35 iterations, 6 of 6 runs: programmatic skill graphs triple Voyager's hit rate on Minecraft's diamond-tool climb

Notes on Evolving Programmatic Skill Networks (arXiv:2601.03509) — Haochen Shi, Xingdi Yuan, Bang Liu · January 2026

Note published July 23, 2026 · written by SkillFed’s research pipeline from the paper above · how these notes are made

Evolving Programmatic Skill Networks builds agent skills as executable, typed programs — not prompts or embeddings — arranged in a directed graph that keeps growing and rewriting itself as the agent acts. The framework, called the Programmatic Skill Network (PSN), grows through two linked mechanisms. Day to day, Reflect traces a failed run backward through the skill graph to pin down which specific subskill broke — a symbolic stand-in for backpropagation — and a maturity-aware update gate throttles how often a skill gets rewritten based on its track record, so reliable skills stop churning while shaky ones stay open to revision. Periodically, a refactoring pass collapses redundant or overlapping skills into one of five canonical patterns — parametric coverage, behavioral coverage, sibling specialization, common-subskill extraction, and duplication — with every rewrite gated behind a rollback check against recent tasks. Both mechanisms are evaluated on the MineDojo Minecraft tech-tree benchmark and on Crafter.

On MineDojo's diamond-tool climb, PSN closes all six runs, averaging 35 iterations; Voyager, the standard open-ended-skill baseline, closes only one of three runs and needs 102. The gap survives a weaker backbone: swapping GPT-5-mini for the open mixture-of-experts model Qwen3-Coder-Next still reaches the goal, just slower (49 vs. 35 iterations), with a shallower skill graph — a reuse ratio (the share of skills called by more than one other skill) of 0.15 against 0.40 for the stronger model — and deeper repair chains (5.0 vs. 2.7 skills touched per multi-skill fix). Retries compound quickly: first-attempt success of 67% reaches 100% within three tries, and on a fixed compositional-task suite, turning refactoring on lifts PSN to 84.62% success against 68.75% for a Voyager variant. Across both benchmarks, PSN keeps its earlier skills intact as it learns new ones, while Voyager shows marked backward interference.