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Matching papers

Recursive Synthesis for Long-Horizon Terminal Tasks

223 upvotes · 5 AUG 2026 · Zhongzhi Li, Yucheng Shi, Zongxia Li et al.

This paper introduces a method to generate long-horizon training tasks for terminal agents at scale, which can be used to improve AI models' performance on tasks like navigation and decision-making. Practitioners might care about this because it could help train more advanced AI models with better performance on complex tasks.

long-horizon tasksterminal agentsrecursive synthesisverified synthesisreinforcement learningdeep reinforcement learning

Mid-Harness: Scaling Actions Between Model and Harness for Terminal Agents

88 upvotes · 30 SEP 2026 · Minki Kang, Ryo Hachiuma, Shaokun Zhang et al.

This paper proposes a technique called Mid-Harness to improve the reliability of actions taken by terminal agents, which are models that act in environments by generating and verifying actions. By allocating test-time compute at the boundary between the model and harness, Mid-Harness can increase the success rate of actions and reduce the cost of testing.

continual learningterminal agentsstochastic model generationsaction verificationtest-time computereinforcement learning benchmarks