New GC-Mem protocol tackles Semantic Shadowing in RAG systems

This paper introduces GC-Mem, a novel inference-time consistency protocol designed to address the "Semantic Shadowing" problem in retrieval-augmented generation (RAG) systems. Semantic Shadowing occurs when conflicting historical observations in RAG's memory architecture lead to agents acting on obsolete information, causing state divergence. The research formalizes state mutability and demonstrates how standard RAG can suffer from asymptotic recall decay and a "Majority Vote Trap," where larger context windows paradoxically reduce accuracy. GC-Mem utilizes a temporal dominance operator and contradiction detection to surgically remove shadowed context, achieving over 90% conflict resolution accuracy in evaluations. AI

IMPACT Improves the reliability and accuracy of autonomous agents by addressing critical memory failures in RAG systems.

RANK_REASON Academic paper detailing a new method for improving AI systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Asymptotic Recall Decay
• DagsHub
• GC-Mem
• Hamed Haddadpajouh
• Hugging Face
• Majority Vote Trap
• retrieval-augmented generation
• Semantic Shadowing

• paper
• infra

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