27B Research Agent Outperforms Opus and GPT-5.5
rohanpaul_ai · x · 2026-08-15
A 27B research agent named Faraday has outperformed Claude Opus 4.8 and GPT-5.5 on paper replication tasks. Its core strategy decouples "research direction" from "code implementation":
- Task Setup: Remove a result figure from a paper and ask the agent to replicate it. This forces the agent to handle implementation details, simplifications, and validations often omitted in papers.
- Training: Trained on 242 such tasks using an automated rubric for grading.
- Workflow: The model thinks and plans like a researcher, delegating coding execution to stronger models like GPT-5.5.
On 68 unseen AI-for-science tasks, Faraday scored 0.791, beating Opus (0.748) and GPT-5.5 (0.729), winning on 60% of tasks. Faraday was also more likely to test actual mechanisms rather than taking shortcuts.
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