X-SYNTH synthesizes enterprise context from digital human attention using Digital Twin Signatures and seven attention filters, raising true lead rate from 9.5% to 61.9% while cutting false lead rate to 18.8%.
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Corrective Retrieval Augmented Generation
Canonical reference. 86% of citing Pith papers cite this work as background.
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abstract
Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate. Although retrieval-augmented generation (RAG) is a practicable complement to LLMs, it relies heavily on the relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong. To this end, we propose the Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation. Specifically, a lightweight retrieval evaluator is designed to assess the overall quality of retrieved documents for a query, returning a confidence degree based on which different knowledge retrieval actions can be triggered. Since retrieval from static and limited corpora can only return sub-optimal documents, large-scale web searches are utilized as an extension for augmenting the retrieval results. Besides, a decompose-then-recompose algorithm is designed for retrieved documents to selectively focus on key information and filter out irrelevant information in them. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches. Experiments on four datasets covering short- and long-form generation tasks show that CRAG can significantly improve the performance of RAG-based approaches.
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representative citing papers
EvolveMem enables autonomous self-evolution of LLM memory retrieval configurations via LLM diagnosis and safeguards, delivering 25.7% gains over strong baselines on LoCoMo and 18.9% on MemBench with positive cross-benchmark transfer.
Pre-Route elicits LLMs' latent routing skills via structured prompts on metadata to proactively choose RAG or long-context, outperforming reactive baselines on cost-effectiveness.
Framing LLM agent loops as a Context Gathering Decision Process POMDP yields a predicate-based belief state that boosts multi-hop reasoning up to 11.4% and an exhaustion gate that cuts token use up to 39% with no performance loss.
SCOUT achieves state-of-the-art long-text understanding with up to 8x lower token use by actively foraging for sparse query-relevant information and updating a compact provenance-grounded epistemic state.
MemFlow routes queries by intent to tiered memory operations, nearly doubling accuracy of a 1.7B SLM on long-horizon benchmarks compared to full-context baselines.
AdaGATE improves evidence F1 scores on HotpotQA for multi-hop RAG under clean, redundant, and noisy conditions by framing selection as gap-aware token-constrained repair, outperforming baselines while using 2.6x fewer tokens.
HaS accelerates RAG retrieval via homology-aware speculative retrieval and homologous query re-identification validation, cutting latency 24-37% with 1-2% accuracy drop on tested datasets.
ArbGraph resolves conflicts in RAG evidence by constructing a conflict-aware graph of atomic claims and applying intensity-driven iterative arbitration to suppress unreliable claims prior to generation.
IG-Search computes step-level information gain rewards from policy probabilities to improve credit assignment in RL training for search-augmented QA, yielding 1.6-point gains over trajectory-level baselines on multi-hop tasks.
Credo proposes representing LLM agent state as beliefs and regulating pipeline behavior with declarative policies stored in a database for adaptive, auditable control.
RegReAct deploys self-correcting multi-agent pipelines across seven stages to extract hierarchical compliance criteria from regulatory texts, outperforming single-pass GPT-4o on EU Taxonomy documents.
RAR retrieves candidate items from a 300k-movie corpus then uses LLM generation with RL feedback to produce context-aware recommendations that outperform baselines on benchmarks.
GDP-RAG targets only information deltas in multi-hop RAG through preliminary grounding, gap-conditioned prompts, and skeletal trajectories, reaching 60.63% accuracy at 0.51 cost-of-pass on HotpotQA, 2WikiMultiHopQA, and MuSiQue.
MACR adaptively assesses LLM confidence via semantic entropy then applies inductive multi-agent reasoning with rule-induction, conflict-analysis, and resolution agents to handle unreliable parametric and contextual knowledge.
REVEAL reformulates multimodal manipulation detection as reference-grounded verification using a 170K-pair authentic library, difference-aware fusion, and task-decoupled MoE for joint detection and localization with training-free domain adaptation.
MemCog introduces a Memory-as-Cognition paradigm with Navigable Memory Store, Cross-Dimensional Navigation Interface, and Proactive Reasoning Protocol, claiming SOTA results on LoCoMo, LongMemEval, and a new ProactiveMemBench.
BELIEF improves closed-set biomedical QA by converting documents to structured evidence objects and fusing D-S symbolic belief estimation with LLM inference through reliability-aware arbitration.
