Predicting post-surgical seizure outcomes in pharmacoresistant epilepsy is a clinical challenge. Conventional deep-learning approaches operate on static, single-timepoint pre-operative scans, omitting longitudinal morphological changes. We propose \emph{Neuro-Oracle}, a three-stage framework that: (i) distils pre-to-post-operative MRI changes into a compact 512-dimensional trajectory vector using a 3D Siamese contrastive encoder; (ii) retrieves historically similar surgical trajectories from a population archive via nearest-neighbour search; and (iii) synthesises a natural-language prognosis grounded in the retrieved evidence using a quantized Llama-3-8B reasoning agent. Evaluations are conducted on the public EPISURG dataset ($N{=}268$ longitudinally paired cases) using five-fold stratified cross-validation. Since ground-truth seizure-freedom scores are unavailable, we utilize a clinical proxy label based on the resection type. We acknowledge that the network representations may potentially learn the anatomical features of the resection cavities (i.e., temporal versus non-temporal locations) rather than true prognostic morphometry. Our current evaluation thus serves mainly as a proof-of-concept for the trajectory-aware retrieval architecture. Trajectory-based classifiers achieve AUC values between 0.834 and 0.905, compared with 0.793 for a single-timepoint ResNet-50 baseline. The Neuro-Oracle agent (M5) matches the AUC of purely discriminative trajectory classifiers (0.867) while producing structured justifications with zero observed hallucinations under our audit protocol. A Siamese Diversity Ensemble (M6) of trajectory-space classifiers attains an AUC of 0.905 without language-model overhead.

翻译：预测药物难治性癫痫术后癫痫发作结局是一项临床挑战。传统深度学习方法依赖静态的单时间点术前扫描，忽视了纵向形态学变化。我们提出Neuro-Oracle三阶段框架：(i) 利用3D孪生对比编码器将术前至术后MRI变化压缩为紧凑的512维轨迹向量；(ii) 通过最近邻搜索从群体档案中检索历史相似手术轨迹；(iii) 基于检索证据，利用量化Llama-3-8B推理代理合成自然语言预后。使用五折分层交叉验证在公开EPISURG数据集（268例纵向配对病例）上进行评估。由于缺乏真实癫痫自由评分，我们基于切除类型采用临床代理标签。需承认网络表征可能学习切除腔的解剖特征（即颞叶与非颞叶位置）而非真实预后形态学指标，因此当前评估主要作为轨迹感知检索架构的概念验证。基于轨迹的分类器AUC值达0.834-0.905，而单时间点ResNet-50基线为0.793。Neuro-Oracle代理（M5）匹配纯判别轨迹分类器AUC（0.867），并在审计协议下生成结构化论证且零观察幻觉。轨迹空间分类器的孪生多样性集成（M6）在无语言模型开销下达到0.905 AUC。