Zhening Li, Joshua Liu, Mateja Vukelic, Nicole Shen, Supriya Lall, Amitayush Thakur, and colleagues at MIT CSAIL introduce JAZ, an agent framework that utilizes a single primitive called `invoke` to perform tasks typically requiring specialized memory or self-improvement systems. By treating the LLM as a runtime provider for function implementations through executable code, the framework allows all inputs and interaction histories to act as variables in the environment.
- The system uses "hooks" instead of dedicated subsystems like file systems or external memory stores to apply constraints and monitoring.
- JAZ outperformed Letta (MemGPT) by 8% at half the cost on recall-heavy tasks within the StuLife dataset.
- In self-improvement evaluations on AppWorld, JAZ exceeded ACE performance by 4% while maintaining a lower cost.
- The system uses "hooks" instead of dedicated subsystems like file systems or external memory stores to apply constraints and monitoring.
- JAZ outperformed Letta (MemGPT) by 8% at half the cost on recall-heavy tasks within the StuLife dataset.
- In self-improvement evaluations on AppWorld, JAZ exceeded ACE performance by 4% while maintaining a lower cost.
