scaffold

This tweet introduces a standardized skill scaffold template for AI development to ensure consistent folder structures, reduce CI breaks, and speed up review processes with key components like contract files and fixture tests.

Introduces Belief-Calibrated Optimization (BCO), a method that maintains a persistent world model to improve LLM agent performance by explicitly tracking and revising beliefs about environment responses to edits.

This paper shows that the choice of agent harness (scaffold) can cause up to a 40x difference in tokens per solved task, while model pass rates vary only slightly, demonstrating that harness–model pairs, not model alone, should be compared for human-centered coding-agent evaluation.

A survey of 239 papers analyzing how AI agents self-improve by updating the model itself or the scaffold (prompts, memory, tools).

This paper presents a novel blueprint for self-improving agents that combines scaffold editing and weight training through a meta-agent and feedback-agent, achieving a 14x speedup on a CUDA kernel for AlphaFold.

This article describes a scaffold that scales test-time compute on Qwen-3.6-27B and Gemma-4-31B using iterative corrections and branch exploration to surpass Claude Mythos in code optimization. It includes a paper link and GitHub repository.

The author proposes that durable memory in multi-agent AI systems should reside with the project owner, not with task specialists, drawing inspiration from consulting firm practices and academic literature on project memory. They present a research scaffold with templates and evaluation rubrics for a field trial.