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  "text": "NEW research from CMU.\n\n(bookmark this one)\n\nThe biggest unlock in coding agents is understanding strategies for how to run them asynchronously.\n\nSimply giving a single agent more iterations helps, but does not scale well.\n\nAnd multi-agent research shows that coordination > compute.\n\nA new paper from CMU proves this with a practical multi-agent system.\n\nCAID (Centralized Asynchronous Isolated Delegation) borrows proven human SWE practices: a manager builds a dependency graph, delegates tasks to engineer agents who work in isolated git worktrees, execute concurrently, self-verify with tests, and integrate via git merge.\n\nCAID improves accuracy over single-agent baselines by 26.7% absolute on paper reproduction tasks (PaperBench) and 14.3% on the Python library\ndevelopment tasks (Commit0).\n\nThe key insight is that isolation plus explicit integration beats both single-agent scaling and naive multi-agent approaches.\n\nFor long-horizon software engineering tasks, multi-agent coordination using git-native primitives should be the default strategy, not a fallback.\n\nPaper: https://t.co/cRAbG7SrR5\n\nLearn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX",
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