Scaling Inference-Time Compute with Teams of Coding Agents
Multi-agent systems promise a new axis for scaling inference: rather than having one agent think longer or sample more attempts, a task is split across many agents that work in parallel and coordinate. However, in practice, more agents can easily produce duplicate work, coordination failures, and higher costs. We therefore formulate team design as a compute allocation problem: starting from a solo agent, where should additional inference compute go to reach the reward-cost Pareto frontier? We build a minimal multi-agent harness from the ground up and trace this frontier by varying, one at a time, the workflow stage at which a lead delegates to its workers (planning, implementation, or ver...
| Authors | Keenan Samway, Daniel D'souza, Omar Darwiche Domingues et al. |
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| Primary category | cs.MA |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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