According to TechCrunch AI's report, Inherent's announcement highlights a growing, and arguably more interesting, trend than raw performance: the push for AI with 'taste.' The focus on a tiny model beating giants is a classic underdog narrative, but the more substantive bet is on reinforcement learning to instill judgment, not just knowledge. In our view, the risk is that 'taste' remains an ill-defined, unmeasurable goal, making it a convenient shield for any shortcoming. The real test won't be replicating known results but generating novel, testable hypotheses that advance a field—a leap the company itself admits is still a 'north star' and not a present reality.
Inherent's small AI agent reportedly beats larger rivals at paper replication
A London startup founded by DeepMind alumni claims its compact AI teammate outperformed frontier models on a scientific benchmark.
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Editor's Take
As reported by TechCrunch AI, a startup called Inherent says its small AI agent has outperformed larger models from Anthropic and OpenAI on a scientific paper replication task. While the claim is intriguing, it's worth asking who cares about replicating old papers when the stated goal is discovering new science. The real story here may be less about the benchmark result and more about a small team's attempt to build 'research taste' into an AI, a nebulous quality that could be the key to genuine scientific contribution—or a marketing dead end.
“"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."”
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