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Extending the AI agent test for encoding: turn trajectories into reusable structured summaries

MeNews
AIMPACT message, April 26 (UTC+8). Recently, a new study proposed a test-time expansion framework for long-horizon encoding agents. The framework converts an agent’s execution trajectory into structured summaries, 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 the candidate summary set; sequential expansion adapts the Parallel-Distribution-Distillation-and-Refinement (PDR) method to agent scenarios, 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 improves Terminus 1 performance from 46.9% to 59.1%. The article argues that test-time expansion for long-horizon agents is fundamentally a question of representation, selection, and reuse. (Source: InFoQ)
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