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Multi-hop Reasoning

7 articles · Page 1 of 1
PaperAgent
PaperAgent
Jul 10, 2026 · Artificial Intelligence

A Deep Dive into QC‑MHM: Boosting Accuracy in Temporal Knowledge Graph Question Answering

The article analyzes the challenges of temporal KGQA, explains why prior models miss time constraints and multi‑hop reasoning, details the four‑module QC‑MHM framework that integrates time‑aware embeddings, question calibration, multi‑hop modeling, and dual‑channel answer prediction, and shows its state‑of‑the‑art performance and interpretability on benchmark datasets.

AAAI 2024Knowledge GraphMulti-hop Reasoning
0 likes · 9 min read
A Deep Dive into QC‑MHM: Boosting Accuracy in Temporal Knowledge Graph Question Answering
Architecture and Beyond
Architecture and Beyond
Jun 7, 2026 · Artificial Intelligence

From Fragmented Retrieval to Deep Reasoning: Reshaping AI Agent Knowledge Engines

The article analyzes why traditional RAG fails on complex, multi‑step enterprise queries, explains how GraphRAG introduces explicit entity‑relationship graphs to enable multi‑hop navigation, explainability, and temporal reasoning, and outlines practical architectures, lightweight and dynamic graph strategies, and trade‑offs for real‑world deployment.

AI agentsGraphRAGKnowledge Graph
0 likes · 26 min read
From Fragmented Retrieval to Deep Reasoning: Reshaping AI Agent Knowledge Engines
James' Growth Diary
James' Growth Diary
May 22, 2026 · Artificial Intelligence

Advanced Graph RAG with Neo4j: When Multi‑Hop Reasoning Beats Vector Search

This article explains why vector retrieval fails on multi‑hop reasoning, shows how Neo4j’s Cypher path traversal enables precise Graph RAG queries, outlines modeling best‑practices, demonstrates hybrid graph‑vector retrieval, compares Graph RAG with vector RAG, and lists common pitfalls to avoid.

CypherGraph RAGHybrid Retrieval
0 likes · 21 min read
Advanced Graph RAG with Neo4j: When Multi‑Hop Reasoning Beats Vector Search
James' Growth Diary
James' Growth Diary
May 12, 2026 · Artificial Intelligence

GraphRAG Deep Dive: Boost Multi‑Hop Reasoning Accuracy from 50% to 85% with Knowledge Graphs

This article explains why traditional vector RAG loses relational information, how GraphRAG reconstructs entity‑relationship triples into a knowledge graph, and provides step‑by‑step code, performance benchmarks, retrieval modes, and practical tips that raise multi‑hop reasoning accuracy from around 50% to 85%.

GraphRAGKnowledge GraphLangChain
0 likes · 14 min read
GraphRAG Deep Dive: Boost Multi‑Hop Reasoning Accuracy from 50% to 85% with Knowledge Graphs
PaperAgent
PaperAgent
Jan 27, 2026 · Artificial Intelligence

How Agentic‑R Boosts Multi‑Turn Retrieval for LLMs by 2–3 EM Points

This article analyzes the Agentic‑R framework, which upgrades traditional single‑hop Retrieval‑Augmented Generation by introducing dual‑perspective scoring and a bidirectional flywheel, resulting in 2–3 absolute EM improvements across seven QA datasets and a 10–15% reduction in search rounds.

Agentic SearchLLMMulti-hop Reasoning
0 likes · 6 min read
How Agentic‑R Boosts Multi‑Turn Retrieval for LLMs by 2–3 EM Points
DataFunSummit
DataFunSummit
Jul 26, 2022 · Artificial Intelligence

Multi-step Reasoning over Large-scale Knowledge Graphs: Query2Box and SMORE Framework

This talk presents recent advances in multi-step reasoning over large-scale, noisy knowledge graphs, introducing the Query2Box model that uses box embeddings for complex queries and the SMORE framework that enables efficient multi-hop inference on massive graphs through scalable query generation, embedding computation, and training pipelines.

AIKnowledge GraphLarge Scale
0 likes · 14 min read
Multi-step Reasoning over Large-scale Knowledge Graphs: Query2Box and SMORE Framework
DataFunSummit
DataFunSummit
Feb 25, 2022 · Artificial Intelligence

Knowledge Graph Representation and Reasoning Forum at DataFun Summit 2022

The DataFun Summit 2022 Knowledge Graph Forum, held on March 12, presents cutting‑edge research on knowledge graph representation learning, multi‑hop reasoning, temporal KG question answering, and their applications in finance and retail, featuring talks by leading experts from JD, Fourth Paradigm, Stanford, and Meituan.

AI ApplicationsKnowledge GraphMulti-hop Reasoning
0 likes · 9 min read
Knowledge Graph Representation and Reasoning Forum at DataFun Summit 2022