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Tao He

AAAI 2026ACL 2026ICLR 2026多模态评测与基准强化学习与后训练推理可解释性
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1 一作 · 3 篇 · AAAI 2026 / ACL 2026 / ICLR 2026

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2026 顶会论文

3
作者位次 #7/16ACL 2026Poster· Natural Language Processing

Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models

Runxuan Liu · Xianhao Ou · Xinyan Ma · Jiyuan Wang · Jiafeng Liang · Jiaqi Li · Tao He · Zheng Chu · Rongchuan Mu · Zekun Wang · Baoxin Wang · Dayong Wu · Ming Liu · Shijin Wang · Guoping Hu · Bing Qin

摘要

Long Chain-of-Thought (LCoT), achieved by Reinforcement Learning with Verifiable Rewards (RLVR), has proven effective in enhancing the reasoning capabilities of Large Language Models (LLMs). However, reasoning in current LLMs is primarily generated as plain text, where performing semantic evaluat...

强化学习与后训练推理可解释性OpenReview 项目页
作者位次 #2/5AAAI 2026Poster· Artificial Intelligence

From Sampling to Cognition: Modeling Internal Cognitive Confidence in Language Models for Robust Uncertainty Calibration

Hao Li · Tao He · Jiafeng Liang · Zheng Chu · Ming Liu

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