Automating the process of discovery.
San Francisco Bay Area
Joined March 2025
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The models are improving the models.
Locus, our automated AI research system, is SOTA on PostTrainBench and post-trains Qwen3 base models that surpass the human post-trained Qwen3 model.
Today, LLMs post-trained end-to-end by Locus are in production to millions. 🧵👇
PostTrainBench evaluates agents' ability to post-train models on various domains given 10 H100 hours.
We extend PostTrainBench via PostTrainBench+, which has a greatly expanded compute budget that provides clearer signal on automated post-training capabilities. We find that thousands of H100 hours help distinguish methods' performance post-training Qwen3 1.7B-Base models, and that Locus scales best. In this setting, modes trained by Locus collectively surpass the perforamce of the offical human post-trained Qwen3 1.7B model.
In a test of generalization, we ran Locus on all live Kaggle competitions with prize money and public leaderboards. After 16 days, Locus achieved the 4th highest average rank among all participants.
Locus sets the frontier for automating molecular design
Our automated research system Locus develops scientific AI models that outperform SOTA expert solutions
Unlocking the potential of scientific AI requires leveraging proprietary data. Locus achieves this autonomously 🧵👇
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We test Locus across a number of tasks related to molecular scoring and design: training molecular property prediction models for a proprietary task, fine-tuning Boltz-2 to predict small-molecule potency, and building antibody design workflows around BoltzGen, Boltz-2, ESMC, and ESMFold2.
Each of these settings are real-world AI for science use cases, where tasks are often out-of-distribution for existing foundation models and limited in data, while featuring expensive and noisy evaluators. Compared to state-of-the-art baselines and LLM agents set to optimize these tasks, Locus achieves higher performance. Additionally, Locus generalizes better despite limited data, finds more sample efficient solutions, and explores a higher diversity of solutions.
Previously, we shared Locus’ state-of-the-art results in automated LLM post-training, demonstrating Locus’ ability to scale in an expensive domain.
Locus’ post-training results were featured in Jack Clark’s blog below: jack-clark.net/2026/08/10/im…
Through our research program, we have improved Locus’ capabilities in dealing with large hypothesis spaces, evaluation costs, and experimental noise.
From language model post-training to scientific AI, we see the challenges in our mission of scaling experimentation across domains as highly complementary.
Read the blog: intology.ai/blog/automating-…
alphaXiv: alphaxiv.org/abs/2611.automa…
@intology recognized at this year's Smart AI 100 Summit!
Thank you to @gracegongGG for honoring @intology at the Smart AI 100 Awards.
Grateful for the panels with leaders from @Cisco , @ServiceNow, @nebiusai, @cerebras, @Atlassian, @datadoghq, @Zoom, @asana, and more.
Really enjoyed the conversations with: Jeetu Patel · Marc Boroditsky · Julie Choi · Amit Zavery · Abbas Haider Ali · Emilio E. · Brendan Ittelson · Arnab Bose
Such a great conference, excited to have been a part of it.
Locus is now verified on the official PostTrainBench leaderboard!
Thank you to @hrdkbhatnagar, @full__rank, and @maksym_andr for reviewing and uploading our solutions on the 10-hour setting.
Thank you @jackclarkSF for covering our work with Locus!
"AI startup Intology, whose goal “is to automate R&D”, has released a new version of Locus, software it has developed to turn LLMs into capable researchers. The new version of Locus is able to get a score of 44.7% on PostTrainBench, a benchmark which sees how well AI systems can take an open weight model and improve its performance above its baseline."
"Posts like this highlight how we are under-eliciting today’s AI systems for their ability to automate AI R&D - especially striking is how the company can jump the performance of Opus 5 by 10 absolute percentage points"
Thanks to @jackclarkSF for covering @intology's work on Locus!
"Posts like this highlight how we are under-eliciting today’s AI systems for their ability to automate AI R&D - especially striking is how the company can jump the performance of Opus 5 by 10 absolute percentage points"
Full blog: importai.substack.com/p/impo…
More clips:
xitter.gq/ronusedh/status/209191…
The models are improving the models.
Locus, our automated AI research system, is SOTA on PostTrainBench and post-trains Qwen3 base models that surpass the human post-trained Qwen3 model.
Today, LLMs post-trained end-to-end by Locus are in production to millions. 🧵👇
PostTrainBench evaluates agents' ability to post-train models on various domains given 10 H100 hours.
We extend PostTrainBench via PostTrainBench+, which has a greatly expanded compute budget that provides clearer signal on automated post-training capabilities. We find that thousands of H100 hours help distinguish methods' performance post-training Qwen3 1.7B-Base models, and that Locus scales best. In this setting, modes trained by Locus collectively surpass the perforamce of the offical human post-trained Qwen3 1.7B model.
In a test of generalization, we ran Locus on all live Kaggle competitions with prize money and public leaderboards. After 16 days, Locus achieved the 4th highest average rank among all participants.
We had a great time presenting at the AI Scientist Summer Workshop in Boston.
Thank you to @WengongJin, @YuanqiD, @YEktefaie, @xiwei393, @BotaoYu24, Yikun Zhang for organizing this amazing event, and thank you to @MSFTResearch for hosting us!
The models are improving the models.
Locus, our automated AI research system, is SOTA on PostTrainBench and post-trains Qwen3 base models that surpass the human post-trained Qwen3 model.
Today, LLMs post-trained end-to-end by Locus are in production to millions. 🧵👇
PostTrainBench evaluates agents' ability to post-train models on various domains given 10 H100 hours.
We extend PostTrainBench via PostTrainBench+, which has a greatly expanded compute budget that provides clearer signal on automated post-training capabilities. We find that thousands of H100 hours help distinguish methods' performance post-training Qwen3 1.7B-Base models, and that Locus scales best. In this setting, modes trained by Locus collectively surpass the perforamce of the offical human post-trained Qwen3 1.7B model.
In a test of generalization, we ran Locus on all live Kaggle competitions with prize money and public leaderboards. After 16 days, Locus achieved the 4th highest average rank among all participants.
We will be presenting at the AI Scientist Summer Workshop in Cambridge, this coming Tuesday, August 4, 2026. Ron Arel (@ronusedh) will be presenting.
Thank you @AdaFang_ for coordinating!
The models are improving the models.
Locus, our automated AI research system, is SOTA on PostTrainBench and post-trains Qwen3 base models that surpass the human post-trained Qwen3 model.
Today, LLMs post-trained end-to-end by Locus are in production to millions. 🧵👇
PostTrainBench evaluates agents' ability to post-train models on various domains given 10 H100 hours.
We extend PostTrainBench via PostTrainBench+, which has a greatly expanded compute budget that provides clearer signal on automated post-training capabilities. We find that thousands of H100 hours help distinguish methods' performance post-training Qwen3 1.7B-Base models, and that Locus scales best. In this setting, modes trained by Locus collectively surpass the perforamce of the offical human post-trained Qwen3 1.7B model.
In a test of generalization, we ran Locus on all live Kaggle competitions with prize money and public leaderboards. After 16 days, Locus achieved the 4th highest average rank among all participants.
Ron Arel (@ronusedh), co-founder of Intology will be on @TBPN today to discuss Locus' automated post-training capabilities, and more
Happy Monday. On today's show:
- @gruber (Daring Fireball)
- @isaiah_p_taylor (Valar Atomics)
- @shaunmmaguire (Sequoia)
- @JLopas (Base Power)
- @ronusedh (Intology)
- Beau Gaston (Satyress)
See you on the stream.
