#self-improvement
4 articles
01
·Agents·★ MEMBER·PAPER·12 min read
Paper Walkthrough: HarnessDev — Can an LLM Build and Maintain the System It Runs Inside?
A ground-up walkthrough of HarnessDev, a benchmark that asks whether an LLM can build an agent harness from a deliberately useless seed and then improve it from execution feedback. Models can build one; the gains rarely survive contact with held-out tasks or a different runtime model.
02
·Evaluation & Judging·★ MEMBER·PAPER·12 min read
Self-Improving AI — Self-Play, Co-Evolution, and Generated Curricula
What has to be true for a model to get better without anyone adding data? This article pulls three conditions out of AlphaZero's self-play, shows exactly which one breaks first for language models, explains how co-evolution and generated curricula try to patch the gap, and ends with why self-improvement claims are unusually easy to evaluate wrong.
03
·Agents·★ MEMBER·PAPER·15 min read
Paper Walkthrough: PILOT in the Loop — Fixing the Run While It Is Still Running
Agent self-improvement that only starts after execution ends arrives too late. A ground-up walkthrough of PILOT, a supervisor–worker harness that redirects the live run while distilling skills from it, from intuition to equations to measured numbers and stated limits.
04
·Agents·★ MEMBER·PAPER·8 min read
Paper Walkthrough: Macaron-V1 — A Frozen Base plus a Mixture of LoRAs, Built to Keep Learning After Launch
A guided tour of Mind Lab's open agent-model family Macaron-V1, straight from the paper: the Mixture-of-LoRA architecture that stacks four specialist adapters on a frozen 744B base and picks one per turn, and the recursive self-improvement loop that trains the model and its harness as a versioned pair.