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A benchmark comparing 18 RAG pipeline variants against a simple agent loop on Google's FRAMES multi-hop questions finds the best pipeline hits 78.9% while an agentic loop that can re-search reaches 92.7%, with surprising results showing reranking often hurts accuracy. The authors also note models sometimes fill gaps from memory despite instructions to stick to retrieved documents.
This paper introduces TRIAD, a three-stage automated approach to generate validated datasets for evaluating multi-hop RAG systems in domain-specific settings, assessed against established benchmarks.
A technical post explaining why RAG fails for multi-hop queries and presenting a 9-step roadmap for building context graphs, where entities are nodes and relationships are edges, with a minimal 150-line engine.
This paper presents a triple-robustness analysis of RAG for multi-hop traceability, varying embedder, corpus, and judge across thousands of runs. It finds that over-citation is architecturally universal in GraphRAG but its faithfulness consequences are corpus-conditional, and LLM-as-judge faithfulness is fragile across retrieval states.
This paper shows that per-chunk verification fails for multi-hop RAG because no single chunk is sufficient, and proposes decomposition-based verification to repair it, demonstrating significant improvements across multiple datasets.
This paper identifies 'context dilution' as a key failure mode in language agents performing multi-hop reasoning and introduces SLEUTH, an epistemic working memory that explicitly tracks confirmed facts, active hypotheses, and open questions, showing consistent gains across benchmarks.
InductWave proposes a wavelet-based inductive embedding method for multi-hop logical query answering on knowledge graphs, achieving competitive performance with fewer message-passing layers.
MOTHRAG is a graph-free multi-hop RAG framework that matches the accuracy of graph-based systems like GraphRAG and HippoRAG on benchmarks, while avoiding costly graph rebuilds by using a dense index and query-time orchestration.
This paper introduces answer-in-context, a diagnostic metric for budget-constrained multi-hop RAG that measures whether the gold answer survives in the packed reader context, and proposes a submodular evidence packing method that improves over heuristics under specific conditions.
Proposes OPI, an ontology-guided framework for multi-hop knowledge graph question answering that leverages a relation-centric ontology graph for bidirectional retrieval and iterative refinement, achieving state-of-the-art results on multiple benchmarks.
FlowRAG proposes a novel semantic-aware retrieval framework that constructs a quad-level heterogeneous graph and uses frequency-aware weighted flow to extract explicit reasoning paths, achieving state-of-the-art performance on complex reasoning benchmarks.
The author describes using a knowledge graph extractor built with a Qwen model to generate challenging multi-hop QA pairs for evaluating agentic search systems.
This paper introduces EvoBrowseComp, a dynamic benchmark of 400 English and 400 Chinese complex questions that are synthesized via live-web traversal to evaluate search agents without test-set contamination, ensuring robustness against parametric memorization.
A comprehensive survey analyzing over 300 papers on LLM reasoning, presenting a taxonomy of reasoning paradigms including Chain-of-Thought, Multi-Hop, Mathematical, Commonsense, and others, along with common failure modes and research gaps.
Introduces a benchmark to evaluate how knowledge editing methods handle logical consequences of fact edits, revealing that existing approaches like ROME and FT accurately insert direct assertions but fail to propagate entailed knowledge, with a performance gap of up to 24%.
AdaGATE is a training-free evidence controller for multi-hop RAG that uses entity-centric gap tracking, micro-query generation, and utility-based selection to improve robustness under noisy retrieval, achieving state-of-the-art evidence F1 with fewer input tokens.