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Hefeng Wu

ACL 2026×3CVPR 2026ICLR 2026多模态推理评测与基准强化学习与后训练检索与知识
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0 一作 · 5 篇 · ACL 2026 / CVPR 2026 / ICLR 2026

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2026 顶会论文

5
作者位次 #4/4ACL 2026Findings· Findings of ACL

Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning

Cong Wan · Ying He · Zhongzhan Huang · Hefeng Wu

摘要

Test-time Scaling (TTS) has emerged as a pivotal research direction for enhancing model performance by dynamically allocating computational resources during inference. Recent advancements have adapted this paradigm to Multimodal Foundation Models (MFMs), unlocking their potential in multimodal re...

推理评测与基准多模态OpenReview 项目页
作者位次 #3/5ACL 2026Findings· Findings of ACL

Collaborative Multi-Agent Scripts Generation for Enhancing Imperfect-Information Reasoning in Murder Mystery Games

Keyang Zhong · Junlin Xie · Hefeng Wu · Haofeng Li · Guanbin Li

摘要

Vision-language models (VLMs) have shown impressive capabilities in perceptual tasks, yet they degrade in complex multi-hop reasoning under multi-player game settings with imperfect and deceptive information. In this paper, we pick up a representative multi-player task, Murder Mystery Games, whic...

多模态强化学习与后训练推理OpenReview 项目页
作者位次 #6/7ACL 2026Findings· Findings of ACL

Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models

Guoming Ling · Zhongzhan Huang · Yupei Lin · Junxin Li · Shanshan Zhong · Hefeng Wu · Liang Lin

摘要

Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models generate reasoning steps sequentially without foresight, often becoming trapped in suboptimal reasoning paths with redundant steps. In contrast, we introd...

推理评测与基准检索与知识OpenReview 项目页

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