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전체 전문분석자료
arXiv:2603.10359v1 Announce Type: new Abstract: Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitation of rejection sampling. …
arXiv:2603.10677v1 Announce Type: new Abstract: Clinical diagnosis is a complex cognitive process, grounded in dynamic cue acquisition and continuous expertise accumulation. Yet most current artificial …
arXiv:2603.09996v1 Announce Type: cross Abstract: The integration of large language models (LLMs) into educational processes introduces significant constraints regarding data privacy and reliability, particularly in …
arXiv:2603.09978v1 Announce Type: cross Abstract: Large language models have recently surpassed specialized systems on code generation, yet their effectiveness on other code-analysis tasks remains less …
arXiv:2603.10396v1 Announce Type: new Abstract: Despite the growing demand for eliciting uncertainty from large language models (LLMs), empirical evidence suggests that LLM behavior is not …
arXiv:2603.10512v1 Announce Type: new Abstract: Artificial intelligence has advanced significantly through the development of intelligent game-playing systems, providing rigorous testbeds for decision-making, strategic planning, and …
arXiv:2603.09987v1 Announce Type: cross Abstract: Feature Transformation (FT) is a core data-centric AI task that improves feature space quality to advance downstream predictive performance. However, …
arXiv:2603.10384v1 Announce Type: new Abstract: Evaluating LLM reliability via scalar probabilities often fails to capture the structural dynamics of reasoning. We introduce TRACED, a framework …
arXiv:2603.10002v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly tasked with producing and manipulating structured artifacts. We consider the task of end-to-end spreadsheet …
arXiv:2603.10661v1 Announce Type: new Abstract: We propose FAME (Formal Abstract Minimal Explanations), a new class of abductive explanations grounded in abstract interpretation. FAME is the …
arXiv:2603.10588v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has achieved remarkable success in logical reasoning tasks, yet whether large language model (LLM) …
arXiv:2603.09983v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models enable scalable performance but face severe memory constraints on edge devices. Existing offloading strategies struggle with I/O …