#parametric-memory News & Analysis

7 articles tagged with #parametric-memory. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

7 articles

AIBullisharXiv – CS AI · Jun 57/10

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FIDES: Faithful Inference via Deep Evidence Signals for Retrieval-Memory Conflict in RAG

FIDES is a training-free decoder that improves how language models handle conflicts between retrieved evidence and internal knowledge by applying selective, token-level corrections rather than uniform adjustments. The method achieves up to 92-94% context fidelity across multiple model scales, demonstrating that targeted intervention at critical decoding points outperforms existing contrastive decoding approaches.

AIBullisharXiv – CS AI · Jun 47/10

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Scaling Self-Evolving Agents via Parametric Memory

Researchers introduce TMEM, a parametric memory framework that enables AI agents to learn and evolve within a single episode by updating LoRA weights online, rather than merely retrieving frozen memories. This approach combines explicit memory storage with fast adaptive weights, allowing agents to genuinely improve their policy during rollouts and demonstrates consistent performance gains across multiple benchmarks.

AIBullisharXiv – CS AI · May 277/10

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ICICLE: Expanding Retrieval with In-Context Documents

Researchers introduce ICICLE, a generative retrieval framework that addresses the inefficiency of traditional corpus expansion by treating new documents as in-context evidence rather than requiring model retraining. The approach uses a copy-based routing mechanism to distinguish between parametric memory and context-provided document associations, achieving better scalability without catastrophic forgetting.

AINeutralarXiv – CS AI · May 97/10

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Attractor Geometry of Transformer Memory: From Conflict Arbitration to Confident Hallucination

Researchers have identified a geometric framework explaining how language models fail through two distinct mechanisms: parametric memory conflicting with working memory, and hallucination from absent learned facts. Both failures produce confident outputs despite being mechanistically different, but hidden-state geometry and 'geometric margin' metrics can distinguish them more reliably than traditional entropy-based detection methods.

AINeutralarXiv – CS AI · Jun 116/10

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Substrate Asymmetry in User-Side Memory: A Diagnostic Framework

Researchers reveal that large language model user-memory capabilities exhibit substrate asymmetry across three orthogonal dimensions—behavioral consistency, factual recall, and factual abstinence—with parametric methods (gamma-LoRA) excelling at style preservation while retrieval-augmented generation (RAG) excels at knowing when to abstain. The same neural circuits drive opposite-direction failures, and this tradeoff intensifies in heavily RLHF-tuned models, suggesting fundamental alignment costs to parametric personalization.

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AINeutralarXiv – CS AI · May 296/10

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How LoRA Remembers? A Parametric Memory Law for LLM Finetuning

Researchers introduce the Parametric Memory Law, a power law framework quantifying how Large Language Models store information through Low-Rank Adaptation (LoRA) finetuning. The study reveals a deterministic phase transition at the token level and proposes MemFT, an optimization strategy that improves memory fidelity by dynamically redistributing training resources toward undertrained tokens.

AIBullisharXiv – CS AI · Apr 76/10

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Memory Intelligence Agent

Researchers have developed Memory Intelligence Agent (MIA), a new AI framework that improves deep research agents through a Manager-Planner-Executor architecture with advanced memory systems. The framework enables continuous learning during inference and demonstrates superior performance across eleven benchmarks through enhanced cooperation between parametric and non-parametric memory systems.