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Xianquan Wang

ACL 2026×2AAAI 2026CVPR 2026ICLR 2026KDD 2026SIGIR 2026多模态检索与知识推理效率与系统评测与基准
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1 一作 · 7 篇 · ACL 2026 / AAAI 2026 / CVPR 2026 / ICLR 2026 / KDD 2026 / SIGIR 2026

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2026 顶会论文

7
作者位次 #5/5ACL 2026Poster· Natural Language Processing

From ID to LLM : Rethinking Representation Learning for Recommendation

Song-Li Wu · Zhaocheng Du · Weinan Gan · Jingyi Wang · Xianquan Wang

摘要

Recent studies indicate a fundamental incompatibility between ID representations and language model (LM) representations, as they capture behavioral and semantic spaces respectively. This mismatch leads LM representations to consistently underperform ID representations in recommendation tasks. In...

对齐与偏好推理可解释性OpenReview 项目页
作者位次 #3/4ACL 2026Findings· Findings of ACL

C a PE dit: Capability-Preserving Lifelong Knowledge Editing For Language Models

Song-Li Wu · Zhaocheng Du · Xianquan Wang · Jingyi Wang

摘要

Lifelong knowledge editing (LKE) aims to incrementally correct factual inaccuracies in large language models (LLMs), but sequential edits can lead to substantial degradation of capabilities. Existing approaches primarily rely on static parameter regularization, which restricts knowledge integrati...

检索与知识多模态OpenReview 项目页
作者位次 #3/7SIGIR 2026Oral· Information Retrieval (Full)

Good Ranks Follow Good Answers: Unsupervised Answer-Driven Reranking for Multimodal Document QA

Keyu Zhu · Shuanghong Shen · Xianquan Wang · Kai Zhang · Shijin Wang · Qi Liu · Zhenya Huang

摘要

Multimodal Document Question Answering (MDQA) systems commonly follow a retrieve-then-answer paradigm; however, the retrieval stage often introduces substantial noise, making an effective reranking component indispensable.Existing reranker training frameworks in MDQA rely predominantly on proxy s...

多模态强化学习与后训练检索与知识OpenReview

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