arXiv:2605.25903v1 Announce Type: new Abstract: Activation verbalization explains hidden representations in natural language, but existing methods are mostly limited to self-explanation, where each model explains only its own activations. We introduce Universal Activation Verbalizer (UAV), a framework that uses a shared decoder to explain activations from heterogeneous donor models. UAV learns a lightweight adapter that converts donor activations into soft tokens in decoder's embedding space, and further supports adapter-only transfer by reusing a frozen decoder-side LoRA while training only a
The proliferation of diverse AI models necessitates unified interpretation tools, and advancements in AI architecture allow for frameworks like UAV to address model heterogeneity.
This development can significantly improve the interpretability and transferability of AI model knowledge, crucial for debugging, safety, and combining capabilities across disparate systems.
Previously siloed model explanations can now be unified, allowing a single framework to explain activations from various models, fostering greater interoperability and understanding in complex AI environments.
- · AI developers
- · AI safety researchers
- · Multi-modal AI systems
- · Defense and intelligence sectors
- · Proprietary model 'black boxes'
Increased understanding and debugging efficiency across diverse AI models.
Accelerated development of more robust and interpretable multi-model AI systems and agents.
Potentially democratizes advanced AI capabilities by reducing the barrier to integrate and understand activations from highly specialized models.
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