Paper page - Scaling Test-Time Compute for Agentic Coding

Source: https://huggingface.co/papers/2604.16529 Authors:

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Abstract

Test-time scaling framework for agentic coding uses compact trajectory representations and recursive voting/parallel-distill-refine methods to improve long-horizon task performance.

Test-time scalinghas become a powerful way to improve large language models. However, existing methods are best suited to short, bounded outputs that can be directly compared, ranked or refined. Long-horizon coding agents violate this premise: each attempt produces an extended trajectory of actions, observations, errors, and partial progress taken by the agent. In this setting, the main challenge is no longer generating more attempts, but representing prior experience in a form that can be effectively selected from and reused. We propose atest-time scalingframework foragentic codingbased on compact representations ofrollout trajectories. Our framework converts each rollout into a structured summary that preserves its salient hypotheses, progress, and failure modes while discarding low-signal trace details. This representation enables two complementary forms of inference-time scaling. For parallel scaling, we introduceRecursive Tournament Voting(RTV), which recursively narrows a population of rollout summaries through small-group comparisons. For sequential scaling, we adaptParallel-Distill-Refine(PDR) to the agentic setting by conditioning new rollouts on summaries distilled from prior attempts. Our method consistently improves the performance of frontier coding agents acrossSWE-Bench VerifiedandTerminal-Bench v2.0. For example, by using our method Claude-4.5-Opus improves from 70.9% to 77.6% onSWE-Bench Verified(mini-SWE-agent) and 46.9% to 59.1% onTerminal-Bench v2.0(Terminus 1). Our results suggest thattest-time scalingfor long-horizon agents is fundamentally a problem of representation, selection, and reuse.

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