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An Agent-Oriented Pluggable Experience-RAG Skill for Experience-Driven Retrieval Strategy Orchestration

A paper proposing an agent-oriented pluggable retrieval orchestration layer for experience-driven retrieval strategy selection.

The authors present Experience-RAG Skill, a layer positioned between the agent and retriever pool. It analyzes the scene, consults an experience memory, selects a retrieval strategy, and returns structured evidence to the agent. The proposed skill outperforms fixed single-retriever baselines on various tasks.

Based on: An Agent-Oriented Pluggable Experience-RAG Skill for Experience-Driven Retrieval Strategy Orchestration · arXiv

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Uncertainty-Aware Hybrid Retrieval for Long-Document RAG

A training-free hybrid retrieval framework for Retrieval Augmented Generation (RAG).

The authors propose Uncertainty-aware Multi-Granularity RAG (UMG-RAG), a hybrid retrieval framework that treats chunk granularity as query-specific reliability estimation. UMG-RAG uses existing dense and sparse retrievers as complementary experts across multiple chunk granularities, estimating reliability from distribution entropy and fusing candidates according to query-specific semantic, lexical, and granularity confidence.

Based on: Uncertainty-Aware Hybrid Retrieval for Long-Document RAG · arXiv

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CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning

A framework for improving the reliability of reasoning language models through error checking and correction.

CheckRLM proposes a framework to improve the reliability of reasoning language models by timely checking and correcting factual errors. It extracts claims from the reasoning chain, identifies inconsistencies, and performs minimal-cost corrections using external knowledge. The framework demonstrates strong capability in mitigating error accumulation in long-horizon reasoning with lower costs.

Based on: CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning · arXiv

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mcp-proto-okn: Natural-language access to open scientific knowledge graphs through the Model Context Protocol

A Python-based Model Context Protocol server for natural-language access to open scientific knowledge graphs.

The MCP Server Proto-OKN enables AI assistants to discover, inspect, and query scientific knowledge graphs through natural language. It provides various functions such as graph routing, schema inspection, and SPARQL execution. The server is implemented in Python using the FastMCP framework and is available on GitHub.

Based on: mcp-proto-okn: Natural-language access to open scientific knowledge graphs through the Model Context Protocol · arXiv

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SeedER: Seed-and-Expand Retrieval from Knowledge Graphs

A retrieval framework for knowledge graphs that leverages structure through iterative expansion.

The authors introduce SeedER, a retrieval framework for knowledge graphs that explicitly uses graph structure to improve efficiency and effectiveness.,SeedER first seeds a compact set of core nodes using lightweight dense and entity-based retrieval, then selectively expands this set via a learned policy.,This design enables efficient discovery of query-relevant nodes while controlling expansion cost.

Based on: SeedER: Seed-and-Expand Retrieval from Knowledge Graphs · arXiv

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DynaKRAG: A Unified Framework for Learnable Evidence Control in Multi-Hop Retrieval-Augmented Generation

A framework for learnable evidence control in multi-hop retrieval-augmented generation.

The paper introduces DynaKRAG, a unified framework that formulates multi-hop evidence acquisition as state-conditioned control over atomic evidence operations. It uses a learned controller to select the next operation and updates the evidence state accordingly. The authors evaluate DynaKRAG on several benchmarks and demonstrate its effectiveness in coordinating retrieval, diagnosis, and gap-directed acquisition.

Based on: DynaKRAG: A Unified Framework for Learnable Evidence Control in Multi-Hop Retrieval-Augmented Generation · arXiv

HighlightJournal of Manufacturing Systems

A Multi-Agent and synergistic Knowledge Graph retrieval-augmented generation framework for intelligent maintenance

This paper proposes a framework for intelligent maintenance using multi-agent and synergistic knowledge graph retrieval-augmented generation.

The authors present a framework that combines multi-agent systems with knowledge graph retrieval-augmented generation to improve intelligent maintenance. The framework is designed to enhance the efficiency and effectiveness of maintenance tasks by leveraging the strengths of both approaches. This work contributes to the development of more advanced maintenance systems that can adapt to complex environments.

Based on: A Multi-Agent and synergistic Knowledge Graph retrieval-augmented generation framework for intelligent maintenance · Journal of Manufacturing Systems

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Proof of Unlearning for Semantic Knowledge Bases in Large Language Models-Enabled Semantic Communication

A framework for efficiently and verifiably updating large language model-enabled semantic knowledge bases.

The authors propose a proof-of-unlearning framework for updating large language models (LLMs) used in semantic knowledge bases. The framework tracks the evolution of unlearning by measuring drifts in the LoRA adapter subspace. Experimental results demonstrate its effectiveness. This work addresses the challenge of removing outdated, malicious, or privacy-sensitive content from LLMs without retraining.

Based on: Proof of Unlearning for Semantic Knowledge Bases in Large Language Models-Enabled Semantic Communication · IEEE Communications Magazine

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TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs

A framework for resolving factual conflicts between LLMs' internal knowledge and external information using knowledge graphs.

This paper proposes TruthfulRAG, a framework that leverages knowledge graphs to resolve factual conflicts in RAG systems. It constructs KGs from retrieved content, identifies relevant knowledge through query-based graph retrieval, and employs entropy-based filtering mechanisms to mitigate inconsistencies. The authors claim that TruthfulRAG outperforms existing methods in resolving knowledge conflicts and improving the robustness of RAG systems.

Based on: TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation with Knowledge Graphs · Proceedings of the AAAI Conference on Artificial Intelligence

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Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models

A paper proposing a framework for automating cause-and-effect logic generation using knowledge graphs and large language models.

The authors present a semantic-AI framework that combines a knowledge graph with a constrained large language model to automate the generation of cause-and-effect logic. The framework builds on an established modular alignment ontology and demonstrates its application on a modular process plant. This approach aims to reduce manual effort in creating engineering specifications.

Based on: Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models · arXiv

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Travel-Oriented Reasoning Large Language Model via Domain-Specific Knowledge Graphs

A paper proposing a modular pipeline for building a travel-domain reasoning LLM grounded in an expert-designed knowledge graph.

The authors propose a modular pipeline to build a travel-domain reasoning large language model (LLM) using a domain-specific knowledge graph. The pipeline integrates a travel KG, bottom-up construction procedure, and supervised fine-tuning stage to embed domain knowledge into the LLM. The approach achieves high accuracy on a benchmark dataset.

Based on: Travel-Oriented Reasoning Large Language Model via Domain-Specific Knowledge Graphs · arXiv

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XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation

A framework that generates causally grounded explanations for GraphRAG systems.

The paper introduces XGRAG, a graph-native framework for explaining knowledge graph-based retrieval-augmented generation. It employs graph-based perturbation strategies to quantify the contribution of individual graph components on the model answer. The authors conduct experiments comparing XGRAG against an existing explainability baseline and evaluate its robustness across various question types and LLMs.

Based on: XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation · arXiv