How Do Language Models Use Memory? From Internal Strategy Readout to Adaptive Control

Retrieval determines which memories a language model can access, but does not ensure their appropriate use. We investigate whether memory-use requirements can be read from a frozen language model's internal representations and used to control how memories influence its responses. Activation differences induced by individual memories encode information about required memory use, while the effect of isolating memories on readout accuracy varies across backbones. Oracle-guided interventions using these differences improve memory use, but struggle to jointly satisfy mixed requirements to enhance some memories and suppress others; stronger interventions can also induce repetitive generation. Motivated by these findings, we propose DUET (Dual-Adaptive Memory Control), which uses internal strategy readouts to specify control targets, updates memory-specific intervention directions at each generation step, and adapts intervention strengths to the resulting output-distribution changes, without updating model parameters. We evaluate DUET across six backbones and three benchmarks. On RPEval, it achieves 68.3% Multi Macro with Qwen3-8B and jointly satisfies all memory-use requirements in 50.0% of mixed queries. Ablations further show that dynamic directions and adaptive strengths jointly improve multi-memory control.