Loop-based cognitive architecture boosts cheap coding agents' token efficiency, but not Opus
mathemagic1an · x · 2026-10-14
mathemagic1an shares a cognitive architecture for agents that goes beyond code: run decision models in a loop to process early environmental features and offload low-level computation from the exec function, with a full implementation published.
Key caveats:
- Traditional coding evals like SWE-bench focus on isolated tasks and miss this approach's benefits;
- It improves token efficiency for cheaper models (Luna) but doesn't help Opus;
- Further tuning is needed to maximize usefulness.
Related event: Spidermonkey architecture cuts steps 10% for weaker models(2 posts)→
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