PiCA uses pivot-based potential rewards derived from historical sub-queries to supply trajectory-aware step guidance in agentic RL, delivering 15% gains on QA benchmarks for 3B/7B models.
FinAgent-RAG achieves 76.81-78.46% execution accuracy on financial QA benchmarks by combining contrastive retrieval, program-of-thought code generation, and adaptive strategy routing, outperforming baselines by 5.62-9.32 points.
CAR reranks documents in RAG by promoting those that increase generator confidence (via answer consistency sampling) and demoting those that decrease it, yielding NDCG@5 gains on BEIR datasets that correlate with F1 improvements.
EviMem improves accuracy on temporal and multi-hop questions in long-term conversational memory by iteratively diagnosing and filling evidence gaps, achieving 81.6% and 85.2% judge accuracy on LoCoMo at 4.5x lower latency than MIRIX.
Faithfulness-QA is a 99k-sample dataset created via counterfactual entity substitution on existing QA benchmarks to train and evaluate context-faithful RAG models.
An agentic multi-source grounding system for marketplace query intent achieves 90.7% accuracy on long-tail queries at DoorDash by combining catalog grounding, web search, and deterministic disambiguation, outperforming baselines by up to 13pp.
citing papers explorer
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X-SYNTH: Beyond Retrieval -- Enterprise Context Synthesis from Observed Digital Human Attention
X-SYNTH synthesizes enterprise context from digital human attention using Digital Twin Signatures and seven attention filters, raising true lead rate from 9.5% to 61.9% while cutting false lead rate to 18.8%.
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EvolveMem:Self-Evolving Memory Architecture via AutoResearch for LLM Agents
EvolveMem enables autonomous self-evolution of LLM memory retrieval configurations via LLM diagnosis and safeguards, delivering 25.7% gains over strong baselines on LoCoMo and 18.9% on MemBench with positive cross-benchmark transfer.
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Route Before Retrieve: Activating Latent Routing Abilities of LLMs for RAG vs. Long-Context Selection
Pre-Route elicits LLMs' latent routing skills via structured prompts on metadata to proactively choose RAG or long-context, outperforming reactive baselines on cost-effectiveness.
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The Context Gathering Decision Process: A POMDP Framework for Agentic Search
Framing LLM agent loops as a Context Gathering Decision Process POMDP yields a predicate-based belief state that boosts multi-hop reasoning up to 11.4% and an exhaustion gate that cuts token use up to 39% with no performance loss.
-
SCOUT: Active Information Foraging for Long-Text Understanding with Decoupled Epistemic States
SCOUT achieves state-of-the-art long-text understanding with up to 8x lower token use by actively foraging for sparse query-relevant information and updating a compact provenance-grounded epistemic state.
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MemFlow: Intent-Driven Memory Orchestration for Small Language Model Agents
MemFlow routes queries by intent to tiered memory operations, nearly doubling accuracy of a 1.7B SLM on long-horizon benchmarks compared to full-context baselines.
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AdaGATE: Adaptive Gap-Aware Token-Efficient Evidence Assembly for Multi-Hop Retrieval-Augmented Generation
AdaGATE improves evidence F1 scores on HotpotQA for multi-hop RAG under clean, redundant, and noisy conditions by framing selection as gap-aware token-constrained repair, outperforming baselines while using 2.6x fewer tokens.
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HaS: Accelerating RAG through Homology-Aware Speculative Retrieval
HaS accelerates RAG retrieval via homology-aware speculative retrieval and homologous query re-identification validation, cutting latency 24-37% with 1-2% accuracy drop on tested datasets.
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ArbGraph: Conflict-Aware Evidence Arbitration for Reliable Long-Form Retrieval-Augmented Generation
ArbGraph resolves conflicts in RAG evidence by constructing a conflict-aware graph of atomic claims and applying intensity-driven iterative arbitration to suppress unreliable claims prior to generation.
-
IG-Search: Step-Level Information Gain Rewards for Search-Augmented Reasoning
IG-Search computes step-level information gain rewards from policy probabilities to improve credit assignment in RL training for search-augmented QA, yielding 1.6-point gains over trajectory-level baselines on multi-hop tasks.
-
Credo: Declarative Control of LLM Pipelines via Beliefs and Policies
Credo proposes representing LLM agent state as beliefs and regulating pipeline behavior with declarative policies stored in a database for adaptive, auditable control.
