Intology

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.

@intology recognized at this year's Smart AI 100 Summit!

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"