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arxiv: 2509.08461 · v4 · submitted 2025-09-10 · 💻 cs.LG · cs.AI· cs.CV· hep-ex

Adapting Vision-Language Models for Neutrino Event Classification in High-Energy Physics

Dikshant Sagar , Kaiwen Yu , Alejandro Yankelevich , Jianming Bian , Pierre Baldi This is my paper

Pith reviewed 2026-05-18 17:17 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CVhep-ex
keywords neutrino event classificationvision-language modelshigh-energy physicstransformer architecturesmachine learning for HEPdetector image analysismodel interpretability
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The pith

Vision-language models adapted from LLaMA 3.2 classify neutrino interactions more accurately and with clearer reasoning than standard CNNs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tests whether vision-language models can handle the task of sorting neutrino events in pixelated detector images from high-energy physics experiments. The authors fine-tune a version of LLaMA 3.2 and compare it directly to a CNN architecture already used in real neutrino experiments plus a vision-only transformer. They report that the transformer family, especially the VLM, reaches higher accuracy and holds up better under variations while also producing explanations that draw on both image features and language prompts. A reader would care because neutrino detectors produce enormous data volumes where reliable event identification directly affects what physics results can be extracted.

Core claim

By fine-tuning LLaMA 3.2 as a vision-language model on pixelated detector data, the work establishes that this multimodal architecture classifies electron and muon neutrino events with better accuracy and robustness than a conventional CNN baseline or a vision-only ViT-h/14 encoder, while the language component supplies auxiliary semantic information and generates step-by-step reasoning for each prediction.

What carries the argument

The fine-tuned LLaMA 3.2 vision-language model that combines a vision transformer encoder with a language model to accept both detector images and textual prompts for joint classification and explanation.

If this is right

  • Transformer architectures deliver higher classification accuracy and robustness than CNNs on neutrino detector images.
  • The VLM gains flexibility by accepting auxiliary textual or semantic information alongside the images.
  • Predictions become more interpretable because the model can output explicit reasoning steps.
  • Large transformer models can serve as general-purpose backbones for event classification across physics experiments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar fine-tuning could extend to other image-like data streams in particle physics such as calorimeter or tracking detector outputs.
  • The built-in reasoning might help flag rare or unexpected event topologies that lack dedicated training labels.
  • Integration with existing simulation and reconstruction software could create hybrid pipelines where language prompts guide analysis choices.

Load-bearing premise

The claim rests on the premise that any performance advantage comes from the VLM design itself rather than from larger training data volumes or more extensive hyperparameter tuning than what was given to the CNN and ViT baselines.

What would settle it

A controlled re-run of the three models on identical neutrino datasets, identical data splits, and matched training schedules that checks whether the reported accuracy and interpretability edges remain.

Figures

Figures reproduced from arXiv: 2509.08461 by Alejandro Yankelevich, Dikshant Sagar, Jianming Bian, Kaiwen Yu, Pierre Baldi.

Figure 1
Figure 1. Figure 1: LLaMa 3.2 Vision finetuning pipeline. 2.2 LLaMa 3.2 Vision LLaMA Vision 3.2 is a suite of multimodal large language models developed by Meta, extending the LLaMA 3.2 series with visual capabilities [3]. Unlike traditional CNNs tailored specifically for image-based tasks, LLaMA Vision 3.2 integrates both textual and visual modalities within a unified transformer-based architecture[24]. It is trained on a di… view at source ↗
Figure 2
Figure 2. Figure 2: LLaMa 3.2 Vision Classification Inference Pipeline [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 2
Figure 2. Figure 2: LLMs or VLMs predict text by autoregressively generating one token at a time, conditioning each new token on the input prompt as well as all previously generated tokens. Given an input prompt and accompanying visual informa￾tion, the model outputs a probability distribution over the vocabulary for each decoding step, selecting the most likely tokens sequentially [1], [2]. However, unconstrained generation … view at source ↗
Figure 3
Figure 3. Figure 3: LLaMa 3.2 Vision Prediction Explanation. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: CNN Pipeline 2.3.2 Training Setup We train the convolutional baseline model using a supervised learning framework. The input to the network consists of 2 grayscale detector images with a resolution of 512 × 512. Each training sample consists of a pair of images corresponding 9 [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Finetuned LLaMa 3.2 Vision’s (a) recall matrix (truth normalized) [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: CNN model’s (a) recall matrix (truth normalized) and (b) precision [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: ROC curves for each class (a) νµ CC, (b) νe CC, and (c) NC comparing performance between the finetuned LLaMa 3.2 Vision and the CNN. CC e CC Neutral Current Predicted label CC e CC Neutral Current True label 0.79 0.09 0.12 0.01 0.91 0.08 0.06 0.10 0.84 (a) CC e CC Neutral Current Predicted label CC e CC Neutral Current True label 0.93 0.08 0.15 0.02 0.85 0.09 0.05 0.07 0.75 (b) [PITH_FULL_IMAGE:figures/fu… view at source ↗
Figure 8
Figure 8. Figure 8: Finetuned LLaMa 3.2 Vision’s (a) recall matrix (truth normalized) [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Siamese MobileNet model’s (a) recall matrix (truth normalized) and [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: ROC curves for each class (a) νµ CC, (b) νe CC, and (c) NC comparing performance between the finetuned LLaMa 3.2 Vision and the CNN for generalization testing. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
read the original abstract

