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GAM-RAG: Gain-Adaptive Memory for Evolving Retrieval in Retrieval-Augmented Generation
arXiv – CS AI|Yifan Wang, Mingxuan Jiang, Zhihao Sun, Yixin Cao, Yicun Liu, Keyang Chen, Guangnan Ye, Hongfeng Chai||2 views
🤖AI Summary
Researchers introduce GAM-RAG, a training-free framework that improves Retrieval-Augmented Generation by building adaptive memory from past queries instead of relying on static indices. The system uses uncertainty-aware updates inspired by cognitive neuroscience to balance stability and adaptability, achieving 3.95% better performance while reducing inference costs by 61%.
Key Takeaways
- →GAM-RAG eliminates static pre-built indices by creating adaptive hierarchical memory that learns from recurring queries.
- →The framework uses a Kalman-inspired gain rule to handle noisy feedback while updating memory states and uncertainty estimates.
- →Performance improvements of 3.95% over strongest baselines and 8.19% with 5-turn memory demonstrate significant advancement.
- →Inference costs reduced by 61% through eliminating repetitive multi-hop traversals for related queries.
- →The training-free approach makes it accessible for implementation without requiring model retraining.
#rag#retrieval-augmented-generation#machine-learning#llm#memory-systems#inference-optimization#cognitive-computing#arxiv#research
Read Original →via arXiv – CS AI
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