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Xiusi Chen

ACL 2026×4ICML 2026强化学习与后训练检索与知识推理评测与基准数据与预训练
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0 一作 · 5 篇 · ACL 2026 / ICML 2026

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2026 顶会论文

5
作者位次 #3/5ICML 2026Spotlight· Deep Learning / Large Language Models

Beyond Log Likelihood: Probability-Based Objectives for Supervised Fine-Tuning across the Model Capability Continuum

Gaotang Li · Ruizhong Qiu · Xiusi Chen · Heng Ji · Hanghang Tong

强化学习与后训练OpenReview
作者位次 #6/7ACL 2026Poster· Natural Language Processing

Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLM s as Semantic Encoders

Yupeng Hou · Jiacheng Li · Xiangjun Fu · Zhankui He · An Yan · Xiusi Chen · Julian J. McAuley

摘要

Feature engineering has long been central to recommender systems, yet effectively leveraging textual item features remains challenging. Recent advances in large language models (LLMs) have enabled their use as semantic encoders for recommendation, but their roles and behaviors in this setting are...

评测与基准检索与知识数据与预训练OpenReview 项目页
作者位次 #4/11ACL 2026Poster· Natural Language Processing

Current Agents Fail to Leverage World Model as Tool for Foresight

Cheng Qian · Emre Can Acikgoz · Bingxuan Li · Xiusi Chen · Yuji Zhang · Bingxiang He · Qinyu Luo · Gokhan Tur · Dilek Hakkani-Tür · Yunzhu Li · Heng Ji

摘要

Agents built on vision-language models increasingly face tasks that demand anticipating future states rather than relying on short-horizon reasoning. Generative world models offer a promising remedy: agents could use them as external simulators to foresee outcomes before acting. This paper empiri...

可解释性多模态推理OpenReview 项目页
作者位次 #4/7ACL 2026Poster· Natural Language Processing

PEARL : Self-Evolving Assistant for Time Management with Reinforcement Learning

Bingxuan Li · Jeonghwan Kim · Cheng Qian · Xiusi Chen · Eitan Anzenberg · Niran Kundapur · Heng Ji

摘要

Overlapping calendar invitations force busy professionals to repeatedly decide which meetings to attend, reschedule, or decline. We refer to this preference-driven decision process as calendar conflict resolution. Automating this decision process is crucial yet challenging. Scheduling logistics c...

智能体与工具对齐与偏好强化学习与后训练OpenReview 项目页
作者位次 #3/6ACL 2026Findings· Findings of ACL

V eri- R 1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning

Qi He · Cheng Qian · Xiusi Chen · Bingxiang He · Yi R. Fung · Heng Ji

摘要

Claim verification with large language models (LLMs) has recently attracted growing attention, due to their strong reasoning capabilities and transparent verification processes compared to traditional answer-only judgments. However, existing approaches to online claim verification, which requires...

推理强化学习与后训练检索与知识OpenReview 项目页

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