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MolBioKG is a two-layer system that grounds unseen molecules in biomedical knowledge graphs via multi-resolution structural anchoring, enabling out-of-graph link recovery and multi-hop reasoning from SMILES strings. It significantly improves Hits@10 and out-of-graph target recall over baselines while maintaining traceable evidence.
This paper introduces D2F-ReAG, a novel paradigm for multi-hop reasoning-augmented generation that dynamically controls reasoning depth by judging root-level reasoning reliability and decomposing questions into sub-questions when needed, improving accuracy on multi-hop benchmarks.
This paper introduces a new benchmark for evaluating machine unlearning, focusing on multi-hop reasoning consistency and recovery robustness. Experiments show existing unlearning methods face an 'impossible triangle' trade-off among forget quality, robustness, and utility preservation.
Introduces Multimodal CoLRAG-TF, a four-axis fusion architecture for retrieval-augmented generation over complex PDFs, integrating dense text, BM25, knowledge-graph triple filtering, and image similarity. Achieves significant improvements in multi-hop reasoning on a benchmark of Japanese disaster lesson PDFs.
Telco-GAIA is a bilingual, multi-modal benchmark for evaluating tool-using agents in the telecom domain, comprising 100 human-verified tasks requiring multi-hop reasoning over heterogeneous sources, with objective scoring via exact string matching.
This paper presents Search-on-Graph-R1 (SoG-R1), which trains an 8B LLM to navigate knowledge graphs by first scaffolding a frontier teacher with gold SPARQL queries to produce grounded trajectories, then applying supervised fine-tuning and reinforcement learning. The compact model surpasses frozen frontier systems on WebQSP, CWQ, and GrailQA, notably achieving the best results on CWQ among compared methods.
DeLIVeR is a framework that uses a reinforced planner LLM to decompose claims into question sets for structured knowledge graph traversal, improving fact-checking accuracy over static RAG baselines by 10-15% on benchmark datasets.
Introduces GRASP, a reinforcement learning framework that trains agents to adaptively coordinate semantic search, keyword search, and paragraph reading during multi-step reasoning, improving retrieval recall and question answering performance on multi-hop benchmarks.
This paper introduces RSF-GLLM, a framework that decouples differentiable graph reasoning from LLM generation to address the semantic gap in multi-hop knowledge graph question answering, achieving competitive performance with superior inference efficiency.
A developer reflects on when GraphRAG is preferable to standard RAG for AI agents, noting it becomes valuable for multi-hop reasoning and relationship understanding, but adds significant complexity.
DiscoLoop introduces a looping architecture that carries both discrete embedding and continuous hidden-state channels to improve multi-hop reasoning in transformers, achieving near-perfect accuracy on synthetic tasks and stronger performance on real-world language modeling.
The Looped Transformer achieves internal reasoning by designing recursion directly into the architecture, avoiding the inefficiency of chain-of-thought having to simulate iteration by generating discrete tokens. Latest research shows it performs excellently on multi-hop reasoning, and can be further improved through stabilization techniques and adaptive recursion.
This paper explores grounding multi-hop textual-spatial stories into geometry-aware modalities like grids, showing a 42% performance improvement when switching from language-only to grid-based reasoning, and introduces a switching metric for modality selection in LLMs.
This paper frames regulatory document review as an LLM-guided planning problem, using a vectorless document tree with browse, read, and search tools and a dynamic knowledge graph as state. On a 200-question benchmark over NuScale FSAR documents, the system achieves 81.5% accuracy with 0.93 RAGAS Faithfulness, significantly outperforming existing RAG methods.
Introduces SAG (SQL-Retrieval Augmented Generation), a novel retrieval-augmented generation architecture based on SQL dynamic hyperedges. It is more efficient and lower cost for multi-hop reasoning compared to traditional RAG and GraphRAG. It is open-sourced on GitHub and has achieved good evaluation results.
This paper proposes HyGRAG, a hierarchical graph RAG framework that integrates contextual and relational information for multi-hop reasoning, achieving a 9.7% average accuracy improvement over existing methods.
An open-source full-stack pipeline that constructs a Knowledge Graph from raw text, uses hybrid search (dense + sparse + graph traversal) to solve multi-hop reasoning problems in LLMs, and re-ranks results with Reciprocal Rank Fusion and a Cross-Encoder.
The article argues that knowledge graphs and vector databases serve different purposes in enterprise AI and should be used together rather than as alternatives. It recommends hybrid architectures or managed solutions like 60x to handle both semantic recall and structural reasoning.
This paper investigates how TMK-based question generation strategies affect dataset quality for procedural and multi-hop reasoning in AI learning systems, comparing strict TMK generation, transcript-first generation, and TMK-aware generation, and introduces a grounding validation framework.
The paper proposes SVoT, a reinforcement learning framework that generates interleaved, verifiable intermediate states and visualizations for multi-hop spatial reasoning in MLLMs, achieving significant accuracy gains on new benchmarks involving multi-object interactions and numerical reasoning.