UIS-Digger: Towards Comprehensive Research Agent Systems for Real-world Unindexed Information Seeking

2026-03-09

AI Summary

This work addresses a critical limitation of current large language model–based retrieval agents, which heavily rely on search engine indexes and consequently fail to access dynamic web pages, embedded files, and other unindexed content, resulting in significant blind spots. To tackle this challenge, the study formally defines the Unindexed Information Seeking (UIS) problem and introduces UIS-Digger, a multi-agent framework that employs a dual-mode browsing mechanism to simultaneously explore web pages and parse embedded documents, actively uncovering previously inaccessible information sources. The authors construct UIS-QA, the first benchmark dedicated to UIS, and optimize their system using a 30B-parameter language model enhanced through both supervised and reinforcement fine-tuning. Experimental results demonstrate that UIS-Digger achieves 27.27% accuracy on UIS-QA, substantially outperforming stronger baselines—including ensembles incorporating GPT-4.1—thereby validating the efficacy of proactive interaction with unindexed sources.

Technology Category

• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web crawling and indexing
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent advancements in LLM-based information-seeking agents have achieved record-breaking performance on established benchmarks. However, these agents remain heavily reliant on search-engine-indexed knowledge, leaving a critical blind spot: Unindexed Information Seeking (UIS). This paper identifies and explores the UIS problem, where vital information is not captured by search engine crawlers, such as overlooked content, dynamic webpages, and embedded files. Despite its significance, UIS remains an underexplored challenge. To address this gap, we introduce UIS-QA, the first dedicated UIS benchmark, comprising 110 expert-annotated QA pairs. Notably, even state-of-the-art agents experience a drastic performance drop on UIS-QA (e.g., from 70.90 on GAIA and 46.70 on BrowseComp-zh to 24.55 on UIS-QA), underscoring the severity of the problem. To mitigate this, we propose UIS-Digger, a novel multi-agent framework that incorporates dual-mode browsing and enables simultaneous webpage searching and file parsing. With a relatively small $\sim$30B-parameter backbone LLM optimized using SFT and RFT training strategies, UIS-Digger sets a strong baseline at 27.27\%, outperforming systems integrating sophisticated LLMs such as O3 and GPT-4.1. This demonstrates the importance of proactive interaction with unindexed sources for effective and comprehensive information-seeking. Our work not only uncovers a fundamental limitation in current agent evaluation paradigms but also provides the first toolkit for advancing UIS research, defining a new and promising direction for robust information-seeking systems.

Problem

• Unindexed Information Seeking
• Information-seeking agents
• Search engine limitations
• Dynamic web content
• Non-indexed data

Innovation

• Unindexed Information Seeking
• Multi-agent Framework
• Dual-mode Browsing
• File Parsing
• Information-seeking Agents