How Far Are AI Scientists from Changing the World?

Key points

• Problem: LLM-based AI Scientist systems have produced papers accepted at an ICLR 2025 workshop, and the authors ask how far such systems are from discoveries that solve grand challenges across medicine, energy, and the environment. They note that earlier surveys gave overviews of LLM architectures, agentic frameworks, and scientific automation tools, but a capability-based roadmap toward the ideal AI scientist was still missing.
• Scope: This is a survey of LLM-based AI Scientist systems. Sections 2 to 6 cover knowledge acquisition, idea generation, verification and falsification, AI reviewer systems, and evolution in turn and summarize representative methods in each; evaluation benchmarks are collected in Sections 2, 4 (Table 2), and 5 (Table 3), while Section 3 covers evaluation criteria for hypotheses and automated and human evaluation. Section 7 covers limitations and ethical challenges, and Section 8 covers future directions such as standardized Scientist-to-Scientist Communication Protocols (SSCP) for structured interaction among AI Scientist systems.
• Taxonomy: The authors divide the capabilities of a mature AI Scientist into four levels. Level 1, knowledge acquisition, is retrieving, reviewing, and comprehending scientific knowledge from existing research; Level 2, idea generation, is generating innovative and feasible hypotheses at scale; Level 3, verification and falsification, is designing, implementing, and analyzing experiments to test and potentially disprove those hypotheses; Level 4, evolution, is continuously advancing overall research abilities from internal and external feedback. They treat Level 2 as the key feature that distinguishes AI Scientist systems from automated scientific tools.
• Repository: The GitHub repository Awesome-AI-Scientist named on the paper's first page keeps a curated list of papers, open source tools, and benchmarks in its README, with files at the top level that include README.md, a CONTRIBUTING.md guide, and an img folder. The README groups papers under the survey's chapter names (Knowledge Acquisition, Idea Generation, Verification and Falsification, Review System, Evolution) and says it links each paper to the paper itself, associated code, and available reviews. For some papers without open reviews, the README says the maintainers added links to AI review analyses generated with their DeepReviewer.
• Evidence: In a table the authors collected from other papers to show that state-of-the-art LLMs have relatively low accuracy on implementation benchmarks, OpenAI o1-preview reaches a 16.90% medal-worthy success rate on MLE-Bench, o1-high a 26.00% replication score on PaperBench, and Claude-Sonnet-3.7 a 39.00% execution accuracy on SciReplicate-Bench. When the authors scored 28 publicly available papers from 5 AI Scientist systems with the AI reviewer model DeepReviewer-14B, the highest-rated system, Zochi, averaged only 4.63 out of 10. Among defect categories, 'Experimental Weakness' appeared in all 28 papers (100%) and 'Methodological Unclarity/Flaws' in 96.4%.
• Limits: The authors attribute the remaining gap to limits of the underlying LLMs, namely hallucination, the high cost and inefficiency of knowledge updating, and catastrophic forgetting, and to weak research abilities such as generating hypotheses with low feasibility and employing incorrect validation methods. They note under the table that publicly available papers may be curated and may not fully represent each system's typical output. The scores come from a single model, DeepReviewer-14B, and average 2 papers for Zochi and 3 for AI Scientist-v2.

Paper links

External research summaries. These are not HDATF publications or measured product results.