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A novel two-axis framework unifies the study of multimodal agent memory, revealing distinct tiers of memory adaptation from static reuse to self-evolving policies.
FairCompressAgent (FCA) is proposed, an agentic framework that integrates fairness-aware pruning, incremental quantization, and sparse low-rank factorization through a common operator interface and demonstrates how measured feedback and explicit constraints support the selection and interactive refinement of fairness-aware compression configurations.