The Budget AI Researcher and the Power of RAG Chains

• URL: http://arxiv.org/abs/2506.12317v1
• Date: Sat, 14 Jun 2025 02:40:35 GMT
• Title: The Budget AI Researcher and the Power of RAG Chains
• Authors: Franklin Lee, Tengfei Ma,
• Abstract summary: Current approaches to supporting research idea generation often rely on generic large language models (LLMs)<n>Our framework, The Budget AI Researcher, uses retrieval-augmented generation chains, vector databases, and topic-guided pairing to recombine concepts from hundreds of machine learning papers.<n>The system ingests papers from nine major AI conferences, which collectively span the vast subfields of machine learning, and organizes them into a hierarchical topic tree.
• Score: 4.797627592793464
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Navigating the vast and rapidly growing body of scientific literature is a formidable challenge for aspiring researchers. Current approaches to supporting research idea generation often rely on generic large language models (LLMs). While LLMs are effective at aiding comprehension and summarization, they often fall short in guiding users toward practical research ideas due to their limitations. In this study, we present a novel structural framework for research ideation. Our framework, The Budget AI Researcher, uses retrieval-augmented generation (RAG) chains, vector databases, and topic-guided pairing to recombine concepts from hundreds of machine learning papers. The system ingests papers from nine major AI conferences, which collectively span the vast subfields of machine learning, and organizes them into a hierarchical topic tree. It uses the tree to identify distant topic pairs, generate novel research abstracts, and refine them through iterative self-evaluation against relevant literature and peer reviews, generating and refining abstracts that are both grounded in real-world research and demonstrably interesting. Experiments using LLM-based metrics indicate that our method significantly improves the concreteness of generated research ideas relative to standard prompting approaches. Human evaluations further demonstrate a substantial enhancement in the perceived interestingness of the outputs. By bridging the gap between academic data and creative generation, the Budget AI Researcher offers a practical, free tool for accelerating scientific discovery and lowering the barrier for aspiring researchers. Beyond research ideation, this approach inspires solutions to the broader challenge of generating personalized, context-aware outputs grounded in evolving real-world knowledge.

Related papers

• IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery [27.218896203253987]
  IRIS is an open-source platform designed for researchers to leverage large language models (LLMs)-assisted scientific ideation.<n>IRIS incorporates innovative features to enhance ideation, including adaptive test-time compute expansion via Monte Carlo Tree Search (MCTS), fine-grained feedback mechanism, and query-based literature synthesis.<n>We conduct a user study with researchers across diverse disciplines, validating the effectiveness of our system in enhancing ideation.
  arXiv  Detail & Related papers  (2025-04-23T14:01:36Z)
• Graph of AI Ideas: Leveraging Knowledge Graphs and LLMs for AI Research Idea Generation [25.04071920426971]
  We propose a framework called the Graph of AI Ideas (GoAI) for the AI research field, which is dominated by open-access papers.<n>This framework organizes relevant literature into entities within a knowledge graph and summarizes the semantic information contained in citations into relations within the graph.
  arXiv  Detail & Related papers  (2025-03-11T15:36:38Z)
• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
  A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.<n>Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
  arXiv  Detail & Related papers  (2025-02-07T18:26:45Z)
• Enhancing LLM Reasoning with Reward-guided Tree Search [95.06503095273395]
  o1-like reasoning approach is challenging, and researchers have been making various attempts to advance this open area of research.<n>We present a preliminary exploration into enhancing the reasoning abilities of LLMs through reward-guided tree search algorithms.
  arXiv  Detail & Related papers  (2024-11-18T16:15:17Z)
• SciPIP: An LLM-based Scientific Paper Idea Proposer [30.670219064905677]
  We introduce SciPIP, an innovative framework designed to enhance the proposal of scientific ideas through improvements in both literature retrieval and idea generation.<n>Our experiments, conducted across various domains such as natural language processing and computer vision, demonstrate SciPIP's capability to generate a multitude of innovative and useful ideas.
  arXiv  Detail & Related papers  (2024-10-30T16:18:22Z)
• Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents [64.64280477958283]
  An exponential increase in scientific literature makes it challenging for researchers to stay current with recent advances and identify meaningful research directions. Recent developments in large language models(LLMs) suggest a promising avenue for automating the generation of novel research ideas. We propose a Chain-of-Ideas(CoI) agent, an LLM-based agent that organizes relevant literature in a chain structure to effectively mirror the progressive development in a research domain.
  arXiv  Detail & Related papers  (2024-10-17T03:26:37Z)
• Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System [62.832818186789545]
  Virtual Scientists (VirSci) is a multi-agent system designed to mimic the teamwork inherent in scientific research.<n>VirSci organizes a team of agents to collaboratively generate, evaluate, and refine research ideas.<n>We show that this multi-agent approach outperforms the state-of-the-art method in producing novel scientific ideas.
  arXiv  Detail & Related papers  (2024-10-12T07:16:22Z)
• From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models [56.9134620424985]
  Cross-modal reasoning (CMR) is increasingly recognized as a crucial capability in the progression toward more sophisticated artificial intelligence systems. The recent trend of deploying Large Language Models (LLMs) to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness. This survey offers a nuanced exposition of current methodologies applied in CMR using LLMs, classifying these into a detailed three-tiered taxonomy.
  arXiv  Detail & Related papers  (2024-09-19T02:51:54Z)
• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is an AI-based system for ideation and operationalization of novel work.<n>ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.<n>We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)

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.