ACGM learns task-adaptive sparse graphs over multi-modal agent histories via policy-gradient optimization, reaching 82.7 nDCG@10 and 89.2% Precision@10 on WebShop, VisualWebArena, and Mind2Web while outperforming 19 baselines.
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Argues for a denoising-first paradigm in LLM-oriented information retrieval, framing challenges via a four-stage progression and providing a taxonomy of signal-to-noise optimization techniques across the pipeline.
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Task-Adaptive Retrieval over Agentic Multi-Modal Web Histories via Learned Graph Memory
ACGM learns task-adaptive sparse graphs over multi-modal agent histories via policy-gradient optimization, reaching 82.7 nDCG@10 and 89.2% Precision@10 on WebShop, VisualWebArena, and Mind2Web while outperforming 19 baselines.
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LLM-Oriented Information Retrieval: A Denoising-First Perspective
Argues for a denoising-first paradigm in LLM-oriented information retrieval, framing challenges via a four-stage progression and providing a taxonomy of signal-to-noise optimization techniques across the pipeline.