Neural Model Mechanistic Interpretability Study

I have a fully-trained neural network that is delivering solid headline metrics, but I now need a rigorous mechanistic interpretation of how and where the model transforms inputs into its final outputs. If you hold a PhD in data science, statistics, or computer science and your research track record already covers explainable AI (XAI) and mechanistic interpretability, then this project will feel familiar.Do not reply otherwise .

Scope
• Deep-dive into the model I will provide (PyTorch, multi-layer transformer architecture).
• Trace and isolate the internal components—layers, heads, neurons, or circuits—that are directly responsible for specific behaviours I will flag ahead of time.
• Apply and, where necessary, develop techniques such as activation patching, attribution, probing, or sparse feature discovery to pinpoint causal pathways.
• Document findings clearly with annotated code, plotted visualisations, and concise commentary that links each observed mechanism to the outputs in question.

Deliverables
1. Jupyter notebook (or equivalent) containing reproducible analysis code.
2. Visual artefacts—heatmaps, attention flow diagrams, neuron/feature dashboards.
3. Written report (PDF/Markdown) summarising methods, results, limitations, and next research steps.

Acceptance criteria
• Each cited behaviour is traced to at least one verifiable internal mechanism with supporting evidence.
• All code executes end-to-end on the test set I supply and is sufficiently commented for another researcher to follow.
• Report communicates technical depth yet remains accessible to non-specialist stakeholders.

If your expertise matches these requirements, I look forward to collaborating and uncovering exactly how this model thinks.