Search: multi-hop - ai.jp.net

Paper #LLM 🔬 ResearchAnalyzed: Jan 3, 2026 06:29

Multi-Agent Model for Complex Reasoning

Published:Dec 31, 2025 04:10

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1 min read

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ArXiv

Analysis

This paper addresses the limitations of single large language models in complex reasoning by proposing a multi-agent conversational model. The model's architecture, incorporating generation, verification, and integration agents, along with self-game mechanisms and retrieval enhancement, is a significant contribution. The focus on factual consistency and logical coherence, coupled with the use of a composite reward function and improved training strategy, suggests a robust approach to improving reasoning accuracy and consistency in complex tasks. The experimental results, showing substantial improvements on benchmark datasets, further validate the model's effectiveness.

Key Takeaways

Reference

“The model improves multi-hop reasoning accuracy by 16.8 percent on HotpotQA, 14.3 percent on 2WikiMultihopQA, and 19.2 percent on MeetingBank, while improving consistency by 21.5 percent.”

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Paper #LLM 🔬 ResearchAnalyzed: Jan 3, 2026 16:37

LLM for Tobacco Pest Control with Graph Integration

Published:Dec 26, 2025 02:48

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1 min read

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ArXiv

Analysis

This paper addresses a practical problem (tobacco pest and disease control) by leveraging the power of Large Language Models (LLMs) and integrating them with graph-structured knowledge. The use of GraphRAG and GNNs to enhance knowledge retrieval and reasoning is a key contribution. The focus on a specific domain and the demonstration of improved performance over baselines suggests a valuable application of LLMs in specialized fields.

Key Takeaways

•Combines LLMs with graph-structured knowledge for domain-specific problem solving.
•Employs GraphRAG and GNNs for enhanced knowledge retrieval and reasoning.
•Demonstrates improved performance over baseline methods in tobacco pest and disease control.
•Utilizes a ChatGLM-based model with LoRA for parameter-efficient adaptation.

Reference

“The proposed approach consistently outperforms baseline methods across multiple evaluation metrics, significantly improving both the accuracy and depth of reasoning, particularly in complex multi-hop and comparative reasoning scenarios.”

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Research #llm 🔬 ResearchAnalyzed: Dec 25, 2025 02:34

M$^3$KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation

Published:Dec 24, 2025 05:00

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1 min read

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ArXiv NLP

Analysis

This paper introduces M$^3$KG-RAG, a novel approach to Retrieval-Augmented Generation (RAG) that leverages multi-hop multimodal knowledge graphs (MMKGs) to enhance the reasoning and grounding capabilities of multimodal large language models (MLLMs). The key innovations include a multi-agent pipeline for constructing multi-hop MMKGs and a GRASP (Grounded Retrieval And Selective Pruning) mechanism for precise entity grounding and redundant context pruning. The paper addresses limitations in existing multimodal RAG systems, particularly in modality coverage, multi-hop connectivity, and the filtering of irrelevant knowledge. The experimental results demonstrate significant improvements in MLLMs' performance across various multimodal benchmarks, suggesting the effectiveness of the proposed approach in enhancing multimodal reasoning and grounding.

Key Takeaways

•Introduces M$^3$KG-RAG for enhanced multimodal RAG.
•Utilizes multi-hop MMKGs to improve reasoning depth.
•Employs GRASP for precise entity grounding and context pruning.

Reference

“To address these limitations, we propose M$^3$KG-RAG, a Multi-hop Multimodal Knowledge Graph-enhanced RAG that retrieves query-aligned audio-visual knowledge from MMKGs, improving reasoning depth and answer faithfulness in MLLMs.”

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Research #Reasoning 🔬 ResearchAnalyzed: Jan 10, 2026 08:13

Accelerating Multi-hop Reasoning with Early Knowledge Alignment

Published:Dec 23, 2025 08:14

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1 min read

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ArXiv

Analysis

The research focuses on enhancing multi-hop reasoning in AI, a critical area for complex question answering and knowledge extraction. Early knowledge alignment shows promise in improving efficiency and accuracy in these tasks, as it addresses a core challenge in knowledge-intensive AI applications.

Key Takeaways

•Addresses the challenge of efficient multi-hop reasoning.
•Proposes a novel technique of early knowledge alignment.
•Potentially improves accuracy and efficiency in knowledge-intensive AI.

