Banger paper from ByteDance Seed. If you are curious about self-evolving agent harnesses, this one is worth your time. (bookmark it) The pro…

Banger paper from ByteDance Seed. If you are curious about self-evolving agent harnesses, this one is worth your time. (bookmark it) The proposes method, HarnessDev, stops scoring a model on the tasks it completes and scores it on the harness it builds. The agent starts from a weak but runnable seed plus a handful of cases, then builds a full execution system. A second stage hands that harness back and asks it to improve on downstream feedback. Both stages are scored on capability and on execution-token cost, so there is awareness of efficiency and spend. The experiments include six creator LLMs, four domains, 2,207 held-out downstream instances. The result splits by domain. Generated harnesses stay well behind mature human-engineered references on code and on search and research, while matching or beating them on writing and machine-learning experimentation. Evolution produces gains, but they are unstable and transfer only partially to held-out tasks, and they depend heavily on which model runs the harness. Paper: https://arxiv.org/abs/2609.01437 Chat with Paper: https://academy.dair.ai/papers/harnessdev-can-llms-create-and-evolve-their-own-agent-harness-2609.01437

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Source: DAIR.AI (X) | 2026-09-02