Trajectories That Segment Themselves: Agent-Declared Boundaries as a Training Unit

Long-horizon coding-agent trajectories are poorly matched to the credit units available to train on: a single action has no stable value, an episode label merges productive exploration with abandoned directions, and a fixed window cuts where the logging mechanics fall. We introduce collection-time semantic self-segmentation, in which a declarative contract has the acting agent expose its own boundaries while the trajectory is generated. Instantiated with falsifiable causal hypotheses, successive

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ethics.ai (3 August 2026), “Trajectories That Segment Themselves: Agent-Declared Boundaries as a Training Unit,” evidence record 15876, https://ethics.ai/record/15876 (originally published by arXiv).

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