SIA解决了AI自改进中框架与权重更新割裂的问题，做AI智能体或自监督学习的开发者可以直接参考其方法，在复杂任务上看到显著提升。
SIA提出了一种自改进循环，让一个语言模型智能体（反馈智能体）同时更新任务特定智能体的框架（工具、提示、重试逻辑等）和模型权重。传统方法中，框架更新和权重更新是分开研究的，而SIA将两者结合。在三个不同领域（中国法律罪名分类、GPU内核优化、单细胞RNA去噪）的测试中，SIA相比仅迭代框架分别提升了56.6%、91.9%和502%。框架更新让模型更智能地搜索和行动，权重更新则构建了提示或框架无法灌输的领域直觉。
SIA: Self Improving AI with Harness & Weight Updates
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.