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Yuan Yao

ACL 2026×3ICLR 2026×2KDD 2026OSDI 2026评测与基准推理社会与人本强化学习与后训练多模态
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0 一作 · 7 篇 · ACL 2026 / ICLR 2026 / KDD 2026 / OSDI 2026

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2026 顶会论文

7
作者位次 #4/7OSDI 2026Oral· Operating Systems & Distributed Systems

Neuro-Symbolic Proof Generation for Scaling Systems Software Verification

Baoding He · Zenan Li · Wei Sun · Yuan Yao · Taolue Chen · Xiaoxing Ma · Zhendong Su

摘要

Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet constructing large proof scripts remains highly manual and limits scalability. Advances in large language models (LLMs), especially in mathematical reasoning, make their int...

推理效率与系统评测与基准OpenReview
作者位次 #8/8ACL 2026Poster· Natural Language Processing

C hess A rena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models

Jincheng Liu · Sijun He · Jingjing Wu · Xiangsen Wang · Yang Chen · Zhaoqi Kuang · Siqi Bao · Yuan Yao

摘要

Recent large language models (LLMs) have shown strong reasoning capabilities. However, a critical question remains: do these models possess genuine strategic reasoning, or do they primarily excel at pattern recognition? To address this, we present ChessArena, a chess-based testbed for evaluating...

推理强化学习与后训练评测与基准OpenReview 项目页
作者位次 #6/9ACL 2026Poster· Natural Language Processing

C har T ide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution

Xiangxi Zheng · Kuang He · Jiayi Hu · Ping Yu · Rui Yan · Yuan Yao · Peng Hou · Anxiang Zeng · Alex Jinpeng Wang

摘要

Chart-to-code generation demands strict visual precision and syntactic correctness from Vision-Language Models (VLMs). However, existing approaches are fundamentally constrained by data-centric limitations: despite the availability of growing chart-to-code datasets, simply scaling homogeneous cha...

多模态强化学习与后训练代码OpenReview 项目页
作者位次 #7/8ACL 2026Findings· Findings of ACL

V - MAGE : A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models

Xiangxi Zheng · Linjie Li · Zhengyuan Yang · Ping Yu · Alex Jinpeng Wang · Rui Yan · Yuan Yao · Lijuan Wang

摘要

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in visual-text processing. However, existing static image-text benchmarks are insufficient for evaluating their dynamic perception and interactive reasoning abilities. We introduce V ision-ce...

多模态评测与基准社会与人本OpenReview 项目页

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