arxiv: 2605.02044 · v1 · submitted 2026-05-03 · 💻 cs.LG

Recognition: 1 theorem link

NeuroViz: Real-time Interactive Visualization of Forward and Backward Passes in Neural Network Training

Reza Rawassizadeh, Tanvi Sharma

Authors on Pith no claims yet

Pith reviewed 2026-05-08 19:25 UTC · model grok-4.3

classification 💻 cs.LG

keywords neural network visualizationinteractive visualizationforward passbackward passneural network trainingusability studymachine learning education

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The pith

NeuroViz enables real-time interactive visualization of forward and backward passes during neural network training to improve interpretability.

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

The paper introduces NeuroViz as a tool that lets users configure network architecture, activation functions, learning rates, and datasets while watching live activations, weight updates, and loss progression. It specifically visualizes how activation signals correspond to weight changes in both forward and backward passes, showing pre- and post-update states within epochs along with dynamically updating per-neuron equations. A comparative study with 31 participants against six other tools produced the highest usability score and reported that over 70 percent of users experienced substantially greater transparency in understanding training. This addresses the core difficulty that neural network training remains opaque to newcomers by making internal dynamics visible and explorable.

Core claim

NeuroViz is an interactive visualization tool that supports real-time exploration of fully connected neural network training by visualizing weight changes in direct correspondence with activation signals in both forward and backward passes, enabling users to distinguish pre- and post-update states within individual epochs and view dynamically updating per-neuron equations.

What carries the argument

Real-time interactive display of forward and backward passes that maps activation signals directly to weight updates while showing pre/post-update states and live per-neuron equations.

If this is right

Users can observe how specific activation signals drive particular weight adjustments during each training step.
Pre-update and post-update network states become distinguishable within the same epoch.
Live configuration of architecture, activation functions, learning rates, and datasets produces immediate visual feedback on training dynamics.
Perceptions of neural network training transparency increase for the majority of users according to the reported study.

Where Pith is reading between the lines

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

The visualization approach could support debugging by surfacing unexpected activation-to-weight relationships that are hard to spot in code.
Extending the same live mapping to convolutional or recurrent layers would test whether the core insight generalizes beyond fully connected networks.
Adding objective tests of users' ability to forecast loss curves or diagnose issues would provide stronger evidence than perceived transparency alone.

Load-bearing premise

A user study with 31 participants comparing NeuroViz to six other tools is assumed to supply sufficient evidence of superior clarity and usefulness without full details on study design, biases, or objective learning gains.

What would settle it

A follow-up experiment in which participants show no measurable improvement in explaining or predicting network behavior after using NeuroViz versus the compared tools.

Figures

Figures reproduced from arXiv: 2605.02044 by Reza Rawassizadeh, Tanvi Sharma.

**Figure 1.** Figure 1: NeuroViz visualizes the training on Iris dataset Abstract Training neural networks is difficult to interpret, particularly for newcomers. We introduce NeuroViz, an interactive visualization tool that supports real-time exploration of fully connected neural network training. Users can configure network architecture, activation functions, learning rates, and datasets, then observe activations, weight update… view at source ↗

**Figure 2.** Figure 2: Process flow of NeuroViz visualization tool 3.1 Dataset Selection and Configuration The workflow begins with a tabular dataset selection. Users can choose between built-in sample datasets or upload their own. The built-in options include the Iris dataset (a classification task with 4 features and 3 species classes) and a Diabetes dataset (a regression task with 6 normalized features). Upon selection, the i… view at source ↗

**Figure 3.** Figure 3: Visualization examples from NeuroViz on the Iris dataset. The “Training Progress” chart (Figure 3d) on the right bottom side of view at source ↗

**Figure 4.** Figure 4: Comparison of cognitive load (left), perceived trust (center), and usability (right) across tools. view at source ↗

read the original abstract

Training neural networks is difficult to interpret, particularly for newcomers. We introduce NeuroViz, an interactive visualization tool that supports real-time exploration of fully connected neural network training. Users can configure network architecture, activation functions, learning rates, and datasets, then observe activations, weight updates, and loss progression. NeuroViz visualizes weight changes in direct correspondence with activation signals in both forward and backward passes, enabling users to distinguish pre- and post-update states within individual epochs and view dynamically updating per-neuron equations. We conduct a comparative user study with 31 participants against six established visualization tools and we achieved the highest usability score (SUS 80.97, in the 'excellent' range), with mean rankings of 2.47 for clarity and 2.23 for usefulness (lower is better). Over 70% of participants reported that the visualizations substantially increased their perception of neural network training transparency. The implemented instance is accessible at https://neuroviz.org.

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.

