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This paper systematically investigates two principal test-time scaling axes of LLM agents: sequential scaling through extended interaction and parallel scaling through trajectory sampling, and finds that neither scaling axis can consistently yield meaningful gains from additional test-time compute in realistic environm...
This work builds an agent harness that separates reasoning from perception and improves by evolving context rather than optimizing weights, and proposes a gradient-free, reward-gated Heuristic Skill Distillation loop that mines the agent's own low-scoring traces and keeps a candidate skill only when it raises a validat...
This work proposes Agentic Context Management (ACM), a framework that equips agents with purpose-built context editing tools for lossless context management and develops a post-training pipeline that constructs high-quality demonstrations of context management and improves model performance on both agentic search and c...
This work introduces Harness-R1, the first method, to the authors' knowledge, that makes failure-conditioned, lifecycle-wide editing of an existing executable runtime a learned capability, and post-trains a dedicated harness engineer with online reinforcement learning so that its edits are optimized for the realized ta...
SkillGate lifts a 9B policy from 40.8% to 53.2% trial success, well ahead of the identical budget spent on outcome reward alone, while cutting exposure to misleading candidates by two thirds and reading fewer skills.
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