Recent advances in Large Language Models (LLMs) have demonstrated their remarkable capacity to process and reason over structured and unstructured data modalities beyond natural language. In this work, we explore the applications of Vision Language Models (VLMs), specifically a fine-tuned variant of LLaMA 3.2 to the task of identifying neutrino interactions in pixelated detector data from high-energy physics (HEP) experiments. We benchmark this model against a state-of-the-art convolutional neural network (CNN) architecture, similar to those used in major neutrino experiments, which have achieved high efficiency and purity in classifying electron and muon neutrino events, and also a Vision Transformer (ViT-h/14), which is the same architecture inside the VLM's vision encoder. Our evaluation considers both classification performance and interpretability of the model predictions, comparing a VLM with a vision-only transformer (ViT) and a convolutional neural network (CNN) baseline. We find that transformer-based architectures outperform conventional CNNs in classification accuracy and robustness, with the VLM providing additional flexibility through the integration of auxiliary textual or semantic information and enabling more interpretable, reasoning-based predictions. These results highlight the potential of large transformer models, particularly vision-language models, as general-purpose backbones for physics event classification, combining strong performance, robustness, and interpretability, and opening new avenues for multimodal reasoning in experimental neutrino physics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript explores adapting a fine-tuned LLaMA 3.2 Vision-Language Model for classifying neutrino interactions in pixelated high-energy physics detector data. It benchmarks the VLM against a state-of-the-art CNN (similar to those in major neutrino experiments) and the ViT-h/14 vision encoder, claiming that transformer-based models outperform CNNs in accuracy and robustness while the VLM adds flexibility via auxiliary textual/semantic information and enables more interpretable, reasoning-based predictions.

Significance. If the performance and interpretability claims hold under controlled conditions, the work could be significant as an early demonstration of VLMs as general-purpose backbones for HEP event classification. The multimodal aspect and reasoning capability represent a potential advance over vision-only models, with possible implications for robustness and new analysis workflows in neutrino physics.

major comments (2)
  1. [Abstract] Abstract: the central claim that 'transformer-based architectures outperform conventional CNNs in classification accuracy and robustness' and that the VLM provides 'additional flexibility' and 'more interpretable, reasoning-based predictions' is presented without any quantitative metrics (accuracy, AUC, efficiency/purity), dataset sizes, or error bars. This absence is load-bearing because the entire contribution rests on the empirical comparison.
  2. [Experimental Setup] Experimental Setup / Benchmarking description: no information is given on training data volume, epoch counts, learning-rate schedules, batch sizes, or hyperparameter tuning protocols applied to the CNN baseline versus the VLM (or the shared ViT-h/14 encoder). Without these details it is impossible to attribute any observed edge to the VLM architecture or multimodal fusion rather than differences in optimization or data exposure.
minor comments (2)
  1. [Results] The manuscript would benefit from a dedicated table or figure summarizing the classification metrics (accuracy, precision, recall, F1) across all three models on the same test set.
  2. [Methods] Clarify whether the textual prompts used with the VLM are fixed templates or learned; this detail affects the claimed advantage of 'auxiliary textual or semantic information'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which help strengthen the clarity and rigor of our manuscript. We address each major comment point by point below, outlining specific revisions where appropriate while defending the core contributions on substance.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'transformer-based architectures outperform conventional CNNs in classification accuracy and robustness' and that the VLM provides 'additional flexibility' and 'more interpretable, reasoning-based predictions' is presented without any quantitative metrics (accuracy, AUC, efficiency/purity), dataset sizes, or error bars. This absence is load-bearing because the entire contribution rests on the empirical comparison.

    Authors: We agree that the abstract would be strengthened by the inclusion of quantitative metrics to support the central claims. In the revised manuscript, we will update the abstract to report key performance figures such as classification accuracy, AUC, efficiency/purity, dataset sizes, and error bars from the experiments. This revision will make the empirical basis of the claims immediately accessible to readers without altering the overall narrative. revision: yes

  2. Referee: [Experimental Setup] Experimental Setup / Benchmarking description: no information is given on training data volume, epoch counts, learning-rate schedules, batch sizes, or hyperparameter tuning protocols applied to the CNN baseline versus the VLM (or the shared ViT-h/14 encoder). Without these details it is impossible to attribute any observed edge to the VLM architecture or multimodal fusion rather than differences in optimization or data exposure.

    Authors: The referee is correct that additional detail on training protocols is necessary for reproducibility and to support attribution of performance differences. The current manuscript provides a high-level description of the benchmarking but lacks the requested specifics. We will expand the Experimental Setup section to include training data volumes, epoch counts, learning-rate schedules, batch sizes, and hyperparameter tuning procedures applied to the CNN, ViT-h/14, and VLM. These additions will clarify that comparisons were performed under consistent conditions. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmarking study with external baselines

full rationale

The paper is an empirical comparison of a fine-tuned LLaMA 3.2 VLM against a CNN baseline and the identical ViT-h/14 vision encoder for neutrino event classification in HEP data. No mathematical derivation chain, equations, or first-principles results are claimed or present. Performance claims rest on reported accuracy, robustness, and interpretability metrics evaluated against stated external baselines. These metrics are externally falsifiable and do not reduce to self-definition, fitted inputs renamed as predictions, or self-citation chains. The study is self-contained against the provided benchmarks; any concerns about unequal training regimes fall under experimental fairness rather than circularity in a derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are described in the provided text.

axioms (1)
  • domain assumption Pixelated detector outputs can be treated as images suitable for standard vision encoders.
    Implicit in the decision to feed detector data directly into a ViT-based vision encoder.

pith-pipeline@v0.9.0 · 5797 in / 1221 out tokens · 20704 ms · 2026-05-18T17:17:52.879893+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training

    hep-ex 2026-04 conditional novelty 6.0

    Self-supervised pre-training on multimodal neutrino detector simulations produces reusable representations that improve downstream classification, regression, and data efficiency over training from scratch.

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