Desk Editor's Note private letter to a colleague

NeuroViz adds a live link between forward activations and backward weight updates with per-neuron equations, plus solid usability scores, but the study evidence stays subjective.

read the letter

The main thing to know is that this paper builds a visualization tool called NeuroViz that lets you watch forward and backward passes happen in real time for a fully connected network, with per-neuron equations updating live. It's new in how it shows the direct link between activations in the forward pass and the corresponding updates in the backward pass, all while you can change the setup and see the effects immediately. That dynamic equation view per neuron is something I haven't seen exactly like this before. They did a user study with 31 people comparing it to six other tools and came out on top with an SUS score of about 81, which is excellent, plus strong marks for clarity and usefulness. Most users said it helped them see the training as more transparent. The weak part is that the study is all self-report. No mention of actual tests to see if people learned the concepts better, like identifying how a weight change affects loss. Without that or details on the experimental setup, the transparency claim rests on perception rather than measured improvement. It might still be useful, but the evidence is softer than it could be. This is the kind of paper that would interest folks who teach intro ML or students who want to poke around inside the training loop. Not for core theory researchers. It should go to peer review. The tool is concrete and the idea has merit; reviewers can help tighten the evaluation.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces NeuroViz, an interactive visualization tool for real-time exploration of fully connected neural network training. It enables configuration of architecture, activations, learning rates, and datasets, with visualizations of activations, weight updates, loss curves, forward/backward pass correspondences, pre/post-update states per epoch, and dynamically updating per-neuron equations. A comparative user study with 31 participants against six other tools reports the highest SUS score of 80.97 (excellent range), mean rankings of 2.47 for clarity and 2.23 for usefulness, and over 70% of participants reporting substantially increased perception of training transparency. The tool is publicly available at https://neuroviz.org.

Significance. If the evaluation holds, NeuroViz offers a practical, accessible resource for improving interpretability of NN training dynamics, particularly for educational use with newcomers. The open implementation and focus on linking weight changes directly to activation signals in both passes represent a concrete contribution to visualization tools in ML. The reported SUS score and participant feedback provide initial evidence of usability, though the broader impact depends on whether the visualizations deliver objective gains in understanding beyond self-reports.

major comments (2)

[User Study] User Study section: The comparative evaluation with 31 participants reports SUS 80.97 and >70% reporting increased transparency, but provides no details on task design (e.g., specific exercises on forward/backward passes or weight updates), counterbalancing of tool order, blinding, objective outcome measures (such as pre/post concept quizzes or error-identification tasks), or adjustments for multiple comparisons. This is load-bearing for the superiority claim, as subjective perceptions alone cannot distinguish genuine insight gains from novelty or polish effects.
[Abstract] Abstract and Evaluation: The headline usability metrics rest on a study whose protocol is not described sufficiently to evaluate controls or generalizability, directly limiting support for the claim that NeuroViz substantially increases perception of neural network training transparency compared to existing tools.

minor comments (2)

The manuscript would benefit from additional screenshots or figures explicitly showing the dynamic per-neuron equations and pre/post-update state distinctions to illustrate the core visualization features more concretely.
Consider adding a brief discussion of limitations, such as restriction to fully connected networks or potential scalability issues with larger architectures.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have revised the paper to address the concerns about the user study by expanding the methodological description. Our point-by-point responses to the major comments follow.

read point-by-point responses

Referee: [User Study] User Study section: The comparative evaluation with 31 participants reports SUS 80.97 and >70% reporting increased transparency, but provides no details on task design (e.g., specific exercises on forward/backward passes or weight updates), counterbalancing of tool order, blinding, objective outcome measures (such as pre/post concept quizzes or error-identification tasks), or adjustments for multiple comparisons. This is load-bearing for the superiority claim, as subjective perceptions alone cannot distinguish genuine insight gains from novelty or polish effects.

Authors: We agree that the original User Study section lacked sufficient methodological detail. In the revised manuscript we have expanded this section to describe the task design (participants performed specific exercises involving network configuration, observation of forward/backward passes, weight updates, and loss curves), the within-subjects design with counterbalancing of tool presentation order, and the fact that full blinding was not feasible given the interactive tools (though participants were not informed of hypotheses). The evaluation relied on standard subjective usability instruments (SUS, rankings, and self-reported transparency gains); we have added an explicit limitations paragraph noting the absence of objective measures such as pre/post quizzes and identifying this as future work. No multiple-comparison corrections were applied because the primary analyses were descriptive. These additions clarify the protocol and scope of the claims without altering the reported results. revision: yes
Referee: [Abstract] Abstract and Evaluation: The headline usability metrics rest on a study whose protocol is not described sufficiently to evaluate controls or generalizability, directly limiting support for the claim that NeuroViz substantially increases perception of neural network training transparency compared to existing tools.

Authors: We have revised the abstract to qualify the usability claims as arising from self-reported perceptions and to reference the expanded Evaluation section for protocol details. The revised text now makes clear that the transparency increase is participant-reported rather than objectively measured, thereby aligning the abstract with the limitations acknowledged in the main body. revision: yes

Circularity Check

0 steps flagged

No circularity: paper is tool description plus empirical user study with no derivations or self-referential predictions

full rationale

The paper introduces NeuroViz as an interactive visualization tool for neural network training and reports usability results from a 31-participant comparative study. No mathematical derivations, equations, parameter fitting, or first-principles predictions are present in the abstract or described claims. The central results (SUS score, rankings, participant reports) are presented as direct empirical outcomes from the study rather than outputs derived from the tool's own inputs or prior self-citations. No load-bearing steps reduce to self-definition, fitted inputs renamed as predictions, or uniqueness theorems imported from the authors' prior work. The paper is therefore self-contained with no circular reasoning in its derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As this is a description of a software visualization tool rather than a theoretical or empirical scientific result involving derivations, there are no free parameters, axioms, or invented entities underlying a central claim.

pith-pipeline@v0.9.0 · 5464 in / 1084 out tokens · 69702 ms · 2026-05-08T19:25:23.557821+00:00 · methodology

discussion (0)

Reference graph

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