arXiv AI

DocAtlas: Long-Document Understanding as Mutable-State Interaction

arXiv:2608. 07527v1 Announce Type: cross Abstract: Long-document understanding requires models to find and combine evidence across many pages, layouts, tables, figures, and charts.

arXiv AI

Jun 16

Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion

arXiv:2606. 14885v1 Announce Type: new Abstract: Agentic search over large corpora relies on retriever-mediated interfaces (e.

By Yi Lu, Zhuofeng Li, Ping Nie, Haoxiang Zhang, Yuyu Zhang, Kai Zou, Wenhu Chen, Jimmy Lin, Dongfu Jiang, Yu Zhang

agents benchmarks

arXiv Computation and Language

Aug 21

Doc-V*:Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA

arXiv:2604. 13731v2 Announce Type: replace Abstract: Multi-page Document Visual Question Answering requires reasoning over semantics, layouts, and visual elements in long, visually dense documents.

By Yuanlei Zheng, Pei Fu, Hang Li, Ziyang Wang, Yuyi Zhang, Wenyu Ruan, Xiaojin Zhang, Zhongyu Wei, Zhenbo Luo, Jian Luan, Wei Chen, Xiang Bai

rag agents computer-vision nlp benchmarks

arXiv Computation and Language

Sep 21

Boosting Deepresearch and LongContext Ability with Self-Generated Deepresearch Rollouts Traces

arXiv:2609.20844v1 Announce Type: new Abstract: Deepresearch (DR) agents interact with real-world web environments through multi-turn search and visit, causing their contexts to grow rapidly over tim...

By Zihan Wang, Hao Wang, Boyuan Jiang, Yiqun Zhang, Shi Feng, Xiaocui Yang, Yiwen Ye, Jianghang Lin, Xiaozhong Ji, Jinghao Lin, Kai Wu

agents reinforcement-learning benchmarks safety

arXiv AI

Jul 29

Addressable Recall Compaction for Long Context-Window Control in AI Agents

arXiv:2607. 25066v1 Announce Type: new Abstract: Long-horizon LLM agents accumulate reasoning traces, actions, and tool observations that can eventually exceed a model's fixed context window.

By Thang Dang, Yuma Ichikawa, Sakina Fatima, Koichi Shirahata

llms agents

arXiv AI

3d ago

GrepSeek: Training Search Agents for Direct Corpus Interaction

arXiv:2605.29307v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) search agents have shown strong promise on knowledge-intensive tasks through iterative reasoning and retrieval. Mo...

By Alireza Salemi, Chang Zeng, Atharva Nijasure, Jui-Hui Chung, Razieh Rahimi, Fernando Diaz, Hamed Zamani

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 10

DocMemo: Dynamic Evidence Discovery via Probabilistic Memory-Guided Retrieval for Multi-Modal Document Understanding

arXiv:2608. 07067v1 Announce Type: new Abstract: Long-document understanding requires locating sparse and heterogeneous evidence across hundreds of pages, yet existing systems remain limited by static retrieval and fragile cross-round memory.

By Hanshu Yao, Janfeng Zhong, Niu Lian, Jinpeng Wang

multimodal benchmarks

arXiv AI

4d ago

AnyAct: Universal Action for Self-Evolving Agents

AnyAct introduces a universal action layer that consolidates diverse tool capabilities into a self‑evolving action space for AI agents operating in open‑world environments. It tackles the scale dilemma, tool non‑stationarity, and heterogeneous feedback by using hierarchical progressive retrieval and test‑time reliability evolution, while a heterogeneous observation grounding module unifies multi‑modal feedback. Evaluations on LiveMCPBench and the newly created OSMCP benchmark show state‑of‑the‑art performance, with significant gains in task success rate and reduced execution steps, especially for models with limited native capabilities.

By Lingrui Xu, Yangqin Jiang, Jiachang Zhang, Xubin Ren, Chao Huang

llms agents efficiency multimodal benchmarks

arXiv AI

Aug 12

DOCSCHISEL: Adaptive Tool Documentation Optimization Framework for LLM Agents

arXiv:2608. 10037v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on external tools to accomplish complex real-world tasks, making tool documentation a critical grounding resource for LLM agents.

By You Lu, Kun Zhang, Bihuan Chen, Xin Peng

llms agents benchmarks

arXiv Machine Learning

Sep 22

Machine-Interpretable Information: Compiling Documents into Searchable and Readable Protocol States

arXiv:2609.23371v1 Announce Type: cross Abstract: Long-context language models interface with external knowledge through raw natural language. In retrieval-augmented systems, this creates a persisten...

By Yifan Wang, Dejing Dou

llms rag agents

arXiv AI

Sep 1

SearchWiki: Learning to Build and Navigate Knowledge Wikis for Active Information Seeking

arXiv:2608.29953v1 Announce Type: new Abstract: Flat retrieval-augmented generation treats a corpus as a bag of chunks, discarding document hierarchy and cross document structure. We introduce Search...

By Guransh Singh, Vishwajeet Kumar, Arkadeep Acharya, Adnan Qidwai, Jaydeep Sen, Sachindra Joshi

rag agents reinforcement-learning benchmarks

arXiv AI

Jun 30

HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning

arXiv:2606. 28349v1 Announce Type: cross Abstract: Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories.

By Zeju Li, Ziyang Zheng, Yizhou Zhou, Qiang Xu

rag agents benchmarks

arXiv AI

3d ago

AtlasNav: Mitigating Evidence Blindness with Persistent Corpus Navigation

arXiv:2608.24764v2 Announce Type: replace Abstract: As language-model agents become more capable of iterative search, corpus access is shifting from retrieval toward interaction. Agents can explore t...

By Hongyu Guo, Zhiyu Zheng, Minghao Hu, Shuai Lei, Zhunchen Luo, Chao Ma, Yunbo Cao, Zhao Cao

agents benchmarks

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