Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review

• URL: http://arxiv.org/abs/2508.05660v1
• Date: Wed, 30 Jul 2025 18:54:15 GMT
• Title: Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review
• Authors: Aditya Nagori, Ricardo Accorsi Casonatto, Ayush Gautam, Abhinav Manikantha Sai Cheruvu, Rishikesan Kamaleswaran,
• Abstract summary: We present an agentic approach that encapsulates the hybrid RAG pipeline within an autonomous agent.<n>Our pipeline ingests bibliometric open-access data from PubMed, arXiv, and Google Scholar APIs.<n>A Llama-3.3-70B agent selects GraphRAG (translating queries to Cypher for KG) or VectorRAG (combining sparse and dense retrieval with re-ranking)
• Score: 2.092154729589438
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The surge in scientific publications challenges traditional review methods, demanding tools that integrate structured metadata with full-text analysis. Hybrid Retrieval Augmented Generation (RAG) systems, combining graph queries with vector search offer promise but are typically static, rely on proprietary tools, and lack uncertainty estimates. We present an agentic approach that encapsulates the hybrid RAG pipeline within an autonomous agent capable of (1) dynamically selecting between GraphRAG and VectorRAG for each query, (2) adapting instruction-tuned generation in real time to researcher needs, and (3) quantifying uncertainty during inference. This dynamic orchestration improves relevance, reduces hallucinations, and promotes reproducibility. Our pipeline ingests bibliometric open-access data from PubMed, arXiv, and Google Scholar APIs, builds a Neo4j citation-based knowledge graph (KG), and embeds full-text PDFs into a FAISS vector store (VS) using the all-MiniLM-L6-v2 model. A Llama-3.3-70B agent selects GraphRAG (translating queries to Cypher for KG) or VectorRAG (combining sparse and dense retrieval with re-ranking). Instruction tuning refines domain-specific generation, and bootstrapped evaluation yields standard deviation for evaluation metrics. On synthetic benchmarks mimicking real-world queries, the Instruction-Tuned Agent with Direct Preference Optimization (DPO) outperforms the baseline, achieving a gain of 0.63 in VS Context Recall and a 0.56 gain in overall Context Precision. Additional gains include 0.24 in VS Faithfulness, 0.12 in both VS Precision and KG Answer Relevance, 0.11 in overall Faithfulness score, 0.05 in KG Context Recall, and 0.04 in both VS Answer Relevance and overall Precision. These results highlight the system's improved reasoning over heterogeneous sources and establish a scalable framework for autonomous, agentic scientific discovery.

