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Xuhong Wang

ACL 2026×4ICLR 2026×2CVPR 2026强化学习与后训练多模态评测与基准效率与系统社会与人本
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0 一作 · 7 篇 · ACL 2026 / ICLR 2026 / CVPR 2026

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2026 顶会论文

7
作者位次 #2/4ICLR 2026Poster· Computer Vision / Vision Models & Multimodal

TPRU: Advancing Temporal and Procedural Understanding in Large Multimodal Models

Zhenkun Gao · Xuhong Wang · Xin Tan · Yuan Xie

多模态OpenReview
作者位次 #3/5ACL 2026Poster· Natural Language Processing

Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with Constraints

Zhenyun Yin · Shujie Wang · Xuhong Wang · Xingjun Ma · Yingchun Wang

摘要

Large language models with search capabilities frequently exhibit miscalibrated confidence, producing incorrect answers with high certainty. We present Deliberative Searcher, a reasoning-primary framework that integrates search operations into chain-of-thought generation while maintaining explici...

推理强化学习与后训练效率与系统OpenReview 项目页
作者位次 #6/8ACL 2026Poster· Natural Language Processing

N avi M aster: Learning a Unified Policy for GUI and Embodied Navigation Tasks

Zhihao Luo · Wentao Yan · Jingyu Gong · Min Wang · Zhizhong Zhang · Xuhong Wang · Yuan Xie · Xin Tan

摘要

Recent advances in Graphical User Interface (GUI) and embodied navigation have driven progress, yet these domains have largely evolved in isolation, with disparate datasets and training paradigms. In this paper, we observe that both tasks can be formulated as Markov Decision Processes (MDP), sugg...

智能体与工具多模态强化学习与后训练OpenReview 项目页
作者位次 #4/4ACL 2026Findings· Findings of ACL

From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Tasks

Qingyu Ren · Tianjun Pan · Xingzhou Chen · Xuhong Wang

摘要

Large language models have achieved remarkable progress in text generation but still struggle with generative writing tasks. In terms of evaluation, existing evaluation benchmarks include few requirement types and writing reward models are not evaluated. In terms of training, existing studies oft...

强化学习与后训练评测与基准对齐与偏好OpenReview 项目页
作者位次 #2/2ACL 2026Findings· Findings of ACL

Decoupled Reasoning with Implicit Fact Tokens ( DRIFT ): A Dual-Model Framework for Efficient Long-Context Inference

Wenxuan Xie · Xuhong Wang

摘要

The integration of extensive, dynamic knowledge into Large Language Models (LLMs) remains a significant challenge due to the inherent entanglement of factual data and reasoning patterns. Existing solutions, ranging from non-parametric Retrieval-Augmented Generation (RAG) to parametric knowledge e...

检索与知识效率与系统长上下文与架构OpenReview 项目页

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