SourceLearn Builds Source-Specific Agent Competence, Wins 13 of 15 Benchmarks
GeorgiaTech · hf · 2026-10-06
Georgia Tech researchers propose "source learning": instead of treating repeated use of a knowledge source as repeated retrieval, LLM agents should progressively build reusable source-specific competence, represented as a persistent "source model" capturing how the source's knowledge is structured, interpreted, and applied.
SourceLearn combines Self-Directed Source Learning (adaptively revisiting what remains incompletely understood) with Task-Guided Source Learning (using downstream experience to reveal representational gaps), while persistent updates are reconstructed from the authoritative source.
Across five benchmarks and three LLM backends, it wins 13 of 15 settings, beating Hybrid RAG by up to 22.6 points and substantially outperforming static source representations and experience-based memory baselines.
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