A training method for self-evolving coding-agent harnesses that separates model-specific quirks from real harness bugs before rewriting anything, reporting a 1.84x training speedup and an 18.56 percent accuracy gain over prior methods on airline and retail agent benchmarks. This corpus already tracks several papers in this specific sub-field. The numbers refine an existing approach.
In the News: September 13, 2026
Ryan Lopopolo argues that no grader is unhackable, exposing the risk of trusting agents in domains where users cannot judge the work.
Morning edition
Machine-readable
Download Markdown
Story
There is no such thing as an unhackable grader, a veteran harness engineer argues
Lopopolo starts with a practical limit: experts can catch an agent's mistakes in their own fields. Outside that expertise, in his examples double-entry accounting, finance, law, and operations, users rely entirely on the model's priors…
Read story →
Story
A researcher's case: your reproducibility habits are already agent context
Barba, a longtime reproducible-research advocate writing on agentic coding for the first time, maps five familiar software artifacts to the context a coding agent needs: a test suite, a clean commit history, a well-organized repository, a…
Read story →Ecdysis: Efficient and Effective Training of Runtime Harnesses for LLM Agents
Garry Tan on X
Tan's quote-tweet was reported as saying that founders outside hardware are converging on building a domain-specific harness instead of remaining a system of record. The post had reached roughly 685,000 views, 3,400 likes, 269 reposts and 137 replies at 19 hours after posting. By the next morning, a separate trending topic tied to the same line had drawn more than 2,300 posts. The post itself and the underlying Demo Day observation have not been independently verified.