PRISM: Unified Neutrino Event Reconstruction via Task-Aware Retrieval and Geometry-Grounded Decoding

Neutrino telescopes infer multiple latent physical quantities from the same sparse, irregular spatio-temporal pulse array, yet heterogeneous reconstruction targets depend on fundamentally distinct observational evidence. We present PRISM, a unified reconstruction framework that addresses this challenge through a three-level decoupling: shared event representation, task-conditioned information retrieval, and targeted structural specialization. PRISM employs a Transport Skeletonizer (TSK) to maintain a compact event-level latent memory while preserving fine-grained hit representations, and learns task-aware queries to independently extract target-dependent features from the combined event-level and hit-level representations. For interaction-vertex localization, a geometry-aware coordinate anchor grounds the decoder with an explicit sensor-coordinate reference prior to unconstrained residual refinement. Across the formal NuBench benchmark, PRISM offers a favorable accuracy–efficiency trade-off: it achieves competitive performance with published per-cell reference results in selected energy and topology regimes, while requiring approximately $5\times$ fewer parameters and providing $4.8$–$5.0\times$ higher inference throughput than sequential multi-model execution. Rigorous equal-compute controls attribute a 32.65% reduction in median vertex error to coordinate anchoring, while ablations and controlled perturbation tests demonstrate task-dependent observation stability rather than universal robustness. Directional reconstruction, absolute vertex precision, and low-energy events remain key open challenges. Overall, our findings support a unified reconstruction strategy that shares expensive spatio-temporal representations, retrieves target-specific evidence, and introduces physically grounded specialization where uniform sharing is insufficient.