AGENT KB: Leveraging Cross-Domain Experience for Agentic Problem Solving
AGENT KB is a shared knowledge base for AI agents that facilitates cross-domain knowledge transfer. It uses a novel teacher-student dual-phase retrieval mechanism, where student agents get strategic guidance and teacher agents provide execution-level refinement. This hierarchical approach allows agents to leverage diverse problem-solving strategies and improve performance on complex tasks. ✨
Article Points:
1
AGENT KB: Shared knowledge base for cross-domain agent experience transfer.
2
Teacher-student dual-phase retrieval for strategic guidance and execution refinement.
3
Hierarchical approach enables agents to incorporate diverse external strategies.
4
Significant performance gains on GAIA (up to 6.06% pass@1) and SWE-bench (8.67% gain).
5
Refinement module is critical for effective knowledge transfer and error correction.
6
Automated knowledge generation achieves comparable performance to manual crafting.
AGENT KB: Leveraging Cross-Domain Experience for Agentic Problem Solving
Core Concept
Shared Knowledge Base
Cross-Domain Transfer
Teacher-Student Dual-Phase
Methodology
KB Construction
Reason-Retrieve-Refine Pipeline
Experience Abstraction
Performance
GAIA Benchmark Gains
SWE-bench Resolution Rates
Consistent LLM Improvements
Ablation Studies
Refinement Module Critical
Hybrid Retrieval Superior
Automated KB Comparable to Manual
Limitations
Scalability Constraints
Knowledge Quality & Deprecation
Cross-Domain Transfer Limits
Future Work