@lillian_ma_: Emerging autoresearch labs worth following: @AutoScienceAI (@eliot_cowan) One of the cleanest “AI builds AI” bets: agen…

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Summary

A Twitter thread highlights emerging autoresearch labs that are building AI systems to automate the full research loop, from hypothesis to experimentation.

Emerging autoresearch labs worth following: @AutoScienceAI (@eliot_cowan) One of the cleanest “AI builds AI” bets: agents that invent, test, and ship ML models; not just tune hyperparameters. @intology (@zhouandy_) Zochi and Locus are built for the full research loop: read, hypothesize, code, run thousands of experiments, learn from failures, repeat. @thesis_labs (@eigentopology) YC F25. Treating ML research as a compounding search problem, where every experiment improves the next one instead of dying in a Notion doc. @Recursive_SI (@RichardSocher, @_rockt, @jeffclune) A very ambitious new bet on AI systems that run open-ended experiments on how to make AI systems better. autoresearch with recursive consequences. @EdisonSci (@SGRodriques, @andrewwhite01) A newer FutureHouse spinout bringing AI scientists into biopharma R&D — where “deep research” has to survive real data, real experiments, and real timelines. @HarmonicMath (@tachim, @vladtenev) Math’s version of autoresearch: AI exploring new proofs, with formal verification as the anti-hallucination layer. @readysetpotato (@Nick___Edwards) An AI scientist for actual research workflows — papers, hypotheses, protocols, computational tools, and eventually lab automation. @EvoScientist (@_xizhang) A very early one to watch: multi-agent AI scientists with persistent memory, so failed ideas and experiments improve the next research cycle. @SakanaAILabs (@hardmaru) The original AI Scientist builders. Still one of the best technical follows for research artifacts, open source, and weird ideas that actually run. not exhaustive - add the early teams I’m missing in the comment
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Cached at: 06/23/26, 02:09 PM

Emerging autoresearch labs worth following:

@AutoScienceAI (@eliot_cowan) One of the cleanest “AI builds AI” bets: agents that invent, test, and ship ML models; not just tune hyperparameters.

@intology (@zhouandy_) Zochi and Locus are built for the full research loop: read, hypothesize, code, run thousands of experiments, learn from failures, repeat.

@thesis_labs (@eigentopology) YC F25. Treating ML research as a compounding search problem, where every experiment improves the next one instead of dying in a Notion doc.

@Recursive_SI (@RichardSocher, @_rockt, @jeffclune) A very ambitious new bet on AI systems that run open-ended experiments on how to make AI systems better. autoresearch with recursive consequences.

@EdisonSci (@SGRodriques, @andrewwhite01) A newer FutureHouse spinout bringing AI scientists into biopharma R&D — where “deep research” has to survive real data, real experiments, and real timelines.

@HarmonicMath (@tachim, @vladtenev) Math’s version of autoresearch: AI exploring new proofs, with formal verification as the anti-hallucination layer.

@readysetpotato (@Nick___Edwards) An AI scientist for actual research workflows — papers, hypotheses, protocols, computational tools, and eventually lab automation.

@EvoScientist (@_xizhang) A very early one to watch: multi-agent AI scientists with persistent memory, so failed ideas and experiments improve the next research cycle.

@SakanaAILabs (@hardmaru) The original AI Scientist builders. Still one of the best technical follows for research artifacts, open source, and weird ideas that actually run.

not exhaustive - add the early teams I’m missing in the comment

@zhengyaojiang what’s special about @WecoAI ?

should have added you guys

which company do you want to talk to?

Super interesting! Who should I DM for private event invitation?

what is he building?

Is Axiom going to ICML next month?

Looks cool. Will help promote in my network.

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