RCA is a training-free module that boosts input context signal strength in the residual stream of LLMs by orthogonal decoupling of attention routing from value magnitude.
Rich knowledge sources bring complex knowledge conflicts: Recalibrating models to reflect conflicting evidence
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Introduces a facet-level diagnostics framework using Facet x Chunk matrices and controlled inference modes to show that RAG hallucinations arise mainly from evidence integration failures rather than retrieval errors.
A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.
A semi-structured thematic synthesis identifies core challenges in FM selection, alignment, prompting, orchestration, testing, deployment, and cross-cutting concerns like observability for production-ready FMware.
citing papers explorer
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Resonant Context Anchoring: Decoupling Attention Routing and Signal Gain at Inference Time
RCA is a training-free module that boosts input context signal strength in the residual stream of LLMs by orthogonal decoupling of attention routing from value magnitude.
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Facet-Level Tracing of Evidence Uncertainty and Hallucination in RAG
Introduces a facet-level diagnostics framework using Facet x Chunk matrices and controlled inference modes to show that RAG hallucinations arise mainly from evidence integration failures rather than retrieval errors.
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Retrieval-Augmented Generation with Graphs (GraphRAG)
A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.
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From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap
A semi-structured thematic synthesis identifies core challenges in FM selection, alignment, prompting, orchestration, testing, deployment, and cross-cutting concerns like observability for production-ready FMware.