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Latent Knowledge Retrieval(LKR)
Retrieve information based on abstract reasoning patterns rather than explicit queries in multi-agent agentic AI systems
In 30 seconds
- What
- Retrieves knowledge by inferring abstract reasoning patterns across agents rather than matching explicit keywords, synthesizing insights from latent conceptual relationships.
- When to use
- Multi-agent systems tackling open-ended problems where relevant knowledge spans disconnected domains and implicit connections matter more than direct matches.
- Watch out
- High computational cost and brittleness: agents may confidently retrieve plausible but incorrect knowledge when latent patterns don't actually align with ground truth.
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Latent Knowledge Retrieval: Overview
Retrieve information based on abstract reasoning patterns rather than explicit queries in multi-agent agentic AI systems
- Pattern-based knowledge access
- Cross-agent implicit reasoning
- Contextual relevance scoring
- Abstract concept matching
- Dynamic knowledge synthesis
- Multi-dimensional latent navigation
- Emergent insight discovery
- Analogical reasoning chains
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References
The papers, specifications, and repositories this pattern is based on.
- REALM: Retrieval-Augmented LM Pre-Training (2020)
- Dense Passage Retrieval for Open-Domain Question AnsweringarXiv:2004.04906
- Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksarXiv:2005.11401
- Precise Zero-Shot Dense Retrieval without Relevance LabelsarXiv:2212.10496
- Self-RAG: Retrieve, Generate, and Critique (2023)
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