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Extending intelligent agent testing: turning trajectories into reusable structured summaries

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Abstract generation in progress
AIMPACT message, April 26 (UTC+8). Recently, a new study proposed a test-time expansion framework for long-horizon coding agents. The framework converts an agent’s execution trajectory into a structured summary, preserving key assumptions, progress, and failure modes, while discarding low-signal details. The framework supports two expansion approaches: parallel expansion uses recursive tournament voting (RTV) to recursively narrow down the candidate-summary set; sequential expansion adapts the parallel-distillation-refinement (PDR) method to the agent setting, using prior summaries to guide the generation of new trajectories. On the SWE-Bench Verified and Terminal-Bench v2.0 benchmarks, using the Claude-4.5-Opus model, the method improves mini-SWE-agent performance from 70.9% to 77.6%, and raises Terminus 1 performance from 46.9% to 59.1%. The article’s view is that test-time expansion for long-horizon agents is essentially a problem of representation, selection, and reuse. (Source: InFoQ)
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