AI

PatchScout: Thematic Web Data Collection via Information Foraging

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 03/11/2026, 09:03:24 UTC.

Briefings

Researchers developed PatchScout, a multi-agent framework for thematic web data collection that balances local exploitation and adaptive patch switching. Experiments on four live-web topics demonstrated that PatchScout substantially improves yield and domain coverage compared to traversal-based and search-augmented reasoning agent baselines. In an applied social science setting with an incomplete prior collection, PatchScout discovered 102 previously unidentified entities.

Key points

• PatchScout is a multi-agent framework that uses patch-switching policies to balance local exploitation and adaptive patch switching under a fixed budget.
• PatchScout substantially improves yield and domain coverage over traversal-based and search-augmented reasoning agent baselines on four live-web topics.

Context

The study addresses the problem of assembling semantically relevant documents scattered across heterogeneous, structurally disconnected web regions. Existing approaches like traversal-based methods and LLM-driven agents are limited by local exploration or short-horizon stopping. The research applies these findings to a social science context where a prior collection was incomplete.

Limitations

The study covers four live-web topics and one applied social science setting with an incomplete prior collection. Results are based on experiments conducted on arXiv:2611.00169v1, which is a preprint. The specific number of entities discovered (102) is derived from the applied setting, not the general web experiments.

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