Rahil Parikh

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"

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.

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. 🧵