Inherent, a London AI lab founded by Google DeepMind alumni, says its AI agent just outperformed much larger models from Anthropic and OpenAI using a fraction of the size.
Of all the startups launched by Google DeepMind alumni, Inherent has gotten relatively little attention. But while better-funded rivals have yet to show the world anything concrete, the London-based team is starting to share what it’s been building.
Just weeks after emerging from stealth with a $50 million seed round, the British startup says its newly released AI agent, Faraday, has outperformed larger, better-known models at a specific task: independently reproducing the findings of published scientific papers without being told the answer in advance.
That may sound like a mere party trick given Inherent’s much loftier goal — building AI that can disc over new scientific knowledge and not just verify old results. But paper replication is a standard training exercise for human scientists, too, cofo under and chief scientist Edward Hughes said. “Many PhD students actually start by doing this.”
Beating other AI systems at the task wasn’t the point, Hughes told TechCrunch; how they got there was. “What was most interesting to us about this was not so much the result of beating those frontier agents — which of course we liked — but was actually the way we went about building this.”
6 that has just 27 billion parameters.
) Inherent’s bar for success was also higher than simply accuracy.
Beyond replicating results, it wanted Faraday to demonstrate “research taste” — an instinct for what experiments are worth running and how to design them well.
Teaching something as intangible as taste is hard, which is where reinforcement learning comes in.
It’s a training method that rewards an AI system for good outcomes rather than spelling out rules for it to follow.
Rather than training its agents primarily on the study of how science itself is conducted, Inherent leans on this reward-based approach, betting it will generalize better to its longer-term goal of agents capable of contributing across many scientific fields.
“We’re always guided by that north star of building an AI scientist agent and imbuing our agents with taste,” Hughes said.
