SIA: Self Improving AI with Harness & Weight Updates
June 13, 2026Posted by Roberto Stagi
The researchers argue that humans still limit AI improvement because both models and agent scaffolds require manual design and correction. They propose SIA, a self-improving loop where a Feedback-Agent updates both an agent’s harness and its model weights.
They test SIA on legal classification, GPU kernel optimization, and single-cell RNA denoising. Across all three, combining harness and weight updates beats scaffold-only improvement, with reported gains of 25.1% over prior SOTA on LawBench, 12.4% faster GPU kernels, and 20.4% over prior SOTA on denoising. The researchers conclude that harness updates improve how agents act and search, while weight updates build domain-specific intuition.
Sources: paper