Capability map

Start with the question.

VRAVEN is not one explainer. It is a connected observatory for capturing evidence, explaining decisions, testing mechanisms and comparing model behaviour.

Quick routeOne prediction → Proof. An executable approximation → Reverse. Many samples → Atlas. Many checkpoints → Watch. Two models → Compare. Failure and risk investigation → DarkSide.
01 · Acquire

Capture

observational

Instrument a real PyTorch forward pass and preserve the internal state needed by later analyses.

  • Activations, gradients, attention and layer metadata
  • Mechanism IR and reproducible capture summaries
  • Trace identity for connecting every result to its source execution
02 · Explain

Proof

single prediction

Explain one decision with linked visual evidence rather than an isolated saliency map.

  • Attribution, relevance, causal pathways and reasoning graphs
  • Counterfactual deltas, perturbation stability and mechanism concordance
  • A combined HTML explanation report or one selected export at a time
Browse the 20 core visuals →
03 · Decompile

Reverse

interventional

Recover an executable decision programme, then test whether it reproduces the source model under declared coverage.

  • Decision Proof with agreement, fidelity and coverage
  • Adversarial mechanism audit and empirical robustness profile
  • Formal robustness and causal-mechanism certificates when prerequisites hold
  • Seven signature views: Raven Eye, Flight, Brain, Code, Contrast, Shadow and Dark Matter
04 · Map

Atlas

cohort

Move from one decision to a population view of the representation space.

  • Activation communities and reasoning landscapes
  • Sample similarity, clusters and representation outliers
  • Dataset-level evidence with sample identities retained
05 · Track

Watch

checkpoints

Follow internal evolution across training checkpoints or model versions.

  • Representation drift and decision changes
  • Checkpoint-aware traces and model-evolution reports
  • Pair with Compare when architectures or trained variants differ
06 · Contrast

Compare

multi-model

Place two models or runs on the same evidence basis and expose where their mechanisms agree or diverge.

  • Prediction, confidence and internal-path comparison
  • Comparable visual scales and explicit provenance
  • Model-comparison report for review or publication
07 · Audit

DarkSide

risk

Investigate the behaviours ordinary explanation dashboards miss.

  • Shortcuts, failure anatomy, adversarial rerouting and explanation uncertainty
  • Prediction depth, unused capacity, circuit multiplicity and causal synergy
  • Representation drift, mechanism transfer and high-confidence out-of-distribution behaviour
Browse all 18 audits →

Cross-cutting tests

VERIFY

Certification

Separate empirical results from formal claims and unmet prerequisites.