arXiv:2608.20379 Surveys Multimodal Integration Across Five Agent Modules
The survey organizes multimodal agent research by fusion architecture and module impact. It highlights efficiency trade-offs and missing evaluation standards. No production systems are evaluated.
The survey catalogs the shift from text-only LLM agents to systems that ingest images, audio, and video. It maps modality integration choices directly to capability outcomes in four domains: robotics, GUI navigation, multimedia editing, and long-form video retrieval. The taxonomy links architectural decisions to measurable differences in grounding and planning success.
Empirical sections compare latency, training cost, and inference throughput across fusion strategies. Early-fusion designs show higher sample efficiency on grounded tasks but scale poorly beyond 7B parameters under current memory budgets. Late-fusion remains dominant in deployed web agents due to lower coordination overhead. The review flags missing standardized benchmarks for multimodal memory retention.
The paper identifies gaps in long-horizon multimodal planning and cross-modal credit assignment. It points to required advances in unified tokenization and retrieval-augmented multimodal memory before generalist agents become practical. No deployment records or production metrics are supplied.
Future work will test whether early-fusion agents close the gap on robotics manipulation benchmarks within 18 months once training recipes stabilize.
Mokaria et al.: Early-fusion multimodal agents will exceed 65% success on ALFRED by Q4 2027.
Sources (2)
- [1]Primary Source(https://arxiv.org/abs/2608.20379)
- [2]Supporting Source(https://arxiv.org/abs/2210.03629)