StateHarness: Maintaining Evolving Project State for Long-Horizon Coding Agents

Key points

• Long-horizon coding requires agents to maintain an evolving view of the project they are changing: what problem is currently being solved, what has already been learned or modified, which other artifacts may be affected, and what work remains unresolved.1
• When this information is scattered across a growing interaction history, local edits can become disconnected from their later consequences and progress can be difficult to carry forward.1
• Across three repository-level evaluations, StateHarness improves task completion and several measures of change consistency.1
• These results show that maintaining project state can improve long-horizon coding while introducing measurable execution and regression trade-offs.1

Sources (1)

• [1]StateHarness: Maintaining Evolving Project State for Long-Horizon Coding Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 02:39 AM

  We introduce StateHarness, a runtime framework that maintains evolving project state without updating model parameters.

  Long-horizon coding requires agents to maintain an evolving view of the project they are changing: what problem is currently being solved, what has already been learned or modified, which other artifacts may be affected, and what work remains unresolved.

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