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A Vision for Auto Research with LLM Agents

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arxiv 2504.18765 v3 pith:DHSNRXFA submitted 2025-04-26 cs.AI

Chengwei Liu , Chong Wang , Jiayue Cao , Jingquan Ge , Kun Wang , Lyuye Zhang , Ming-Ming Cheng , Penghai Zhao
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Tianlin Li Xiaojun Jia Xiang Li Xingshuai Li Yang Liu Yebo Feng Yihao Huang Yijia Xu Yuqiang Sun Zhenhong Zhou Zhengzi Xu
This is my paper · ORCID
classification cs.AI
keywords researchautoframeworkreviewscientificaddressingagentagent-based
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research. Leveraging the capabilities of large language models (LLMs) and modular agent collaboration, the system spans all major research phases, including literature review, ideation, methodology planning, experimentation, paper writing, peer review response, and dissemination. By addressing issues such as fragmented workflows, uneven methodological expertise, and cognitive overload, the framework offers a systematic and scalable approach to scientific inquiry. Preliminary explorations demonstrate the feasibility and potential of Auto Research as a promising paradigm for self-improving, AI-driven research processes.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes

    cs.AI 2026-07 conditional novelty 7.0 of 10

    Conference accept/reject outcomes yield 15 operational ideation patterns that, as an LLM skill suite, improve automated-judged research-proposal quality over no-skill and generic-skill baselines.

  2. Internalizing Academic Writing Workflows for Introduction Generation via Struct-Aware Policy Learning

    cs.CL 2026-08 conditional novelty 6.0 of 10

    RL with per-section rewards and a revision penalty lets a single LLM pass generate structured paper introductions comparable to GPT-5.1 in human preference.

  3. BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers?

    cs.CR 2025-10 conditional novelty 6.0 of 10

    An LLM agent generating fabricated papers without experiments gets acceptance-level scores from LLM reviewers up to 82% of the time, and simple integrity-checking mitigations barely beat random.

  4. SGSimEval: A Comprehensive Multifaceted and Similarity-Enhanced Benchmark for Automatic Survey Generation Systems

    cs.CL 2025-08 unverdicted novelty 5.0 of 10

    SGSimEval is a multifaceted benchmark showing that automatic survey generation systems match humans on outline quality but lag on content and references.

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