arXiv:2606.26891v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) promise transparent reasoning by predicting through human-interpretable concepts, yet their effectiveness fundamentally depends on how well visual and textual representations are aligned or matched. Existing vision-language CBMs often rely on pre-aligned encoders or global cosine similarity, which obscures fine-grained concept localization and fails to reflect true semantic geometry. In this work, we rethink concept alignment as a dynamic cross-modal transport process instead of static projection and propose the
The increasing complexity and demand for transparency in AI models necessitate more sophisticated methods for aligning cross-modal data representations, moving beyond simplistic approaches.
Improved concept alignment in AI models can lead to more robust, interpretable, and ultimately more trusted AI systems, impacting their deployment in sensitive applications.
The proposed 'dynamic cross-modal transport process' fundamentally rethinks how vision and language concepts are associated, potentially enabling more nuanced and accurate concept localization.
- · AI researchers in interpretable AI
- · Developers of multimodal AI applications
- · Industries requiring transparent AI (e.g., healthcare, autonomous driving)
- · Developers relying solely on global similarity for concept alignment
- · Systems with opaque AI reasoning
More accurate and interpretable Concept Bottleneck Models (CBMs) become feasible.
Enhanced CBMs could accelerate the adoption of AI in areas where explainability is critical, fostering greater public and institutional trust.
The ability to truly understand and localize concepts across modalities could lead to new forms of AI interaction and learning, potentially influencing artificial general intelligence pathways.
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Read at arXiv cs.AI