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Xianhao Ou

ACL 2026×2推理强化学习与后训练可解释性代码对齐与偏好
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0 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #2/16ACL 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 #6/11ACL 2026Findings· Findings of ACL

From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning

Guo Tang · Ke Cheng · HuiMing Fan · Heng Chang · Wenxiang Zheng · Xianhao Ou · Junjia Xiang · Ming Liu · Yujun Zhou · Li Lanyu · Bing Qin

摘要

Temporal Knowledge Graph (TKG) forecasting faces significant challenges due to distribution shifts and poor inductive generalization in parametric models. While Large Language Models (LLMs) offer potent semantic reasoning, existing LLM-based approaches struggle with implicit modality alignment an...

推理代码对齐与偏好OpenReview 项目页

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