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cs.AI2026
Training LLM Agents for Spontaneous, Reward-Free Self-Evolution via World Knowledge Exploration
Qifan Zhang, Dongyang Ma, Tianqing Fang +5
Most agents today ``self-evolve'' by following rewards and rules defined by humans. However, this process remains fundamentally dependent on external supervision; without human gui…
cs.AI2026
Exposing Weaknesses of Large Reasoning Models through Graph Algorithm Problems
Qifan Zhang, Jianhao Ruan, Aochuan Chen +4
Large Reasoning Models (LRMs) have advanced rapidly; however, existing benchmarks in mathematics, code, and common-sense reasoning remain limited. They lack long-context evaluation…
cs.AI2024
GraphArena: Evaluating and Exploring Large Language Models on Graph Computation
Jianheng Tang, Qifan Zhang, Yuhan Li +2
The ``arms race'' of Large Language Models (LLMs) demands new benchmarks to examine their progresses. In this paper, we introduce GraphArena, a benchmarking tool designed to evalua…