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arXiv:2604.05485v1 Announce Type: new Abstract: LLM agents call tools, query databases, delegate tasks, and trigger external side effects. Once an agent system can act in …
arXiv:2604.05483v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown a high capability in answering questions on a diverse range of topics. However, these …
arXiv:2604.05468v1 Announce Type: new Abstract: Temporal knowledge graph (TKG) extrapolation is an important task that aims to predict future facts through historical interaction information within …
arXiv:2604.05465v1 Announce Type: new Abstract: Serverless computing eliminates infrastructure management overhead but introduces significant challenges regarding cold start latency and resource utilization. Traditional static resource …
arXiv:2604.05435v1 Announce Type: new Abstract: Incomplete or inconsistent discharge documentation is a primary driver of care fragmentation and avoidable readmissions. Despite its critical role in …
arXiv:2604.05424v1 Announce Type: new Abstract: PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection Siyuan Cheng, Bozhong Tian, Yanchao Hao, Zheng Wei Published: 06 Apr 2026, …
arXiv:2604.05416v1 Announce Type: new Abstract: Multi-Agent Pathfinding (MAPF) plays a critical role in various domains. Traditional MAPF methods typically assume unit edge costs and single-timestep …
arXiv:2604.05407v1 Announce Type: new Abstract: LLM-based code agents treat repositories as unstructured text, applying edits through brittle string matching that frequently fails due to formatting …
arXiv:2604.05400v1 Announce Type: new Abstract: Machine data is central to observability and diagnosis in modern computing systems, appearing in logs, metrics, telemetry traces, and configuration …
arXiv:2604.05396v1 Announce Type: new Abstract: Despite its success, existing in-context learning (ICL) relies on in-domain expert demonstrations, limiting its applicability when expert annotations are scarce. …
arXiv:2604.05394v1 Announce Type: new Abstract: Physics-based character animation has become a fundamental approach for synthesizing realistic, physically plausible motions. While current data-driven deep reinforcement learning …
arXiv:2604.05383v1 Announce Type: new Abstract: Large language models (LLMs) have made notable progress in logical reasoning, yet still fall short of human-level performance. Current boosting …