# OpenLoopEvolve: A Verifiable Self-Evolution Framework for Loop Policies in Long-Horizon Complex Tasks

arXiv: https://arxiv.org/abs/2608.09380 · HTML: https://mlsanity.com/paper/2608.09380/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 23 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Builds on (most cited first, 19 of 19)

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- [textttYC-Bench: Benchmarking AI Agents for Long-Term Planning and Consistent Execution](https://mlsanity.com/paper/2604.01212/) (2026)
- [Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting](https://mlsanity.com/paper/2607.00038/) (2026)
- [Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control](https://mlsanity.com/paper/2607.14890/) (2026)
- [ChaCha for Online AutoML](https://mlsanity.com/paper/2106.04815/) (2021)

## Abstract (arXiv)

Long-horizon complex tasks require agents to repeatedly observe states, formulate plans, invoke tools, verify results, and recover from failures in continuously changing environments. However, such control experience often remains confined to a single context or a fixed prompt, and is difficult to accumulate and reuse across historical traces. This paper presents OpenLoopEvolve (OLE), a self-evolution framework centered on the Loop Policy. OLE represents an agent's observation, planning, memory, action, verification, recovery, stopping, and budget control as portable policy assets with versions and lineages, and provides online and offline evolution modes that can be selected according to practical needs: the online mode triggers candidate generation based on feedback from continuous operation, whereas the offline mode searches for candidate policies from archived traces and failure evidence. Both modes share an evolution mechanism consisting of autonomous proposals by a large language model, Champion--Challenger paired evaluation, and robust release. Policies released online are activated at a subsequent task boundary, monitored using subsequent feedback, and rolled back to their parent versions when degradation conditions are met. On the simulated business benchmark YC-Bench, both modes improve aggregate task performance, task success rate, and risk metrics relative to a fixed initial Loop Policy. The results indicate that treating the Loop Policy as a governable asset can support the accumulation, comparison, release, and reuse of control experience and improve agent performance on long-horizon complex tasks.