arXiv:2603.16806v2 Announce Type: replace-cross Abstract: To meet the demands of increasingly diverse dexterous hand hardware, it is crucial to develop a policy that enables zero-shot cross-embodiment grasping without redundant re-learning. Cross-embodiment alignment is challenging due to heterogeneous hand kinematics and physical constraints. Existing approaches typically predict intermediate motion targets and retarget them to each embodiment, […]
Towards Safer Large Reasoning Models by Promoting Safety Decision-Making before Chain-of-Thought Generation
arXiv:2603.17368v1 Announce Type: new Abstract: Large reasoning models (LRMs) achieved remarkable performance via chain-of-thought (CoT), but recent studies showed that such enhanced reasoning capabilities are at the expense of significantly degraded safety capabilities. In this paper, we reveal that LRMs’ safety degradation occurs only after CoT is enabled, and this degradation is not observed when […]
A Hierarchical Error-Corrective Graph Framework for Autonomous Agents with LLM-Based Action Generation
arXiv:2603.08388v2 Announce Type: replace Abstract: We propose a Hierarchical Error-Corrective Graph FrameworkforAutonomousAgentswithLLM-BasedActionGeneration(HECG),whichincorporates three core innovations: (1) Multi-Dimensional Transferable Strategy (MDTS): by integrating task quality metrics (Q), confidence/cost metrics (C), reward metrics (R), and LLM-based semantic reasoning scores (LLM-Score), MDTS achieves multi-dimensional alignment between quantitative performance and semantic context, enabling more precise selection of high-quality candidate […]
AdapTS: Lightweight Teacher-Student Approach for Multi-Class and Continual Visual Anomaly Detection
arXiv:2603.17530v1 Announce Type: cross Abstract: Visual Anomaly Detection (VAD) is crucial for industrial inspection, yet most existing methods are limited to single-category scenarios, failing to address the multi-class and continual learning demands of real-world environments. While Teacher-Student (TS) architectures are efficient, they remain unexplored for the Continual Setting. To bridge this gap, we propose AdapTS, […]
100x Cost & Latency Reduction: Performance Analysis of AI Query Approximation using Lightweight Proxy Models
arXiv:2603.15970v2 Announce Type: replace-cross Abstract: Several data warehouse and database providers have recently introduced extensions to SQL called AI Queries, enabling users to specify functions and conditions in SQL that are evaluated by LLMs, thereby broadening significantly the kinds of queries one can express over the combination of structured and unstructured data. LLMs offer remarkable […]
From Digital Twins to World Models:Opportunities, Challenges, and Applications for Mobile Edge General Intelligence
arXiv:2603.17420v1 Announce Type: new Abstract: The rapid evolution toward 6G and beyond communication systems is accelerating the convergence of digital twins and world models at the network edge. Traditional digital twins provide high-fidelity representations of physical systems and support monitoring, analysis, and offline optimization. However, in highly dynamic edge environments, they face limitations in autonomy, […]
Oracular Programming: A Modular Foundation for Building LLM-Enabled Software
arXiv:2502.05310v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can solve previously intractable tasks given only natural-language instructions and a few examples, but they remain difficult to steer precisely and lack a key capability for building reliable software at scale: the modular composition of computations under enforceable contracts. As a result, they are often embedded […]
Automated Grammar-based Algebraic Multigrid Design With Evolutionary Algorithms
arXiv:2603.17641v1 Announce Type: cross Abstract: Although multigrid is asymptotically optimal for solving many important partial differential equations, its efficiency relies heavily on the careful selection of the individual algorithmic components. In contrast to recent approaches that can optimize certain multigrid components using deep learning techniques, we adopt a complementary strategy, employing evolutionary algorithms to construct […]
Objective Mispricing Detection for Shortlisting Undervalued Football Players via Market Dynamics and News Signals
arXiv:2603.17687v1 Announce Type: cross Abstract: We present a practical, reproducible framework for identifying undervalued football players grounded in objective mispricing. Instead of relying on subjective expert labels, we estimate an expected market value from structured data (historical market dynamics, biographical and contract features, transfer history) and compare it to the observed valuation to define mispricing. […]
In-Context Compositional Q-Learning for Offline Reinforcement Learning
arXiv:2509.24067v2 Announce Type: replace-cross Abstract: Accurate estimation of the Q-function is a central challenge in offline reinforcement learning. However, existing approaches often rely on a shared global Q-function, which is inadequate for capturing the compositional structure of tasks that consist of diverse subtasks. We propose In-context Compositional Q-Learning (ICQL), an offline RL framework that formulates […]
OMNIFLOW: A Physics-Grounded Multimodal Agent for Generalized Scientific Reasoning
arXiv:2603.15797v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated exceptional logical reasoning capabilities but frequently struggle with the continuous spatiotemporal dynamics governed by Partial Differential Equations (PDEs), often resulting in non-physical hallucinations. Existing approaches typically resort to costly, domain-specific fine-tuning, which severely limits cross-domain generalization and interpretability. To bridge this gap, we propose […]
Proactive Knowledge Inquiry in Doctor-Patient Dialogue: Stateful Extraction, Belief Updating, and Path-Aware Action Planning
arXiv:2603.17425v1 Announce Type: new Abstract: Most automated electronic medical record (EMR) pipelines remain output-oriented: they transcribe, extract, and summarize after the consultation, but they do not explicitly model what is already known, what is still missing, which uncertainty matters most, or what question or recommendation should come next. We formulate doctor-patient dialogue as a proactive […]