DynamicLRP: The Future of Explainable AI (No Code Needed)

Описание: 🚀 Ready to explain ANY AI model—no code, no hacks, no exceptions? Meet *DynamicLRP**: the first truly model-agnostic explainability framework that works on *any neural architecture—ViTs, Transformers, Mamba, Whisper, and beyond—using just **47 universal operation rules**.

No more hand-crafted layer rules for attention, convolutions, or normalization. No more retraining for new models. DynamicLRP operates at the **tensor operation level**—addition, multiplication, softmax, attention—so it scales with AI evolution, not against it.

🔥 The secret? The *Promise System**: when a forward activation is missing (thanks to auto-diff limitations), nodes don’t stall—they *promise to return later. Lazy evaluation. No retraversal. No backtracking. Just seamless, efficient relevance propagation.

✅ 99.92% node coverage across 15 models (RoBERTa, Flan-T5, VGG, Mamba, DePlot & more)
✅ Faithfulness on par with specialized tools: *ABPC 1.77 on VGG**, **95% SQuAD accuracy* for Flan-T5
✅ Runs fast on consumer GPUs—no heavy infrastructure needed

This isn’t just a tool. It’s a *principled leap forward* in explainable AI. One implementation. Zero model-specific code. Infinite scalability.

📌 Perfect for researchers, engineers, and AI enthusiasts who want **deep, trustworthy insights**—without getting lost in architecture-specific complexity.

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Read more on arxiv by searching for this paper: 2512.07010v1.pdf