Harness Self-Evolution for Long-Horizon Terminal Agents
Long-horizon terminal agents may solve a task in one run yet fail across repeated runs.
ProofPaper ↗
Key points
- Stochastic errors can propagate through actions and context, creating a gap between capability and reliable execution.
- We present RiseHarness, a framework for iteratively improving agent harnesses with model weights fixed.
- RiseHarness combines agent trajectories with internal harness traces from a runtime monitor to examine how model actions interact with command execution, feedback, context management, and completion decisions.
- A Harness Handbook links this evidence to harness behaviors and source code, helping identify recurring problems in mechanisms shared across tasks.
Sources (2)
- [1]Harness Self-Evolution for Long-Horizon Terminal AgentsHugging Face Daily Papers · Oct 20, 12:00 AM
Long-horizon terminal agents may solve a task in one run yet fail across repeated runs.
Stochastic errors can propagate through actions and context, creating a gap between capability and reliable execution.
- [2]Harness Self-Evolution for Long-Horizon Terminal AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 12:36 AM · same content
Extractive summary: sentences quoted from the sources.
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