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It is found that starting performance and subsequent growth are associated with different capabilities: within a task category, similar early scores can precede different later gains, and a continuation policy to save time and compute with limited score loss is derived.
A time-truncation harness is proposed that enforces a temporal cut-off at every turn, enabling TIR-style sampling from historical events, enabling TIR-style sampling from historical events, reducing temporal leakage and reliance of rejection sampling or unsolved queries, increasing the sampling efficiency.
A systematic evaluation of seven frontier models on 36 long-horizon tasks based on a new framework that uses rule-based metrics to characterize within-run behavior through Solution Framing, Execution, and Feedback Control and controlled comparisons to assess experience reuse within and across tasks is presented.
Co-Harness is introduced, a framework that jointly optimizes the agent harness and model parameters during post-training and suggests that joint harness and model optimization is an effective way to improve agents beyond fixed-harness post-training.
ToFu is presented, an agentic harness for researchers that reads your codebase, edits files, runs commands, and integrates with your development tools and provides a white-box agentic harness that allows researchers to inspect, modify, and evaluate its orchestration logic, tool-use behavior, and harness design.
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