When to forget: risk-controlled context adaptation for long-horizon agents
In brief
- RECAP dynamically evaluates the value of past failures relative to the agent's current state.
- The framework enables selective retention, compression, or removal of history blocks to enhance performance.
- On Hunyuan-A13B, RECAP cuts prompt costs by 2.13% while maintaining a 91.89% success rate.
Researchers introduce RECAP, a framework to help long-horizon AI agents decide whether to keep, compress, or drop past failure records to improve future performance. The system dynamically assesses the value of past failures based on current execution states and adjusts memory accordingly.
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