We analyzed 125 multimodal AI models to understand how they really work - here's what we found
Hi HackerNews! I'm Jisu An, and my team just published a comprehensive survey that tackles a critical gap in our understanding of multimodal AI.
WHY THIS MATTERS RIGHT NOW
The field is exploding with models like GPT-4V, Gemini, and Claude 3 - but there's been no systematic framework for understanding how they actually integrate different modalities (vision, audio, speech) with language models. This creates real problems for researchers and engineers trying to build or improve these systems.
WHAT WE DID
We analyzed 125 multimodal LLMs from 2021-2025 and discovered that the field has been developing somewhat chaotically. So we created the first comprehensive taxonomy based on three key dimensions:
1. LLM-based Fusion Levels
- Early fusion: Modalities combined before the LLM
- Intermediate fusion: Integration happens within LLM layers
- Hybrid fusion: Combining multiple approaches
2. Contextual Fusion Mechanisms
- Projection: Direct mapping to language space
- Abstraction: High-level feature extraction
- Semantic Embedding: Meaning-preserving transformations
- Cross-Attention: Dynamic interaction between modalities
3. Representation Learning Approaches
- Joint: Shared embedding spaces
- Coordinate: Separate but aligned spaces
- Hybrid: Best of both worlds
KEY INSIGHTS THAT SURPRISED US
Most models use ad-hoc integration strategies - there's been little principled design. Training paradigms vary wildly with no consensus on best practices. The field desperately needs standardization - current approaches are difficult to compare or reproduce.
WHY YOU SHOULD CARE
If you're working with multimodal AI, this framework provides clear guidelines for architectural decisions, systematic comparison of different approaches, evidence-based recommendations for integration strategies, and a roadmap for future development.
THE BIGGER PICTURE
Multimodal AI is becoming the backbone of everything from autonomous vehicles to medical diagnosis. But without understanding how these models actually work under the hood, we're building on shaky foundations. This survey aims to change that.
What do you think? Are there specific aspects of multimodal integration you'd like us to explore further? And for those building multimodal systems - what challenges are you facing that this framework might help address?
This is my first post here, so please let me know if there are better ways to share research with this community!
We analyzed 125 multimodal AI models to understand how they really work - here's what we found
Hi HackerNews! I'm Jisu An, and my team just published a comprehensive survey that tackles a critical gap in our understanding of multimodal AI.
WHY THIS MATTERS RIGHT NOW
The field is exploding with models like GPT-4V, Gemini, and Claude 3 - but there's been no systematic framework for understanding how they actually integrate different modalities (vision, audio, speech) with language models. This creates real problems for researchers and engineers trying to build or improve these systems.
WHAT WE DID
We analyzed 125 multimodal LLMs from 2021-2025 and discovered that the field has been developing somewhat chaotically. So we created the first comprehensive taxonomy based on three key dimensions:
1. LLM-based Fusion Levels - Early fusion: Modalities combined before the LLM - Intermediate fusion: Integration happens within LLM layers - Hybrid fusion: Combining multiple approaches
2. Contextual Fusion Mechanisms - Projection: Direct mapping to language space - Abstraction: High-level feature extraction - Semantic Embedding: Meaning-preserving transformations - Cross-Attention: Dynamic interaction between modalities
3. Representation Learning Approaches - Joint: Shared embedding spaces - Coordinate: Separate but aligned spaces - Hybrid: Best of both worlds
KEY INSIGHTS THAT SURPRISED US
Most models use ad-hoc integration strategies - there's been little principled design. Training paradigms vary wildly with no consensus on best practices. The field desperately needs standardization - current approaches are difficult to compare or reproduce.
WHY YOU SHOULD CARE
If you're working with multimodal AI, this framework provides clear guidelines for architectural decisions, systematic comparison of different approaches, evidence-based recommendations for integration strategies, and a roadmap for future development.
THE BIGGER PICTURE
Multimodal AI is becoming the backbone of everything from autonomous vehicles to medical diagnosis. But without understanding how these models actually work under the hood, we're building on shaky foundations. This survey aims to change that.
Paper: https://www.alphaxiv.org/overview/2506.04788 arXiv: https://arxiv.org/abs/2506.04788
What do you think? Are there specific aspects of multimodal integration you'd like us to explore further? And for those building multimodal systems - what challenges are you facing that this framework might help address?
This is my first post here, so please let me know if there are better ways to share research with this community!