CertBind from Multimodal Connectivity to Certifiable Retrieval Decisions
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 6 Aug 2026.
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References · 10
- 2019Similarity of Neural Network Representations Revisitedcited by 1,755
- 2023ImageBind: One Embedding Space To Bind Them Allcited by 1,175
- 2023LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignmentcited by 370
- 2021Revisiting Model Stitching to Compare Neural Representationscited by 209
- 2022Relative representations enable zero-shot latent space communicationcited by 191
- 2020On Linear Identifiability of Learned Representationscited by 105
- 2024UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them Allcited by 49
- 2024OmniBind: Large-scale Omni Multimodal Representation via Binding Spacescited by 39
- 2019Robust Group Synchronization via Cycle-Edge Message Passingcited by 19
- 2026Text-Preserving Lossy Text Compression: A Study of Strategic Deletion and LLM Reconstructioncited by 1
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