A five-step decomposition probe and a new conflict benchmark show that RAG context compliance is measurable, and that accuracy gains from decomposition can transfer across model families even when causal coupling to the reasoning trace does not.
Astute rag: Overcoming imperfect retrieval augmentation and knowledge conflicts for large language models
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
representative citing papers
DyKnow-RAG uses Group Relative Policy Optimization with dual-group rollouts and posterior-driven advantage scaling to optimize context utilization in RAG for e-commerce relevance, showing offline gains and production lifts when deployed at Taobao.
PMSR progressively constructs structured reasoning trajectories with dual-scope queries and compositional reasoning to improve knowledge acquisition and answer accuracy in knowledge-intensive VQA.
PRA-RAG is a new aggregation algorithm for RAG that claims provable robustness bounds against poisoned retrieved texts and reduces attack success rate to 1% while keeping 71% accuracy.
A conformal prediction filter for retrieval chunks plus an attention-based factuality classifier can raise RAG answer quality by up to 6% and detect inconsistent generations up to 77% of the time.
Interacting Gaussian mixture models with RAG-style updates are shown to mimic aspects of interacting LLMs and are used to prove lower bounds on polarization probability in the resulting Markov chain.
This survey categorizes anomalies in agent systems into intra-agent and inter-agent types and introduces the AgentOps framework with four operational stages.
citing papers explorer
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Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict
A five-step decomposition probe and a new conflict benchmark show that RAG context compliance is measurable, and that accuracy gains from decomposition can transfer across model families even when causal coupling to the reasoning trace does not.
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Learning to Trust: Dynamic Utilization of Retrieval-Augmented Generation for E-commerce Search Relevance
DyKnow-RAG uses Group Relative Policy Optimization with dual-group rollouts and posterior-driven advantage scaling to optimize context utilization in RAG for e-commerce relevance, showing offline gains and production lifts when deployed at Taobao.
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Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering
PMSR progressively constructs structured reasoning trajectories with dual-scope queries and compositional reasoning to improve knowledge acquisition and answer accuracy in knowledge-intensive VQA.
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PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption
PRA-RAG is a new aggregation algorithm for RAG that claims provable robustness bounds against poisoned retrieved texts and reduces attack success rate to 1% while keeping 71% accuracy.
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Towards Dependable Retrieval-Augmented Generation Using Factual Confidence Prediction
A conformal prediction filter for retrieval chunks plus an attention-based factuality classifier can raise RAG answer quality by up to 6% and detect inconsistent generations up to 77% of the time.
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Gaussian mixture models as a proxy for interacting language models
Interacting Gaussian mixture models with RAG-style updates are shown to mimic aspects of interacting LLMs and are used to prove lower bounds on polarization probability in the resulting Markov chain.
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Agent System Operations: Categorization, Challenges, and Future Directions
This survey categorizes anomalies in agent systems into intra-agent and inter-agent types and introduces the AgentOps framework with four operational stages.