Papers › The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

12 Aug 2024arXiv:2408.06292archive 2025-07-28

Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, David Ha

One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used as aides to human scientists, e.g. for brainstorming ideas, writing code, or prediction tasks, they still conduct only a small part of the scientific process. This paper presents the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models to perform research independently and communicate their findings. We introduce The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a full scientific paper, and then runs a simulated review process for evaluation. In principle, this process can be repeated to iteratively develop ideas in an open-ended fashion, acting like the human scientific community. We demonstrate its versatility by applying it to three distinct subfields of machine learning: diffusion modeling, transformer-based language modeling, and learning dynamics. Each idea is implemented and developed into a full paper at a cost of less than $15 per paper. To evaluate the generated papers, we design and validate an automated reviewer, which we show achieves near-human performance in evaluating paper scores. The AI Scientist can produce papers that exceed the acceptance threshold at a top machine learning conference as judged by our automated reviewer. This approach signifies the beginning of a new era in scientific discovery in machine learning: bringing the transformative benefits of AI agents to the entire research process of AI itself, and taking us closer to a world where endless affordable creativity and innovation can be unleashed on the world's most challenging problems. Our code is open-sourced at https://github.com/SakanaAI/AI-Scientist

PaperPDFCodeCode Syntology ran

In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.

Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2408.06292")

Code

Syntology Ran 10 of 11 code samples harvested from 2 repositories linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · honoured contract; 9 ran with no contract checked.

By repository: official repository: 9 samples from 1 repository, 9 ran; community (archive-listed): 2 samples from 1 repository, 1 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

sakanaai/ai-scientist officialmentioned in papermentioned on GitHubpytorchNOASSERTION report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

11 samples harvested; 10 ran; 1 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.

1ran · honoured contract
9ran
1unverified

Licence: 9 of the 11 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

extract_json_between_markers sakanaai/ai-scientist/ai_scientist/llm.py official repository ran fingerprinted licence not identified · pointer only · 41adbcc04070fd12 · report
generate_color_palette sakanaai/ai-scientist/example_papers/layerwise_lr_grokking/plot.py official repository ran licence not identified · pointer only · de494a578ed3156e · report
get_data sakanaai/ai-scientist/example_papers/layerwise_lr_grokking/experiment.py official repository ran licence not identified · pointer only · ef71cac55b75b747 · report
load_review sakanaai/ai-scientist/ai_scientist/perform_review.py official repository ran licence not identified · pointer only · 0560771a47a3e327 · report
operation_mod_p_data sakanaai/ai-scientist/example_papers/layerwise_lr_grokking/experiment.py official repository ran licence not identified · pointer only · ef0e4401922abbd5 · report
perform_experiments sakanaai/ai-scientist/ai_scientist/perform_experiments.py official repository ran licence not identified · pointer only · 5dfa236b6bc7969c · report
run_experiment sakanaai/ai-scientist/ai_scientist/perform_experiments.py official repository ran licence not identified · pointer only · 8d71b435fe16e51f · report
run_plotting sakanaai/ai-scientist/ai_scientist/perform_experiments.py official repository ran licence not identified · pointer only · 4faf099f4b9d5c34 · report
train sakanaai/ai-scientist/example_papers/layerwise_lr_grokking/experiment.py official repository ran licence not identified · pointer only · a8a8eb2391a886e7 · report
get_available_gpus Masao-Taketani/AI-Scientist-with-Local-LLMs/launch_scientist.py community (archive-listed) ran · honoured contract Apache-2.0 (permissive) · b9a353fd9f420101 · report
train Masao-Taketani/AI-Scientist-with-Local-LLMs/templates/nanoGPT/experiment.py community (archive-listed) unverified Apache-2.0 (permissive) · d3a0ec611e5c88d1 · report

Tasks

Language ModelingLanguage Modellingscientific discovery

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections