Inherent introduced Faraday, a 27B-parameter AI Scientist that extends the capabilities of coding agents with a layer of scientific intuition.

Trained via long-horizon RL, Faraday outperforms Claude Opus 4.8 and GPT-5.5 on the task of replicating research papers.

To train Faraday, team developed Replica, a scalable task space for paper replication.

Each task requires an agent to replicate a figure from a machine learning or AI for science research paper with a limited time and compute budget, and without access to the original plot.

Faraday uses GPT-5.5 Codex as a tool, much like human scientists use coding agents.

Faraday directs a model several orders of magnitude larger, improving replication on domains as diverse as meta-learning, structural biology and materials science.

Faraday discovers new insights at test time, with no special-purpose harness and no test-time reward. In other words, Faraday learns to value new insights intrinsically.