# SIA: Self Improving AI with Harness and Weight Updates > Source: > Published: 2026-05-28 19:50:25+00:00 # Computer Science > Artificial Intelligence [Submitted on 26 May 2026] # Title:SIA: Self Improving AI with Harness & Weight Updates [View PDF](/pdf/2605.27276) [HTML (experimental)](https://arxiv.org/html/2605.27276v1) 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. The harness-update school has a meta-agent rewrite the scaffold of a task-specific agent (its tools, prompts, retry logic, and search procedure) while the model weights are held fixed. The test-time training school uses hand-written RL pipelines to update the model's own weights on task feedback while the harness is held fixed. These two silos operate in isolation. We propose SIA, a self-improving loop in which a language-model agent (the Feedback-Agent) updates both the harness and the weights of a task-specific agent. We evaluate across three contrasting domains: Chinese legal charge classification, low-level GPU kernel optimisation, and single-cell RNA denoising. Combining both levers outperforms scaffold iteration alone on all three benchmarks. The gains are 56.6% on LawBench, 91.9% runtime reduction on GPU kernels, and 502% on denoising over the initial baseline. Harness updates make the model agentic, shaping how it searches and acts, while weight updates build the domain intuition that no prompt or scaffold can instil. ### References & Citations Loading... # Bibliographic and Citation Tools Bibliographic Explorer *(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer)) Connected Papers *(*[What is Connected Papers?](https://www.connectedpapers.com/about)) Litmaps *(*[What is Litmaps?](https://www.litmaps.co/)) scite Smart Citations *(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article alphaXiv *(*[What is alphaXiv?](https://alphaxiv.org/)) CatalyzeX Code Finder for Papers *(*[What is CatalyzeX?](https://www.catalyzex.com)) DagsHub *(*[What is DagsHub?](https://dagshub.com/)) Gotit.pub *(*[What is GotitPub?](http://gotit.pub/faq)) Hugging Face *(*[What is Huggingface?](https://huggingface.co/huggingface)) ScienceCast *(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos # Recommenders and Search Tools Influence Flower *(*[What are Influence Flowers?](https://influencemap.cmlab.dev/)) CORE Recommender *(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).