TraceMem: Trajectory-Induced Reusable Memory for Multimodal Reasoning

Multimodal reasoning is a key capability for general-purpose AI systems, requiring models to jointly interpret visual evidence, language instructions, and multi-step inference processes. However, multimodal reasoning models often repeat similar mistakes across related samples, suggesting a limited ability to transform past reasoning outcomes into reusable experience. While memory-based methods preserve prior episodes, they typically store them as weakly structured text, making retrieved memories less actionable. Test-time trajectory exploration can reveal successful and failed reasoning patterns, but these trajectories are usually discarded after the current prediction. We propose TraceMem, a trajectory-induced memory framework that converts reasoning trajectories into structured memory units with explicit triggers, procedural guidance, and avoidance rules. These units are maintained through four lifecycle-aware memory banks, allowing experience from successful and failed trajectories to be accumulated, updated, and reused. Experiments on four multimodal reasoning benchmarks show that TraceMem consistently improves performance, demonstrating the effectiveness of trajectory-induced structured memory for multimodal reasoning. The code is available at https://anonymous.4open.science/r/TraceMem-57C6/.