arXiv:2605.25480v1 Announce Type: new Abstract: LLM agents require retrieval to behave less like one-shot context fetching and more like reasoning: searching, reading, traversing, and deciding when evidence is sufficient. However, Retrieval-Augmented Generation (RAG) typically organizes external knowledge as flat chunks retrieved by embedding similarity, exposing a retrieval-as-lookup interface that is poorly aligned with tool-using agents. We propose LLM-Wiki, an agent-native retrieval system that operationalizes the Retrieval-as-Reasoning paradigm by treating external knowledge as a compilab
The proliferation of LLM agents highlights the limitations of current RAG architectures, necessitating more sophisticated methods for knowledge retrieval and reasoning.
This development moves beyond simple lookup, enabling AI agents to engage in more complex and nuanced interactions with information, which is critical for autonomous operations.
Retrieval for AI agents shifts from static context injection to dynamic, reasoning-driven interaction with external knowledge bases, enhancing their capabilities.
- · AI agent developers
- · Enterprises deploying AI agents
- · Knowledge management platforms
- · Cloud AI service providers
- · Legacy RAG system providers
- · Companies relying on simple information lookup for AI
- · Data architectures not easily 'compilable'
AI agents become significantly more capable in tasks requiring complex information synthesis.
Increased adoption of AI agents in roles previously requiring human analytical capabilities.
New paradigms for knowledge representation and interaction emerge across all digital systems.
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Read at arXiv cs.CL