Reference

“The research is sourced from ArXiv, indicating a potential for further peer review and validation.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 09:56

M$^3$KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation

Published:Dec 23, 2025 07:54

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1 min read

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ArXiv

Analysis

The article introduces M$^3$KG-RAG, a system that combines multi-hop reasoning, multimodal data, and knowledge graphs to improve retrieval-augmented generation (RAG) for language models. The focus is on enhancing the accuracy and relevance of generated text by leveraging structured knowledge and diverse data types. The use of multi-hop reasoning suggests an attempt to address complex queries that require multiple steps of inference. The integration of multimodal data (likely images, audio, etc.) indicates a move towards more comprehensive and contextually rich information retrieval. The paper likely details the architecture, training methodology, and evaluation metrics of the system.

Key Takeaways

•M$^3$KG-RAG is a system for improving Retrieval-Augmented Generation (RAG).
•It uses multi-hop reasoning, multimodal data, and knowledge graphs.
•The goal is to enhance the accuracy and relevance of generated text.

Reference

“The paper likely details the architecture, training methodology, and evaluation metrics of the system.”

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Research #Search Agent 🔬 ResearchAnalyzed: Jan 10, 2026 10:10

ToolForge: Synthetic Data Pipeline for Advanced AI Search

Published:Dec 18, 2025 04:06

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1 min read

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ArXiv

Analysis

This research from ArXiv presents ToolForge, a novel data synthesis pipeline designed to enable multi-hop search capabilities without reliance on real-world APIs. The approach has potential for advancing AI research by providing a controlled environment for training and evaluating search agents.

Key Takeaways

•ToolForge provides a synthetic data environment for multi-hop search.
•The pipeline aims to eliminate the need for real-world APIs in the search process.
•This research has implications for AI agent training and evaluation.

Reference

“ToolForge is a data synthesis pipeline for multi-hop search without real-world APIs.”

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Research #LLM 🔬 ResearchAnalyzed: Jan 10, 2026 10:45

Document Packing Impacts LLMs' Multi-Hop Reasoning

Published:Dec 16, 2025 14:16

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1 min read

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ArXiv

Analysis

This ArXiv paper likely explores how different document organization strategies affect the ability of Large Language Models (LLMs) to perform multi-hop reasoning. The research offers insights into optimizing input formatting for improved performance on complex reasoning tasks.

Key Takeaways

•Focuses on how document structure impacts LLM reasoning.
•Investigates multi-hop reasoning capabilities.
•Potentially reveals ways to improve LLM performance.

Reference

“The study investigates the effect of document packing.”

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Research #Reasoning 🔬 ResearchAnalyzed: Jan 10, 2026 11:03

MMhops-R1: Advancing Multimodal Multi-hop Reasoning

Published:Dec 15, 2025 17:29

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1 min read

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ArXiv

Analysis

The article introduces MMhops-R1, which focuses on multimodal multi-hop reasoning. Further analysis of the paper would be needed to assess the novelty and the potential impact of the research in the field.

Key Takeaways

•MMhops-R1 focuses on multimodal reasoning, which integrates multiple data types.
•The research likely targets complex question-answering scenarios.
•The paper is available on ArXiv providing access to the technical details.

Reference

“The article is sourced from ArXiv.”

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Research #RAG 🔬 ResearchAnalyzed: Jan 10, 2026 11:58

Fixed-Budget Evidence Assembly Improves Multi-Hop RAG Systems

Published:Dec 11, 2025 16:31

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1 min read

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ArXiv

Analysis

This research paper from ArXiv explores a method to mitigate context dilution in multi-hop Retrieval-Augmented Generation (RAG) systems. The proposed approach, 'Fixed-Budget Evidence Assembly', likely focuses on optimizing the evidence selection process to maintain high relevance within resource constraints.

Key Takeaways

•Addresses context dilution, a common problem in multi-hop RAG.
•Proposes a 'Fixed-Budget Evidence Assembly' approach.
•Published on ArXiv, indicating it is a research paper.

Reference

“The context itself does not provide enough specific information to extract a key fact. Further analysis is needed.”

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Research #Agent 🔬 ResearchAnalyzed: Jan 10, 2026 13:01

Trust-Based Agent Selection: A GNN Approach for Multi-Hop Collaboration in AI

Published:Dec 5, 2025 15:16

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1 min read

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ArXiv

Analysis

This research explores a crucial aspect of multi-agent systems: establishing trust for effective collaboration. The use of Graph Neural Networks (GNNs) for task-specific trust evaluation in a distributed agentic AI framework is a promising direction.

Key Takeaways

•Proposes a GNN-aided approach for evaluating and establishing trust in multi-agent systems.
•Addresses the challenge of collaborator selection in complex, multi-hop collaborative tasks.
•Focuses on task-specific trust, potentially leading to more reliable and efficient AI agent interactions.

