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Review regressions before expanding the test." ], "measure": "Held-out quality, peak memory, training time, and full compute cost to the target quality.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2610.26828", "title": "Agent World Model in the Wild: Computer-Use Agent Environment Synthesis", "summary": "WildAWM replaces reliance on human-collected computer-use trajectories and hand-built training environments with an open-source-model pipeline that synthesizes executable GUI/MCP/CLI environments, tasks, and execution-based verifiers. 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