The Trap of Trajectory: Towards Understanding and Mitigating Spurious Correlations in Agentic Memory
Researchers identify a critical vulnerability in agentic memory systems where Large Language Models retrieve and amplify spurious correlations from stored information, leading to erroneous reasoning in downstream decisions. The study benchmarks this risk and proposes CAMEL, a lightweight calibration method that mitigates spurious pattern reliance while maintaining performance on clean data.