Related papers

• NERFIFY: A Multi-Agent Framework for Turning NeRF Papers into Code [49.610331036334316]
  We introduce NERFIFY, a framework that reliably converts NeRF research papers into trainable Nerfstudio plugins.<n>Code, data and implementations will be publicly released.
  arXiv  Detail & Related papers  (2026-02-28T20:57:32Z)
• TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework [62.66056331998838]
  TeaRAG is a token-efficient agentic RAG framework capable of compressing both retrieval content and reasoning steps.<n>Our reward function evaluates the knowledge sufficiency by a knowledge matching mechanism, while penalizing excessive reasoning steps.
  arXiv  Detail & Related papers  (2025-11-07T16:08:34Z)
• Towards Global Retrieval Augmented Generation: A Benchmark for Corpus-Level Reasoning [50.27838512822097]
  We introduce GlobalQA, the first benchmark specifically designed to evaluate global RAG capabilities.<n>We propose GlobalRAG, a multi-tool collaborative framework that preserves structural coherence through chunk-level retrieval.<n>On the Qwen2.5-14B model, GlobalRAG achieves 6.63 F1 compared to the strongest baseline's 1.51 F1.
  arXiv  Detail & Related papers  (2025-10-30T07:29:14Z)
• HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis [55.2480439325792]
  HySemRAG is a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG)<n>System addresses limitations in existing RAG architectures through a multi-layered approach.
  arXiv  Detail & Related papers  (2025-08-01T20:30:42Z)
• PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning [57.89188317734747]
  PrismRAG trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages.<n>It instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without relying on extensive human engineered instructions.
  arXiv  Detail & Related papers  (2025-07-25T00:15:31Z)
• SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN [0.2812395851874055]
  We introduce SCoRE, a modular and cost-effective sentence-level relation extraction system.<n>SCoRE enables easy PLM switching, requires no finetuning, and adapts smoothly to diverse corpora and KGs.<n>We show that SCoRE matches or surpasses state-of-the-art methods while significantly reducing energy consumption.
  arXiv  Detail & Related papers  (2025-07-09T14:33:07Z)
• LLM-Based Emulation of the Radio Resource Control Layer: Towards AI-Native RAN Protocols [28.04609776570199]
  Large AI Models (LAMs) are key enablers of the AI-Native Air Interface (AI-AI)<n>This paper presents the first standards-compliant emulation of the Radio Resource Control layer using a decoder-only LAM.<n>Results demonstrate that LAMs, when augmented with protocol-aware reasoning, can directly orchestrate control-plane procedures.
  arXiv  Detail & Related papers  (2025-05-22T15:55:56Z)
• GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics [0.0]
  We present GNN-Suite, a framework for constructing and benchmarking Graph Neural Network (GNN) architectures in computational biology.<n>We demonstrate its utility in identifying cancer-driver genes by constructing molecular networks from protein-protein interaction (PPI) data.<n>Our results show that a common framework for implementing and evaluating GNN architectures aids in identifying not only the best model but also the most effective means of incorporating complementary data.
  arXiv  Detail & Related papers  (2025-05-15T21:14:30Z)
• Enhancing Domain-Specific Retrieval-Augmented Generation: Synthetic Data Generation and Evaluation using Reasoning Models [0.6827423171182154]
  Retrieval-Augmented Generation (RAG) systems face significant performance gaps when applied to technical domains.<n>We propose a framework combining granular evaluation metrics with synthetic data generation to optimize domain-specific RAG performance.<n>Our empirical analysis reveals critical insights: smaller chunks (less than 10 tokens) improve precision by 31-42%.
  arXiv  Detail & Related papers  (2025-02-21T06:38:57Z)
• TrustRAG: An Information Assistant with Retrieval Augmented Generation [73.84864898280719]
  TrustRAG is a novel framework that enhances acRAG from three perspectives: indexing, retrieval, and generation.<n>We open-source the TrustRAG framework and provide a demonstration studio designed for excerpt-based question answering tasks.
  arXiv  Detail & Related papers  (2025-02-19T13:45:27Z)
• How to Make LLMs Strong Node Classifiers? [70.14063765424012]
  Language Models (LMs) are challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs)<n>We propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art (SOTA) GNNs on node classification tasks.
  arXiv  Detail & Related papers  (2024-10-03T08:27:54Z)
• SFR-RAG: Towards Contextually Faithful LLMs [57.666165819196486]
  Retrieval Augmented Generation (RAG) is a paradigm that integrates external contextual information with large language models (LLMs) to enhance factual accuracy and relevance. We introduce SFR-RAG, a small LLM that is instruction-textual with an emphasis on context-grounded generation and hallucination. We also present ConBench, a new evaluation framework compiling multiple popular and diverse RAG benchmarks.
  arXiv  Detail & Related papers  (2024-09-16T01:08:18Z)
• RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs [60.38044044203333]
  Large language models (LLMs) typically utilize the top-k contexts from a retriever in retrieval-augmented generation (RAG) We propose a novel instruction fine-tuning framework RankRAG, which instruction-tunes a single LLM for the dual purpose of context ranking and answer generation in RAG. For generation, we compare our model with many strong baselines, including GPT-4-0613, GPT-4-turbo-2024-0409, and ChatQA-1.5, an open-sourced model with the state-of-the-art performance on RAG benchmarks.
  arXiv  Detail & Related papers  (2024-07-02T17:59:17Z)
• Adaptive Universal Generalized PageRank Graph Neural Network [36.850433364139924]
  Graph neural networks (GNNs) are designed to exploit both sources of evidence but they do not optimally trade-off their utility. We introduce a new Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR weights. GPR-GNN offers significant performance improvement compared to existing techniques on both synthetic and benchmark data.
  arXiv  Detail & Related papers  (2020-06-14T19:27:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.