SIA: Self Improving AI with Harness & Weight Updates (May 2026)

Title: SIA: Self Improving AI with Harness & Weight Updates (May 2026)
Link: http://arxiv.org/abs/2605.27276v2
Date: May 2026

Summary:
This paper proposes SIA, a self-improving loop where a meta-agent (the Feedback-Agent) updates both the 'harness' (prompts, tools, and scaffold) and the model weights of a task-specific agent. While previous research bifurcated into either scaffold evolution or test-time training, SIA combines both levers to achieve significant performance gains across diverse domains including legal classification, GPU kernel optimization, and biological data denoising.

Key Topics:
- Self-Improving Agents
- Test-Time Training
- Reinforcement Learning
- Harness Engineering
- Scaffold Generation
- Low-Rank Adaptation (LoRA)

Chapters:
00:00 - The Human Bottleneck
00:57 - Current Self-Improvement Landscape
02:05 - Scaffold Evolution Limitations
03:34 - Test-Time Training Silos
05:30 - SIA Architecture Breakdown
07:15 - Feedback Agent Decision Logic
08:14 - Case: Chinese Legal Classification
10:06 - PPO For Legal Intuition
11:55 - GPU Kernel Speed Optimization
13:30 - Solving Sparse Rewards
14:55 - Internalizing Hardware Realities
16:00 - Biological RNA Data Denoising
17:40 - Discovering Biological Invariants
19:17 - Internal vs External Improvement
20:02 - The Coupled Goodhart Trap
21:35 - Recursive Learning Horizons

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