LLM Reasoning with Graphs

LLM: Large Language Model

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18 papers in the last four weeks, up 350% on the four weeks before. 0.2% of all new papers.

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UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
Jan 28, 2026Dobrik Georgiev, Kheeran K. Naidu, Alberto Cattaneo +3Multi-Hop QA LLM Reasoning with Graphs
Graphcore Research
DAGGER: Distractor-Aware Graph Generation for Executable Reasoning in Math Problems
Jan 11, 2026Zabir Al Nazi, Shubhashis Roy Dipta, Sudipta KarNumerical Reasoning in Language Models Mathematical Reasoning Benchmarks
University of California, Riverside · University of Maryland, Baltimore County · Oracle Health AI
SpatialThinker: Reinforcing Scene Graph-Grounded Spatial Reasoning via Dense Rewards
Nov 10, 2025Hunar Batra, Haoqin Tu, Hardy Chen +3Visual Question Answering Scene Graph Generation
University of Oxford · University of California, Santa Cruz
KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering
Nov 6, 2025Yuanning Cui, Zequn Sun, Wei Hu +1Multi-Hop QA LLM Grounding
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China · State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China · National Institute of Healthcare Data Science, Nanjing University, Nanjing 210093, China +2
Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering
Feb 17, 2025Runxuan Liu, Bei Luo, Jiaqi Li +5LLM Reasoning with Graphs Graph-Based Retrieval
Harbin Institute of Technology, Harbin, China · Beijing University of Posts and Telecommunications, Beijing, China · Joint Laboratory of HIT and iFLYTEK, Beijing, China +2
ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces
Date pendingJinu Lee, Shivam Agarwal, Amruta Parulekar +3LLM Reasoning with Graphs LLM Auditing
The Grainger College of Engineering University of Illinois Urbana-Champaign

Jan 28, 2026, cs.IRJ/K move · Enter open · S save

UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG

Dobrik Georgiev, Kheeran K. Naidu, Alberto Cattaneo, Federico Monti+2

Graphcore Research

Multi-Hop QA LLM Reasoning with Graphs Graph Retrieval-Augmented Generation Knowledge Graph Question Answering Knowledge Graphs

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Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon often known as hallucination). Retrieval augmented generation (RAG) tries to reduce factual errors by identifying information in a knowledge corpus and putting it in the context window of the model. While this approach is well-established for document-structured data, it is non-trivial to adapt it for Knowledge Graphs (KGs), especially for queries that require multi-node/multi-hop reasoning on graphs. We introduce UltRAG, a training-free KG-RAG recipe that combines LLM query generation, a fully inductive neural query executor, and LLM arbitration. This off-the-shelf composition achieves state-of-the-art results on Knowledge Graph Question Answering (KGQA) tasks without retraining the LLM or executor, while enabling language models to interface with Wikidata-scale graphs (116M entities, 1.6B relations) at comparable or lower costs. Our ablation studies indicate that these gains come from the full system design rather than from any single component.

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Latest papers 96

UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG

DAGGER: Distractor-Aware Graph Generation for Executable Reasoning in Math Problems

SpatialThinker: Reinforcing Scene Graph-Grounded Spatial Reasoning via Dense Rewards

KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering

Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering

ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces