VRAVEN
Evidence-aware AI explainability · local first

Understand why your model decided.

VRAVEN is a PyTorch explainability and model-auditing toolkit. It turns predictions into inspectable evidence, tests internal mechanisms through controlled interventions, and produces reports that state what the evidence can—and cannot—support.

Explain your first modelExplore real explanations
Runs locallyPython 3.10+PyTorch 2.2+
VRAVEN raven calling while perched on a branch
Official markObserve · test · explain
57documented outputs
20core visual methods
175API symbols
22CLI commands
19DarkSide audits
Reference depth

Go from question to exact call.

Short paths for first use; complete generated references when you need precision.

OUTPUTS

57 result pages

Each visual or report gets its own code, real image, reading guide, evidence level and claim boundary.

PYTHON

Complete API

Generated from 175 exported symbols across the core, decompiler and DarkSide namespaces.

TERMINAL

Complete CLI

Every command, argument, default and direct --help route from the real parser.

Explainability with evidence

Move beyond a single attribution map.

Explain one prediction, test a suspected mechanism, compare models, or map reasoning across a dataset.

Acquire

Capture

Record activations, gradients, metadata and Mechanism IR from a real forward pass.

 Explain

Proof

Explain a single prediction with traceable evidence, competing signals and exportable visuals.

 Decompile

Reverse

Recover the decision programme, test its fidelity and issue causal or robustness certificates.

 Map

Atlas

Explore representation communities, similarity, outliers and reasoning landscapes across samples.

 Track

Watch

Measure how internal representations and decisions evolve across checkpoints.

 Audit

Compare + DarkSide

Contrast models and expose shortcuts, drift, failure anatomy, rerouting and uncertainty.
Public walkthrough

Evidence you can inspect.

These results come from torchvision MobileNetV2 and one openly licensed Labrador photograph.

VRAVEN MobileNetV2 decision flow
Decision flowSupporting and opposing first-order signals through 96 captured layers
VRAVEN MobileNetV2 intervention results
Intervention evidenceThree controlled layer-output interventions for the Labrador target
Follow the full walkthroughOpen the output gallery
A disciplined path

From model file to defensible claim.

01LoadModel or bundle
02ObserveActivations and paths
03InterveneChange and retest
04ReportEvidence + limits
The VRAVEN methodology
The VRAVEN methodology connects a trained model to an evidence-tested Decision Proof.
Visual Reasoning and Activation Visualisation for Explainable Networks
The name and the mark

A patient observer of hidden structure.

VRAVEN means Visual Reasoning and Activation Visualisation for Explainable Networks. The raven represents observation, memory, adaptability and problem-solving: qualities that match a system built to inspect a model carefully before making a claim.

Read the identity and origin story →
More than fifteen years of research · one product

VRAVEN could not have taken its present form without the research that came before it.

VRAVEN synthesises modern neural-network explanation research from 2010 to the present, translating ideas from local decision explanation, attribution, faithfulness, causal mechanisms, mechanistic interpretability and programme extraction into one usable, testable workflow. Every paper remains the work of its authors; VRAVEN's contribution is the careful synthesis, engineering and evidence discipline that connects those ideas.

Meet the intellectual foundations
Your model stays yoursVRAVEN runs locally, adds no telemetry and does not upload models or tensors. Reports disclose evidence type, coverage and unresolved computation.