Meta Releases CORAL Framework to Optimize Recommendation Systems Using Closed-Loop LLM Agents

Meta published the paper CORAL, proposing an LLM-native agent harness for closed-loop optimization of production-grade recommendation systems. Through closed-loop observation of runtime signals, retrieval of past decision memories, and invocation of tools such as numerical optimizers, the system continuously reconfigures online recommendation strategies within a fixed change budget. The paper demonstrates that the strategy progressively improves across iterative cycles without requiring parameter updates.