-
REGREACT: Self-Correcting Multi-Agent Pipelines for Structured Regulatory Information Extraction
RegReAct deploys self-correcting multi-agent pipelines across seven stages to extract hierarchical compliance criteria from regulatory texts, outperforming single-pass GPT-4o on EU Taxonomy documents.
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Retrieval Augmented Conversational Recommendation with Reinforcement Learning
RAR retrieves candidate items from a 300k-movie corpus then uses LLM generation with RL feedback to produce context-aware recommendations that outperform baselines on benchmarks.
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Only Ask What You Don't Know: Grounded Delta Planning for Efficient Multi-step RAG
GDP-RAG targets only information deltas in multi-hop RAG through preliminary grounding, gap-conditioned prompts, and skeletal trajectories, reaching 60.63% accuracy at 0.51 cost-of-pass on HotpotQA, 2WikiMultiHopQA, and MuSiQue.
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Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference
MACR adaptively assesses LLM confidence via semantic entropy then applies inductive multi-agent reasoning with rule-induction, conflict-analysis, and resolution agents to handle unreliable parametric and contextual knowledge.
-
REVEAL: Reference-Grounded Reasoning for Multimodal Manipulation Detection
REVEAL reformulates multimodal manipulation detection as reference-grounded verification using a 170K-pair authentic library, difference-aware fusion, and task-decoupled MoE for joint detection and localization with training-free domain adaptation.
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MemCog: From Memory-as-Tool to Memory-as-Cognition in Conversational Agents
MemCog introduces a Memory-as-Cognition paradigm with Navigable Memory Store, Cross-Dimensional Navigation Interface, and Proactive Reasoning Protocol, claiming SOTA results on LoCoMo, LongMemEval, and a new ProactiveMemBench.
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BELIEF: Structured Evidence Modeling and Uncertainty-Aware Fusion for Biomedical Question Answering
BELIEF improves closed-set biomedical QA by converting documents to structured evidence objects and fusing D-S symbolic belief estimation with LLM inference through reliability-aware arbitration.
-
PiCA: Pivot-Based Credit Assignment for Search Agentic Reinforcement Learning
PiCA uses pivot-based potential rewards derived from historical sub-queries to supply trajectory-aware step guidance in agentic RL, delivering 15% gains on QA benchmarks for 3B/7B models.
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Agentic Retrieval-Augmented Generation for Financial Document Question Answering
FinAgent-RAG achieves 76.81-78.46% execution accuracy on financial QA benchmarks by combining contrastive retrieval, program-of-thought code generation, and adaptive strategy routing, outperforming baselines by 5.62-9.32 points.
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CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation
CAR reranks documents in RAG by promoting those that increase generator confidence (via answer consistency sampling) and demoting those that decrease it, yielding NDCG@5 gains on BEIR datasets that correlate with F1 improvements.
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EviMem: Evidence-Gap-Driven Iterative Retrieval for Long-Term Conversational Memory
EviMem improves accuracy on temporal and multi-hop questions in long-term conversational memory by iteratively diagnosing and filling evidence gaps, achieving 81.6% and 85.2% judge accuracy on LoCoMo at 4.5x lower latency than MIRIX.
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Faithfulness-QA: A Counterfactual Entity Substitution Dataset for Training Context-Faithful RAG Models
Faithfulness-QA is a 99k-sample dataset created via counterfactual entity substitution on existing QA benchmarks to train and evaluate context-faithful RAG models.
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Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study
An agentic multi-source grounding system for marketplace query intent achieves 90.7% accuracy on long-tail queries at DoorDash by combining catalog grounding, web search, and deterministic disambiguation, outperforming baselines by up to 13pp.
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Towards Effective Long Video Understanding of Multimodal Large Language Models via One-shot Clip Retrieval
OneClip-RAG enables MLLMs to handle long videos via one-shot clip retrieval and unified chunking-retrieval, delivering performance gains like matching GPT-5 level on MLVU with high efficiency on standard GPUs.
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ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
ReSearch trains LLMs via RL to integrate search operations into reasoning steps, achieving strong generalization across benchmarks and eliciting reflection and self-correction without supervised reasoning data.
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Retrieval-Augmented Generation for Natural Language Processing: A Survey
The survey organizes RAG methods via a taxonomy of query-based, logits-based, latent, and parametric fusion with comparisons on accessibility, efficiency, applications, and challenges.
