Universal instruction switching beyond the evaluated transfer datasets

Determine whether instruction-conditioned multimodal embeddings support reliable instruction switching across new domains, attribute combinations, natural long-tailed product pools, and cross-type grouping tasks beyond the evaluated ProductMIC, Cards, CLEVR-4, and CIFAR10-MC settings.

Background

The transfer experiments show that product-trained encoders can recover some out-of-domain partitions, but performance varies substantially by dataset, attribute, and training configuration. In particular, CLEVR-4 exhibits stronger conditional response than CIFAR10-MC, so improved representation quality does not guarantee robust switching between instructions.

The paper explicitly limits its conclusions regarding generalization: new attribute combinations, natural long-tailed pools, and cross-type grouping are outside the evaluated scope. Establishing whether the method can switch reliably under these broader conditions remains unresolved.

References

The three transfer datasets support some out-of-domain grouping, but do not establish universal instruction switching: CLEVR-4 shows stronger conditional response than CIFAR10-MC.

— CIM-Embed: Learning Instruction-Conditioned Multimodal Embeddings for Clustering  (2610.21805 - Xu et al., 15 Oct 2026) in Appendix A, Section “Extended Limitations,” subsection “Generalization”