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20232026
most citedA Survey of Reinforcement Learning for Large Reasoning Models

2 citations · 5 across the 43 of their papers we have counts for

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Showing 2026 · cs.AIShow all

5 papers · 2 filters

cs.AI2026

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…

cs.AI2026

-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…

cs.AI2026

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…

cs.AI2026

XSkill: Continual Learning from Experience and Skills in Multimodal Agents

Guanyu Jiang, Zhaochen Su, Xiaoye Qu +1

Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestration in open-ended settings. A…

cs.AI2026

Toward Efficient Agents: Memory, Tool learning, and Planning

Xiaofang Yang, Lijun Li, Heng Zhou +12

Recent years have witnessed increasing interest in extending large language models into agentic systems. While the effectiveness of agents has continued to improve, efficiency, whi…