Unclear human-like capabilities and real-world applicability of multimodal foundation models

Determine whether multimodal foundation models possess fundamental human-like capabilities such as associative reasoning (for example, imagining a person upon hearing a voice) and whether these models can be effectively applied to real-world tasks under resource constraints.

Background

The paper investigates the training-free use of multimodal foundation models for predicting missing modalities. Despite strong benchmark performance driven by large-scale data and computation, the authors explicitly note uncertainty regarding whether these models exhibit human-like cognitive abilities such as associative reasoning and whether they can be practically deployed under resource constraints.

This open question motivates the paper's systematic evaluation across paradigms and the development of an agentic framework to improve semantic extraction and verification, highlighting the gap between benchmark success and reliable, real-world capability.

References

Despite these massive investments and their strong performance on standardized benchmarks, it remains unclear whether such models possess fundamental human-like capabilities such as associative reasoning (e.g., imagining a person upon hearing a voice), or can be effectively applied to real-world tasks under resource constraints.

— How Far Are We from Generating Missing Modalities with Foundation Models?  (2506.03530 - Ke et al., 4 Jun 2025) in Section 1 (Introduction)

Therefore, the safety-oriented assessment of mainstream LMMs in real-world logistics scenarios remains an open question.

— LogiScope-VQA: Benchmarking Vision-Language Models for Logistics Hazard Identification in Industrial Scenarios  (2609.09790 - Zhou et al., 9 Sep 2026) in Introduction, Section 1

Finally, SpatialHarness currently relies on powerful multimodal foundation models for spatial reasoning and action generation, and its effectiveness with smaller models remains unverified.

— SpatialHarness: Test-Time Spatial Scaffolding for Fine Robotic Manipulation  (2610.12457 - Wang et al., 8 Oct 2026) in Section Limitations