Applying Large Language Models to Multimodal Content Analysis

Determine effective approaches for applying large language models (LLMs) to multimodal content analysis that integrates textual and visual inputs, establishing whether and how LLMs can be used to analyze multimodal content reliably.

Background

The paper studies zero-shot character identification and speaker prediction in comics by integrating textual and visual information through an iterative multimodal framework. While LLMs have demonstrated strong capabilities in text understanding and reasoning, their role in multimodal content analysis remains unresolved.

The authors note that existing large multimodal models can only handle a small number of images at a time, whereas comics analysis requires longer-range context across multiple pages and persistent character identity tracking. Their framework provides a first baseline, but the broader challenge of effectively applying LLMs to multimodal content analysis remains open.

References

Recent LLMs have shown great capability for text understanding and reasoning, while their application to multimodal content analysis is still an open problem.

— Zero-Shot Character Identification and Speaker Prediction in Comics via Iterative Multimodal Fusion  (2404.13993 - Li et al., 2024) in Abstract

Evidence availability is therefore necessary but not sufficient: even when the complete evidence set is handed to a model, integrating it into a correct scientific conclusion remains a substantial, unresolved challenge.

— Sci-MMR: Benchmarking Multi-Step Evidence-Grounded Scientific Reasoning in Multimodal Agents  (2609.11243 - Li et al., 10 Sep 2026) in Section 4.1, RQ1: Is Evidence Availability Sufficient for Reliable Scientific Reasoning?

Our evaluation does not address these modalities, and understanding how their inclusion affects VLM moderation performance remains an open question we leave for future work.

— Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization  (2609.10410 - Majumdar et al., 9 Sep 2026) in Section “Limitations”, first paragraph

Collecting and representing such context for VLM-based moderation remains an open challenge.

— