This paper is written for teams that already know how to ship text-only models and are now being asked to “make it multimodal”.
1. Designing outcomes for your multimodal model
Why multimodal isn’t just “more data” and how to optimize for tasks, not benchmarks.
2. From “one big model” to decomposed systems
Multimodal perception is harder than it looks. Decompose the task, don’t rely on one world model, and beware the “just add vision” reflex.
3. Data pipelines as engineered systems
Make the end-to-end flow explicit, from single scores to slice-aware evaluation
4. Technical challenges: latent spaces, evolution, and compute
Designing latent spaces for multimodal work, and designing for evolution, not a one-off launch.
5. Evaluation and benchmarking: beyond leaderboards
Use metrics as diagnostics, not just scores.
Designing multimodal systems
Adding vision or audio to a text model does not, by itself, give you a useful multimodal system. You need perception, alignment, and decision-making over messy, synchronized streams that don’t behave like tokens. Invisible’s latest paper sketches a practical approach to multimodal system design for researchers used to working with text-only models.
Adding vision or audio to a text model does not, by itself, give you a useful multimodal system. You need perception, alignment, and decision-making over messy, synchronized streams that don’t behave like tokens. Invisible’s latest paper sketches a practical approach to multimodal system design for researchers used to working with text-only models.
Adding vision or audio to a text model does not, by itself, give you a useful multimodal system. You need perception, alignment, and decision-making over messy, synchronized streams that don’t behave like tokens. Invisible’s latest paper sketches a practical approach to multimodal system design for researchers used to working with text-only models.
