Weight space programming could unlock self-inspection, RSI and continual learning
Michael_D_Moor · x · 2026-11-15
The author argues weight space programming (WSP) is a strong reason to make larger models more encapsulated and intelligible for self-inspection. Running WSP on the teacher model itself could then enable RSI, test-time adaptation, and continual learning — a directional research thesis about making model weights readable and programmable as infrastructure for self-improvement loops.
More from Research
- MaxxBench: tiny task tweaks spread coding-agent scores by 23 points — MFarajtabar · 2026-11-15
- Researcher: Make LLMs Self-Inspectable Enough for Weight-Space Programming and RSI — Michael_D_Moor · 2026-11-15
- ValsAI Releases Three Social Mobility Benchmarks Covering Justice, Immigration, and Financial Coaching — AnkaReuel · 2026-11-15
- Math professor on OpenAI's quasi-Riemann proof: never thought I'd live to see it — haider1 · 2026-11-15
- Roughgarden's new paper: equilibria in lemon markets keep a constant fraction of trade gains — chaumian · 2026-11-15
- Flex-RSI benchmark tests whether agents can self-improve across 18 frontier models — _vztu · 2026-11-15