Finding and using interpretable latents in a neutrino foundation model with sparse autoencoders

2026-08-26

AI Summary

研究使用稀疏自动编码器在中微子基础模型中找到可解释的潜变量，通过因果干预训练不确定性头部，提高角分辨率。

Abstract

We present a first application of sparse-autoencoder-based mechanistic interpretability to particle physics. Studying a neutrino foundation model pretrained on IceCube data and fine-tuned for direction reconstruction, we identify a validated atlas of physical concepts in the model representation, using a strict validation protocol consisting of held-out tests, matched nuisance controls, and replication across independent dictionary trainings. Causal interventions show that the direction head barely draws on this atlas. Motivated by this underused information, we train an uncertainty head on the same event-level representation to predict the model's angular reconstruction error. Unlike the direction head, it depends causally on quality and brightness features from the atlas. At $20\%$ selection efficiency, this interpretable estimator improves the median angular resolution from $20.2^\circ$ to $3.2^\circ$. These results suggest that mechanistic interpretability can reveal learned latent physics encoded within a model's internal representation and help design downstream tasks that exploit it.

Problem

• sparse autoencoders
• neutrino foundation model
• interpretable latents
• direction reconstruction
• angular reconstruction error

Innovation

• sparse autoencoders
• mechanistic interpretability
• latent physics
• uncertainty head
• angular resolution