MAttr

I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.

## Technique: MAttr
New attribution method tops interpretability benchmark by 2.9x, pinpoints Llama refusals to 1% of weights

MAttr: attribution method identifying refusal behavior to just 1% of model weights — 2.9x better than prior methods on interpretability benchmarks. Control without retraining. Highly relevant to production LLM deployments for behavior steering and alignment. Score 8: implementable interpretability technique, but primary contribution is empirical (identifying weight subset) rather than a drop-in production pattern.

What the reference demo shows: 5-sub-tab Gradio demo — (1) Method Overview: gradient × activation attribution algorithm; (2) Weight Map: 32×32 attention head heatmap showing the ~1% refusal heads; (3) Attribution Demo: interactive prompt analysis with head activation scores; (4) Benchmark Comparison: MAttr vs LIME/SHAP/Int. Gradients bar chart; (5) Implementation Patterns: 3 code patterns for production behaviour steering.

## Reference implementation (Vector Ridge Labs, POC-041)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-041
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/mattr_attribution
Original source: AlphaSignal Signal · 2026-09-24 · 931 likes

## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">

## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures agreement of the new metric with human judgement on a sample of my outputs, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.

Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.