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Yifu Guo

ACL 2026×4AAAI 2026×2ICML 2026×2KDD 2026推理多模态智能体与工具评测与基准代码
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1 一作 · 9 篇 · ACL 2026 / AAAI 2026 / ICML 2026 / KDD 2026

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2026 顶会论文

9
作者位次 #3/9ACL 2026Poster· Natural Language Processing

ACE -Router: Generalizing History-Aware Routing from MCP Tools to the Agent Web

Zhiyuan Yao · Zishan Xu · Yifu Guo · Zhiguang Han · Cheng Yang · Shuo Zhang · Ruiming Tang (Huawei Noah&#x27 · Xingshan Zeng · Weiwen Liu

摘要

With the rise of the Agent Web and Model Context Protocol (MCP), the agent ecosystem is evolving into an open collaborative network, exponentially increasing accessible tools. However, current architectures face severe scalability and generality bottlenecks. To address this, we propose ACE-Router...

智能体与工具评测与基准代码OpenReview 项目页
作者位次 #6/7ACL 2026Findings· Findings of ACL

R epo S hapley: Shapley-Enhanced Context Filtering for Repository-Level Code Completion

Yu Huo · Kun Zeng · Siyu Zhang · YuQuan Lu · Cheng Yang · Yifu Guo · Xiaoying Tang

摘要

Repository-level code completion benefits from retrieval-augmented generation (RAG). However, controlling cross-file evidence is difficult because chunk utility is often interaction-dependent: some snippets help only when paired with complementary context, while others harm decoding when they con...

代码检索与知识安全OpenReview 项目页
作者位次 #6/10ACL 2026Findings· Findings of ACL

R eal M em: Benchmarking LLM s in Real-World Memory-Driven Interaction

Haonan Bian · Zhiyuan Yao · Sen Hu · Zishan Xu · Shaolei Zhang · Yifu Guo · Ziliang Yang · Xueran Han · Huacan Wang · Ronghao Chen

摘要

As Large Language Models (LLMs) evolve from static dialogue interfaces to autonomous general agents, effective memory is paramount to ensuring long-term consistency. However, existing benchmarks primarily focus on casual conversation or task-oriented dialogue, failing to capture “long-term projec...

智能体与工具评测与基准数据与预训练OpenReview 项目页
作者位次 #6/10ACL 2026Findings· Findings of ACL

From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning

Beining Wu · Fuyou Mao · Jiong Lin · Cheng Yang · Jiaxuan Lu · Yifu Guo · Siyu Zhang · Yifan Wu · Ying Huang · Fu Li

摘要

Generative engines (GEs) are reshaping information access by replacing ranked links with citation-grounded answers, yet current Generative Engine Optimization (GEO) methods optimize each instance in isolation, unable to accumulate or transfer effective strategies across tasks and engines. We refr...

智能体与工具评测与基准推理OpenReview 项目页

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