HighlightCurated by Aramai EditorialarXiv

Skill Retrieval Augmentation for Agentic AI

A paper proposing a paradigm for dynamically retrieving and incorporating skills in large language models.

The authors introduce Skill Retrieval Augmentation (SRA), a method for agents to retrieve relevant skills from external corpora on demand. They construct a benchmark, SRA-Bench, to evaluate the full SRA pipeline. The paper shows that retrieval-based skill augmentation can improve agent performance and highlights the need for more efficient skill incorporation.

Based on: Skill Retrieval Augmentation for Agentic AI · arXiv

HighlightCurated by Aramai EditorialarXiv

Separating Semantic Competition from Context Length in RAG Reading

Paper on evaluating Retrieval-Augmented Generation (RAG) systems.

The authors introduce a matched-control protocol to isolate the effect of semantic competition on RAG reading performance. They apply this protocol to two compact open models on SQuAD and report improvements in F1, answer inclusion, and exact match scores. The results suggest that the competition effect is distinct from context length.

Based on: Separating Semantic Competition from Context Length in RAG Reading · arXiv

HighlightCurated by Aramai EditorialarXiv

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

HighlightCurated by Aramai EditorialarXiv

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

HighlightCurated by Aramai EditorialarXiv

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

HighlightCurated by Aramai EditorialarXiv

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

HighlightCurated by Aramai EditorialarXiv

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

HighlightCurated by Aramai EditorialJournal 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

HighlightCurated by Aramai EditorialProceedings of the AAAI Conference on Artificial Intelligence

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

HighlightCurated by Aramai EditorialComputer Science Review

From vectors to knowledge graphs: A comprehensive analysis of modern retrieval-augmented generation architectures

A study on modern retrieval-augmented generation architectures.

The paper analyzes the evolution of retrieval-augmented generation (RAG) models from vector-based representations to knowledge graph-based ones. It provides a comprehensive overview of the current state-of-the-art in RAG architectures and their applications. The authors discuss the benefits and limitations of using knowledge graphs in RAG models.

Based on: From vectors to knowledge graphs: A comprehensive analysis of modern retrieval-augmented generation architectures · Computer Science Review

HighlightCurated by Aramai EditorialarXiv

Peak-Then-Collapse and the Four Interface Channels of Knowledge-Graph Tool Use

A study on the performance of a knowledge-graph tool use recipe with large language models.

The authors test a standard recipe for using knowledge graphs with large language models, observing a 'peak-then-collapse' pattern in performance. They identify four recurring failure modes and argue that interface feedback is a key difference from other tools. The study also explores the effect of self-distillation as a mitigation strategy.

Based on: Peak-Then-Collapse and the Four Interface Channels of Knowledge-Graph Tool Use · arXiv

HighlightCurated by Aramai EditorialarXiv

GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases

A plan-guided graph retrieval method for semi-structured knowledge bases.

The paper proposes GRASP, a plan-guided graph retrieval system that uses adaptive fusion and reranking to improve the accuracy of retrieving relevant information from semi-structured knowledge bases. The approach combines a planning module with a retrieval module to adaptively select relevant subgraphs and fuse their representations. This allows for more accurate and efficient retrieval of relevant information.

Based on: GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases · arXiv