[2603.26458] Can AI Models Direct Each Other? Organizational Structure as a Probe into Training Limitations

Computer Science > Software Engineering arXiv:2603.26458 (cs) [Submitted on 27 Mar 2026] Title:Can AI Models Direct Each Other? Organizational Structure as a Probe into Training Limitations Authors:Rui Liu View a PDF of the paper titled Can AI Models Direct Each Other? Organizational Structure as a Probe into Training Limitations, by Rui Liu View PDF HTML (experimental) Abstract:Can an expensive AI model effectively direct a cheap one to solve software engineering tasks? We study this question by introducing ManagerWorker, a two-agent pipeline where an expensive "manager" model (text-only, no code execution) analyzes issues, dispatches exploration tasks, and reviews implementations, while a cheap "worker" model (with full repo access) executes code changes. We evaluate on 200 instances from SWE-bench Lite across five configurations that vary the manager-worker relationship, pipeline complexity, and model pairing. Our findings reveal both the promise and the limits of multi-agent direction: (1) a strong manager directing a weak worker (62%) matches a strong single agent (60%) at a fraction of the strong-model token usage, showing that expensive reasoning can substitute for expensive execution; (2) a weak manager directing a weak worker (42%) performs worse than the weak agent alone (44%), demonstrating that the directing relationship requires a genuine capability gap--structure without substance is pure overhead; (3) the manager's value lies in directing, not merely reviewi...