetitions. Finally, on the real end-to-end task
benchmark PinchBench, TTSE is integrated into a general
agent framework via
retrieval-based injection and stably outperforms the baseline across three independent runs.
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All authors Ruimin Pei, Yongkang Wu, Shangyi Zheng, Yaqing Zhang, Deyang Li, Jianjun Tao, Xinyu Zhang, Xiang Zhang
Artificial Intelligence · Cross-listed from cs.CR
Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detection
Md. Asif Sajeed, Md. Nazrul Islam Mondal and Md Ashraful Hossen Akash
Malware detection is a critical task in cybersecurity, and graph neural networks over control flow graphs have shown promising results for it. However, detectors are usually evaluated on a random split of a corpus collected over a single period, which cannot show how well a model generalizes to later samples. This study addresses that limitation with a strict temporal split: every model is trained on one period and scored once on a later one. Two corpora of control flow graphs, each node carrying 37 features, were extracted statically from 1,989 Windows portable executables: 459 graphs from 2024-2025 for training and 223 from 2026 for evaluation. Twelve variants and a flat-feature control were trained on the earlier corpus. The choice of message-passing operator changes robustness to the shift significantly, and every pairwise gap that survives correction separates an aggregating architecture from one built around a learned attentional readout. The ranking also reverses: the flat control, which sees node features but no
topology, is the best in-distribution model and among the worst across the boundary, so a conventional
benchmark would have rejected message passing. Neither recalibration nor ensembling substitutes for the operator choice. Attributions do not shift, but explanation validity is architecture-specific, and the most accurate operator on the later corpus is the hardest to explain. An architecture derived from the finding matches the best searched operator without search. The shift affects both malware and benign classes alike, so these are results about robustness to distribution shift, not malware
evolution.
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Artificial Intelligence
How Many Pixels Is a Digit Worth? Place-Aware Coordinate Entropy for GUI Agent Confidence Estimation
Yunxiang Li, Xixin Wu and Helen Meng
GUI
agents predict click coordinates as digit-
token sequences, but standard text-LLM confidence estimation methods rank correct clicks from wrong ones only weakly. GUI-specific alternatives use K samples or new supervision, but still leave room for improvement. We trace part of this to place-value asymmetry: bounding-box correctness often makes higher-place digits more important than lower-place digits, so uniform aggregation weakens the signal that determines correctness. The fix is to weight each digit's Shannon
entropy by its place value. We call this Place-Aware Coordinate
Entropy (PACE). Across fixed-scale
agents on ScreenSpot-Pro and ScreenSpot-v2, PACE wins both AUROC and selective accuracy on all primary comparisons in a single forward pass, matching or outperforming K-sample baselines at a fraction of the cost. PACE provides a per-click confidence estimate that turns coordinate-
token internals into a practical confidence signal for GUI
agent deployment.
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Artificial Intelligence · Cross-listed from cs.RO
vla.simd: Efficient CPU Inference for Language-Conditioned Manipulation
Khanh D. Nguyen, Hoang M. Truong and An T. Le
Deploying
language-conditioned manipulation without a dedicated GPU requires efficient inference and action chunks that cover the delay between policy queries. We present vla.simd, a CPU inference engine that combines shared SIMD micro-kernels, reusable computation, and target-specific optimization. We relate query latency and execution horizon to action availability under lagged and time-aligned execution, distinguishing action supply from feedback frequency. Across six policies and four CPUs, vla.simd achieves approximately $1.4\times$ median speedup over compiled PyTorch references while preserving fp32 numerical fidelity. We also introduce IMPACT, an ACT-based policy with cached text representations and
language-modulated visual features. IMPACT is the only
language-conditioned policy in our evaluated set that supplies at least 30 actions/s on the Raspberry Pi 5: after a 90 s thermal soak, it supplies 33.5 actions/s in fp32 and 81.2 with int8. Separate GPU evaluations yield $76.4\%$ mean success across four LIBERO suites without
robot pretraining; instruction-shuffling tests demonstrate selection among familiar goals. Trials with IMPACT on an SO-101 arm and SmolVLA on a UR10e with a Robotiq gripper demonstrate CPU deployment on two
robot embodiments.
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Artificial Intelligence
Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Prior
Hugues Roy, Reuben Dorent and Ninon Burgos
Unsupervised anomaly detection (UAD) aims to localize abnormal regions in medical scans without pixel-level annotations. A typical strategy seeks to reconstruct a pseudo-healthy image that preserves subject-specific anatomy. Recently,
diffusion models have been proposed to perform UAD. However, these methods rely on heuristic noise schedules or synthetic corruptions to balance subject-specificity and anomaly removal. In this work, we propose an alternative formulation of UAD as a
Bayesian inverse problem under a
diffusion prior. First, we introduce a latent spatial anomaly mask that models pixel-wise consistency between a test image and its latent corresponding pseudo-healthy image. Then, we propose an approximation of the unknown generation process that links healthy anatomy, anomalies, and the observed image, enabling a well-defined likelihood within the
Bayesian framework. Building on recent advances in
diffusion-based inverse problem methods, we jointly infer the pseudo-healthy image and the anomaly mask via annealed posterior sampling. We evaluate our approach on FDG PET (ADNI) and FLAIR MRI (BraTS 2021), demonstrating improved anomaly localization performance compared to other
diffusion-based approaches and validating the contribution of our introduced model. Our code is available at https://github.com/HuguesRoy/UAD_DAPS.
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