SIA: Self Improving AI with Harness & Weight Updates
The paper presents SIA, a self-improving AI framework that integrates harness and weight updates. It aims to overcome the limitations of current AI development, which relies heavily on human intervention. The proposed method shows significant performance improvements across various tasks, demonstrating its potential for advancing AI capabilities.
- ▪SIA combines harness updates and weight updates to enhance AI self-improvement.
- ▪The framework was evaluated in three domains: legal charge classification, GPU kernel optimization, and RNA denoising.
- ▪Performance gains were reported as 56.6% on LawBench, 91.9% runtime reduction on GPU kernels, and 502% improvement on RNA denoising.
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Computer Science > Artificial Intelligence arXiv:2605.27276 (cs) [Submitted on 26 May 2026] Title:SIA: Self Improving AI with Harness & Weight Updates Authors:Prannay Hebbar, Yogendra Manawat, Samuel Verboomen, Alesia Ivanova, Selvam Palanimalai, Kunal Bhatia, Vignesh Baskaran View a PDF of the paper titled SIA: Self Improving AI with Harness & Weight Updates, by Prannay Hebbar and 6 other authors View PDF HTML (experimental) Abstract:Humans are the bottleneck in building and improving AI. Both the models and the agents that wrap them are written, tuned, and corrected by people. The long-horizon goal of an AI that can figure out how to improve itself remains open. Two largely disjoint research lines attack this bottleneck.
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