Workspace Optimization: How to Train Your Agent

• URL: http://arxiv.org/abs/2605.09650v1
• Date: Sun, 10 May 2026 16:52:10 GMT
• Title: Workspace Optimization: How to Train Your Agent
• Abstract summary: We argue it is the agent's emphworkspace, the structured external substrate it reads, writes, and tests.<n>We propose a principled way to evolve the workspace, mirroring the structure of weight-space training.<n>We instantiate the idea in DreamTeam, a multi-agent harness for ARC-AGI-3.
• Score: 49.579258311548635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern agents built on frontier language models often cannot adapt their weights. What, then, remains trainable? We argue it is the agent's \emph{workspace}, the structured external substrate it reads, writes, and tests; we call its evolution workspace optimization. Workspace optimization targets hard multi-turn environments where a frontier model has strong priors but cannot solve the task in a single shot, so the agent must learn through interaction. We propose a principled way to evolve the workspace, mirroring the structure of weight-space training: artifacts in place of parameters, evidence in place of data, counterexamples in place of losses, and textual feedback in place of gradients. We instantiate the idea in DreamTeam, a multi-agent harness for ARC-AGI-3 whose roles build an executable world model, plan, hypothesize, probe, strategize, and route failures. On the current 25-game ARC-AGI-3 public set under the official scoring protocol and averaged over two independent runs, DreamTeam improves the SOTA protocol-matched agent's score from 36% to 38.4%, while using 31% fewer environment actions per game.

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