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Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents

Paper · 2025 · Shuo Ren, Can Xie, Pu Jian et al.

Summary

This survey examines LLM-based scientific agents. These systems go beyond general-purpose chat or coding agents by drawing on domain knowledge, calling specialized tools such as simulators, APIs and lab instruments, and handling data like molecular structures or biological sequences. Earlier surveys sorted such agents by research lifecycle stage, by role, or by scientific domain. The authors instead organize the field by mechanism. They break agent design into four interacting components: a planner that decomposes tasks, memory that keeps context, an action space that executes tools and code, and a verifier that checks results. The four run in an iterative loop until the output is judged valid. For each component they describe sub-types, show how sub-types can be combined (illustrated with a running cathode-design example) and list open problems. They also review benchmarks, applications across disciplines, and ethics and reproducibility, which they treat as built-in architectural constraints rather than afterthoughts. The resulting design-focused map should help domain researchers find transferable techniques when assembling agents for their own fields.

Key points

  • Scope: covers more than 120 representative papers and over 40 domain benchmarks. Appendix tables classify each system by planner, memory, action and verifier type, grouped by field (e.g., biomedicine, physics/engineering, astronomy, Earth sciences, ML, literature review).
  • Planner taxonomy, prompt-native types (P1–P6):
    • instructional/schema-driven (e.g., Coscientist, CRISPR-GPT)
    • context-augmented (e.g., PaSa)
    • deliberative/reflective (e.g., LLMatDesign)
    • search-based (e.g., AI Scientist-v2)
    • role-interactive/multi-agent (e.g., AI co-scientist, ProtAgents)
    • programmatic code/DSL/DAG (e.g., AlphaEvolve, Biomni)
  • Planner taxonomy, learned types (L1–L2):
    • SFT/domain-trained (e.g., BioGPT, AstroMLab)
    • RL/preference-optimized (e.g., CycleResearcher, STEP-DPO)
  • Positioning: sets itself apart from Luo et al. (2025, task-focused), Wang et al. (2025c, the Assistant/Partner/Avatar levels) and Wei et al. (2025, Agentic Science, domain-oriented).
  • Future directions: interdisciplinary knowledge integration, dynamic adaptation, and standardized reproducibility protocols.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏛️Chinese Academy of SciencesBeijing · China256 works · 25,136 citations🔬Institute of AutomationBeijing · China89 works · 14,908 citations🎓University of Chinese Academy of SciencesBeijing · China196 works · 8,907 citations🏢Artificial Intelligence in MedicineToronto · Canada3 works · 170 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksArts and Humanities · 17,959 worksLanguage and Linguistics · 17,789 worksDecision Sciences · 306 worksInformation Systems and Management · 96 works

Cite

Cite as Shuo Ren, Can Xie, Pu Jian, Zhenjiang Ren, Chunlin Leng and Jiajun Zhang. 2025. Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents. arXiv:2503.24047.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2503.24047 · arXiv 2503.24047

@misc{ren2025scientific,
  title = {{Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents}},
  author = {Shuo Ren, Can Xie, Pu Jian, Zhenjiang Ren, Chunlin Leng and Jiajun Zhang},
  year = {2025},
  doi = {10.48550/arxiv.2503.24047},
  eprint = {2503.24047},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2503.24047}
}

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