Reference

“The research focuses on task-specific trust evaluation within a multi-hop collaborator selection process.”

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Research #QA 🔬 ResearchAnalyzed: Jan 10, 2026 13:06

PathFinder: A Novel Approach for Multi-Hop Question Answering Using LLM Feedback and MCTS

Published:Dec 5, 2025 00:33

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1 min read

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ArXiv

Analysis

This research explores a new method for improving multi-hop question answering by combining Monte Carlo Tree Search (MCTS) with feedback from a Large Language Model (LLM). The paper likely demonstrates a potentially significant advancement in the field by leveraging the strengths of both search and language modeling.

Key Takeaways

•The core innovation lies in the integration of MCTS for path selection.
•The system uses LLM feedback to guide the search process of MCTS, improving accuracy.
•This approach targets improving multi-hop question answering performance.

Reference

“PathFinder utilizes MCTS and LLM feedback for multi-hop question answering.”

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Research #Video AI 🔬 ResearchAnalyzed: Jan 10, 2026 13:46

Automated Video Workload Construction via Knowledge Graph Traversal

Published:Nov 30, 2025 19:24

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1 min read

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ArXiv

Analysis

This research paper, published on ArXiv, introduces Med-CRAFT, a system designed to automatically construct video workloads. The use of knowledge graph traversal for interpreting and generating multi-hop video tasks is a novel approach.

Key Takeaways

•Med-CRAFT automates the creation of video workloads.
•It uses knowledge graph traversal for interpretation.
•The system focuses on multi-hop video tasks.

Reference

“Med-CRAFT leverages knowledge graph traversal.”

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Research #VLM 🔬 ResearchAnalyzed: Jan 10, 2026 14:18

Enhancing Factual Accuracy in Vision-Language Models with Multi-Hop Reasoning

Published:Nov 25, 2025 17:34

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1 min read

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ArXiv

Analysis

This ArXiv paper explores the use of multi-hop reasoning to improve the factual accuracy of Vision-Language Models, a critical area for trustworthy AI. The research likely offers insights into enhancing model performance in tasks requiring complex inference across visual and textual data.

Key Takeaways

•Investigates the use of multi-hop reasoning.
•Aims to improve factual accuracy in Vision-Language Models.
•Appears to be a research paper.

Reference

“The paper focuses on multi-hop reasoning within Vision-Language Models.”

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Research #Knowledge Graph 🔬 ResearchAnalyzed: Jan 10, 2026 14:21

LLM-Powered Question Answering on Knowledge Graphs: Efficiency Through Planning and Embedding Search

Published:Nov 24, 2025 19:27

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1 min read

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ArXiv

Analysis

This research addresses a critical challenge in knowledge graph question answering: efficient multi-hop reasoning. The proposed method, leveraging LLM planning and embedding-guided search, likely offers improved performance and scalability.

Key Takeaways

•Addresses the challenge of efficient multi-hop question answering.
•Employs LLM planning for improved reasoning.
•Utilizes embedding-guided search for efficient information retrieval.

Reference

“The paper focuses on efficient multi-hop question answering over knowledge graphs.”

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Multi-Agent Model for Complex Reasoning

Analysis

Key Takeaways

LLM for Tobacco Pest Control with Graph Integration

Analysis

Key Takeaways

M$^3$KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation

Analysis

Key Takeaways

Accelerating Multi-hop Reasoning with Early Knowledge Alignment

Analysis

Key Takeaways

M$^3$KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation

Analysis

Key Takeaways

ToolForge: Synthetic Data Pipeline for Advanced AI Search

Analysis

Key Takeaways

Document Packing Impacts LLMs' Multi-Hop Reasoning

Analysis

Key Takeaways

MMhops-R1: Advancing Multimodal Multi-hop Reasoning

Analysis

Key Takeaways

Fixed-Budget Evidence Assembly Improves Multi-Hop RAG Systems

Analysis

Key Takeaways

Trust-Based Agent Selection: A GNN Approach for Multi-Hop Collaboration in AI

Analysis

Key Takeaways

PathFinder: A Novel Approach for Multi-Hop Question Answering Using LLM Feedback and MCTS

Analysis

Key Takeaways

Automated Video Workload Construction via Knowledge Graph Traversal

Analysis

Key Takeaways

Enhancing Factual Accuracy in Vision-Language Models with Multi-Hop Reasoning

Analysis

Key Takeaways

LLM-Powered Question Answering on Knowledge Graphs: Efficiency Through Planning and Embedding Search

Analysis

Key Takeaways

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