AI 中文总结
该研究提出CertBind多尺度理论，解决多模态编码器组合后的检索决策可验证问题，通过多尺度设计实现误差控制与恢复，在实验中验证了其对原生检索能力的可控性与无损害性。
AI 中文摘要
轻量级连接器可让冻结的多模态编码器在表示层面实现组合，而部署则在任务决策层面暴露出第二个问题。连接的路由可扩展跨模态覆盖范围，同时改变已确立的原生检索能力。我们提出了CertBind，一种针对冻结多模态连接器图的可验证组合的多尺度理论：在节点尺度，原生锚点在所述图表模型下建立精确的任务识别边界；在边尺度，感知约束的共形秩提供图范围的族式误差控制；在路径尺度，感知重叠的预算与干净校准在声明条件下产生有限样本恢复半径；在查询尺度，该半径产生覆盖的前k个候选集，当大小等于k时成为点证书。因此，CertBind将支持的路由保留为“直接”，仅将标记的路由发送至恢复，对决定性恢复返回“已验证”，对未解决的查询返回“弃权（不执行）”。评估中，C-MCR共享路由将原生CLIP的R@1从0.524降至0.290，生产 fallback恢复了0.963±0.002的干净检索，而通过分支记录的无损害值为1.000。CertBind将多模态组合性从连接的表示扩展至可验证的任务决策。
英文摘要
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.
