Automating AI Discovery @intology | Prev- Post-training @amazonscience @UofMaryland, @bitspilaniindia
San Francisco, CA
Joined August 2012
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After 4+ years building models at @AmazonScience (Alexa + AWS AI), I joined @intology a few months ago!
We’re automating AI R&D. Here are our results showing Locus building scientific AI models that beat SOTA expert solutions in molecular design
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.
@ndtvindia @News18India JVPD residents have been trying to protect our open spaces for years now, only to see new buildings/developments taking over our parks. We’ve already seen one ground become a construction site these past 6 months.
@mhadaofficial @MilindBorikar67 Tilak Udyan is a public garden and green space near schools and colleges in JVPD. It has now been handed over to private developers for the construction of a large building. Please look into this immediately!
@HDFCLIFE @HDFCERGOGIC Please look into this issue! My mother has been trying to get in touch with support but hasn’t received any help.
Rahil Parikh retweeted
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.
Rahil Parikh retweeted
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…
Rahil Parikh retweeted
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.
Rahil Parikh retweeted
Can coding agents do research?
We release NanoGPT-Bench, an internal eval we’ve used to test agents on an AI R&D problem with months of human progress
Codex, Claude Code, Autoresearch recover only 9.3% of human progress, mostly tuning hyperparams & ignoring algorithmic research
NanoGPT-Bench is built on the NanoGPT Speedrun, a popular LLM pretraining competition to minimize the training time of a GPT-2 style model. Existing human submissions constitute nearly 2 years of work. To control for dependencies and contamination in frontier models, we standardize evaluation to a 5-month window of world records. Evaluation is fully autonomous and end-to-end, with no human intervention or internet access. 🧵
@MumbaiPolice @mybmc Please look into this issue that has not been resolved despite of years of complaining
Thanks— that’s useful information :-)
I’ll keep a lookout for communities of researchers and builders working on trustworthy AI