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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.798ACL Anthology · ACL 2026

PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing

Zhuoqun Li, Xuanang Chen, Hongyu Lin Institute of Software, Chinese Academy of Sciences, Yaojie Lu Institute of Software, Chinese Academy of Sciences, Xianpei Han Institute of Software, CAS, Shanshan Jiang, Bin Dong Ricoh Software Research Center Beijing Co., Ltd., Le Sun Institute of Software, Chinese Academy of Sciences

As researchers delve more deeply into their work, paper search requirements may become more flexible, sometimes involving specific details such as module configuration rather than being limited to coarse-grained topics. However, previous paper search systems are unable to meet these flexible-grained requirements, as previous systems mainly collect paper abstract to construct corpus index, which lacks detailed information to support retrieval by some finer-grained queries. In this work, we propose PaperRegister, which transforms traditional abstract-based index into a hierarchical index tree, thereby supporting queries at flexible granularity. Experiments on paper search tasks across a range of granularity demonstrate that PaperRegister achieves the SOTA performance, and particularly excels in the fine-grained scenarios, highlighting good potential as an effective solution for flexible-grained paper search in real-world applications.

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cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

ACL Anthology · ACL 2026 ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.87ACL Anthology · ACL 2026

GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks

Jianwen Luo, Yiming Huang, Jinxiang Meng University of the Chinese Academy of Sciences, Fangyu Lei, Shizhu He Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Xiao Liu Microsoft Research Asia, Shanshan Jiang, Bin Dong Ricoh Software Research Center Beijing Co., Ltd. 等 10 位作者

Large Language Models (LLMs) have shown great promise in tool-making, yet existing frameworks often struggle to efficiently construct reliable toolsets and are limited to single-task settings. To address these challenges, we propose GATE (Graph-based Adaptive Tool Evolution), an adaptive framework that dynamically constructs and evolves a hierarchical graph of reusable tools across multiple scenarios. We evaluate GATE on open-ended tasks (Minecraft), agent-based tasks (TextCraft, DABench), and code generation tasks (MATH, Date, TabMWP). Our results show that GATE achieves up to 4.3× faster milestone completion in Minecraft compared to the previous state-of-the-art method, and provides an average improvement of 9.23% over existing tool-making methods in code generation tasks and 10.03% in agent tasks. Further analysis shows that GATE exhibits strong adaptive evolution capabilities, effectively balancing tool quantity, complexity, and functionality while maintaining high efficiency. Code and data are available at https://github.com/ayanami2003/GATE.

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cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

ACL Anthology · ACL 2026 ↗PDF ↗