💡 The paper proposes a new framework called MRAgent that improves the ability of large language model agents to reason over long interaction histories. Current memory-augmented agents struggle with this task because they rely on a static retrieve-then-reason approach, which prevents them from dynamically adapting memory access to new evidence discovered during inference. To address this issue, MRAgent combines an associative memory graph with an active reconstruction mechanism. The memory graph represents information as a network of cues, tags, and contents, where tags serve as semantic bridges between cues and contents. The active reconstruction mechanism integrates language model reasoning directly into memory access, allowing the agent to iteratively explore and refine retrieval paths based on accumulated evidence. This approach enables the agent to dynamically adapt memory retrieval to the reasoning context, avoiding the need to consider all possible retrieval paths and reducing computational costs. The authors evaluate MRAgent on two benchmarks, LoCoMo and LongMemEval, and demonstrate significant improvements over strong baselines, with up to 23% better performance, while also reducing token and runtime costs. Overall, the paper contributes a new framework for long-horizon memory reasoning that is more efficient and effective than existing approaches.