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cs.AI2026
GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning
Yuchen Ying, Weiqi Jiang, Tongya Zheng +4
Knowledge graphs provide structured and reliable information for many real-world applications, motivating increasing interest in combining large language models (LLMs) with graph-b…
cs.AI2026
AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
Yutao Yang, Junsong Li, Qianjun Pan +9
In practical LLM applications, users repeatedly express stable preferences and requirements, such as reducing hallucinations, following institutional writing conventions, or avoidi…
cs.AI2025
NP-Engine: Empowering Optimization Reasoning in Large Language Models with Verifiable Synthetic NP Problems
Xiaozhe Li, Xinyu Fang, Shengyuan Ding +4
Large Language Models (LLMs) have shown strong reasoning capabilities, with models like OpenAI's O-series and DeepSeek R1 excelling at tasks such as mathematics, coding, logic, and…