MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution

High-precision medical diagnosis relies not only on staticimaging features but also on the implicit diagnostic memory experts in-stantly invoke during image interpretation. We pinpoint a fundamentalcognitive misalignment in medical VLMs caused by discrete tokeniza-tion, leading to quantization loss, long-range information dissipation,and missing case-adaptive expertise. To bridge this gap, we proposeMedSynapse-V, a framework for latent diagnostic memory evolution thatsimulates the experiential invocation of clinicians by dynamically synthe-sizing implicit diagnostic memories within the model’s hidden stream.Specifically, it begins with a Meta Query for Prior Memorizationmechanism, where learnable probes retrieve structured priors from ananatomical prior encoder to generate condensed implicit memories. Toensure clinical fidelity, we introduce Causal Counterfactual Refine-ment (CCR) which leverages reinforcement learning and counterfactualrewards derived from region-level feature masking to quantify the causalcontribution of each memory, thereby pruning redundancies and aligninglatent representations with diagnostic logic. This evolutionary processculminates in Intrinsic Memory Transition (IMT), a privileged-autonomous dual-branch paradigm that internalizes teacher-branch di-agnostic patterns into the student-branch via full-vocabulary divergencealignment. Comprehensive empirical evaluations across multiple datasetsdemonstrate that MedSynapse-V, by transferring external expertise intoendogenous parameters, significantly outperforms existing state-of-the-art methods, particularly Chain-of-Thought (CoT) paradigms, in diag-nostic accuracy and multi-dataset generalization without compromisingthe inference efficiency of standard VLMs.