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EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments
Zhilin Wang, Han Song, Runzhe Zhan +13
Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a final score or confound it…
ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics
Shunkai Zhang, Haoran Zhang, Yun Luo +15
Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structural insight. Recent evidence…
SubtleMemory: A Benchmark for Fine-Grained Relational Memory Discrimination in Long-Horizon AI Agents
Wenxuan Wang, Haoyu Sun, Fukuan Hou +4
Persistent AI assistants, such as OpenClaw, accumulate large collections of related memories over long-term interactions. As these memories grow, they may reinforce one another, di…
-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows
Haoran Zhang, Luxin Xu, Zhilin Wang +11
The rise of personal assistant agents, e.g., OpenClaw, highlights the growing potential of large language models to support users across everyday life and work. A core challenge in…
Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling
Yafu Li, Runzhe Zhan, Haoran Zhang +25
Recent progress in reasoning models has substantially advanced long-horizon mathematical and scientific problem solving, with several systems now reaching gold-medal-level performa…
Memory Intelligence Agent
Jingyang Qiao, Weicheng Meng, Yu Cheng +6
Deep research agents (DRAs) integrate LLM reasoning with external tools. Memory systems enable DRAs to leverage historical experiences, which are essential for efficient reasoning…
Conditional Advantage Estimation for Reinforcement Learning in Large Reasoning Models
Guanxu Chen, Yafu Li, Yuxian Jiang +6
Reinforcement Learning with Verifiable Rewards (RLVR) for large language models (LLMs) has achieved remarkable progress in enhancing LLMs' reasoning capabilities on tasks with clea…
Diversity-Incentivized Exploration for Versatile Reasoning
Zican Hu, Shilin Zhang, Yafu Li +7
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a crucial paradigm for incentivizing reasoning capabilities in Large Language Models (LLMs). Due to vast state-…