CertBind from Multimodal Connectivity to Certifiable Retrieval Decisions

2026-08-06

AI Summary

This work addresses the degradation in retrieval performance and lack of decision credibility observed when frozen multimodal encoders are deployed compositionally due to varying connection paths. To tackle this, the authors propose CertBind, a novel framework that extends multimodal compositionality from representation learning to certifiable task-level decisions. CertBind introduces a four-tier certification mechanism—spanning nodes, edges, paths, and queries—integrated with anchored boundary modeling, contract-aware conformal ranking, overlap-aware budget allocation, and clean calibration to construct a certified retrieval system with a finite-sample recovery radius. Experiments demonstrate that under shared-path C-MCR settings, CertBind recovers 96.3% of the original retrieval performance while achieving perfect branch accuracy (1.000), effectively balancing compositional extensibility with decision reliability.

Abstract

Lightweight connectors make frozen multimodal encoders composable at the representation level. Deployment exposes a second problem at the level of task decisions. A connected route can expand cross-modal reach while changing an established native retrieval capability. We introduce CertBind, a multiscale theory of certifiable composition for frozen multimodal connector graphs. At the node scale, native anchors establish the exact task identification boundary under the stated chart model. At the edge scale, contract-aware conformal ranks provide graph-wide family-wise error control. At the path scale, an overlap-aware budget and clean calibration yield a finite-sample recovery radius under declared conditions. At the query scale, this radius yields a covered top-k candidate set that becomes a point certificate when its size equals k. CertBind therefore retains supported routes as Direct, sends only flagged routes to recovery, returns Certified for decisive recovery, and returns Abstain for unresolved queries. The evaluated C-MCR shared route reduced native CLIP R@1 from 0.524 to 0.290. The production fallback recovered 0.963 +- 0.002 of clean retrieval, while the passing branch recorded a no-harm value of 1.000. CertBind extends multimodal composability from connected representations to certifiable task decisions.

Problem

• multimodal composition
• certifiable retrieval
• frozen encoders
• task decisions
• retrieval degradation

Innovation

• Certifiable composition
• Multimodal connectors
• Conformal prediction
• Family-wise error control
• Retrieval certification