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CRITIC-R1: Learning Structured Critics for Retrieval-Augmented Generation
CRITIC-R1 learns structured RAG critics via GRPO RL with Conservative Judgement Alignment and Diagnostic Quality Alignment rewards, reporting gains on five QA benchmarks.
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CRAFT: Critic-Refined Adaptive Key-Frame Targeting for Multimodal Video Question Answering
CRAFT introduces a query-conditioned pipeline with dynamic keyframe selection, ASR, and a hybrid critic loop that achieves top scores on MAGMaR 2026 for grounded multi-video question answering.
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Vector RAG vs LLM-Compiled Wiki: A Preregistered Comparison on a Small Multi-Domain Research
A preregistered comparison on 24 papers found that an LLM-compiled wiki outperformed vector RAG on cross-document synthesis and citation accuracy but used more query tokens, with no system best across all metrics.
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AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
AgenticRAG equips an LLM with iterative retrieval and navigation tools, delivering 49.6% recall@1 on BRIGHT, 0.96 factuality on WixQA, and 92% correctness on FinanceBench.
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FinGround: Detecting and Grounding Financial Hallucinations via Atomic Claim Verification
FinGround reduces financial hallucinations by 68% over baselines in retrieval-equalized tests through atomic claim verification and grounding, with an 8B model retaining 91.4% F1 at low cost.
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STEM: Structure-Tracing Evidence Mining for Knowledge Graphs-Driven Retrieval-Augmented Generation
STEM reframes multi-hop KGQA as schema-guided graph search with semantic-to-structural projection and Triple-GNN guidance, claiming SOTA accuracy and evidence completeness on multi-hop benchmarks.
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A Control Architecture for Training-Free Memory Use
A training-free control architecture with uncertainty-based routing, confidence-selective acceptance, and evidence-based memory governance improves arithmetic reasoning by +7 points on SVAMP and ASDiv benchmarks.
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Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization
RePAIR improves agentic RAG performance by learning direct response-to-action mappings without taxonomy-based error categorization or explicit critic supervision.
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Adaptive Query Routing: A Tier-Based Framework for Hybrid Retrieval Across Financial, Legal, and Medical Documents
Tree reasoning outperforms vector search on complex document queries but a hybrid approach balances results across tiers, with validation showing an 11.7-point gap on real finance documents.
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Contradictions in Context: Challenges for Retrieval-Augmented Generation in Healthcare
Contradictions between highly similar medical abstracts degrade the factual accuracy and consistency of LLM responses in retrieval-augmented generation.
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Retrieval-Augmented Generation for AI-Generated Content: A Survey
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.
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BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discovery
BioInsight is a multi-agent system that generates interactive, provenance-preserving biomedical evidence interfaces from disease names and protein data.
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Token Economics for LLM Agents: A Dual-View Study from Computing and Economics
The paper delivers a unified survey of token economics for LLM agents, conceptualizing tokens as production factors, exchange mediums, and units of account across micro, meso, macro, and security dimensions using established economic theories.
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An Agent-Oriented Pluggable Experience-RAG Skill for Experience-Driven Retrieval Strategy Orchestration
Experience-RAG Skill is a reusable agent skill that selects retrieval strategies via experience memory, achieving 0.8924 nDCG@10 on BeIR/nq, hotpotqa, and scifact while outperforming fixed retriever baselines.
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Adaptive ToR: Complexity-Aware Tree-Based Retrieval for Pareto-Optimal Multi-Intent NLU
Adaptive ToR uses a query complexity classifier to route multi-intent queries to either fast single-step or deeper hierarchical retrieval, improving accuracy by 9.7% and cutting latency by 37.6% on NLU benchmarks.
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PDF Retrieval Augmented Question Answering
Develops a multimodal RAG QA system for PDFs by processing non-textual elements and fine-tuning LLMs to handle complex queries combining multiple data types.
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Retrieval-Augmented Generation for Large Language Models: A Survey
A survey of RAG paradigms, components, benchmarks, and challenges for improving LLMs on knowledge-intensive tasks.
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A Survey on Retrieval-Augmented Text Generation for Large Language Models
A survey that categorizes RAG methods for LLMs into four retrieval-centric stages, reviews their evolution and evaluation, and outlines challenges and future directions.
- ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation
- SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning
- Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict
- Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization
- Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routing