2 citations · 2 across the 5 of their papers we have counts for
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cs.AI2026
DASH: Divergence-Adaptive Supervision Horizons for On-Policy Self-Distillation of Reasoning Models
ZhiYan Hou, Xinyu Tang, Hongyan An +9
Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models using automatically verifiable outcome signals, but these signals…
cs.AI2026★ 2 cited
EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer
Xingze Gao, Chuanrui Hu, Hongda Chen +9
Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and ve…
cs.AI2026
LingxiDiagBench: A Multi-Agent Framework for Benchmarking LLMs in Chinese Psychiatric Consultation and Diagnosis
Shihao Xu, Tiancheng Zhou, Jiatong Ma +8
Mental disorders are highly prevalent worldwide, but the shortage of psychiatrists and the inherent subjectivity of interview-based diagnosis create substantial barriers to timely…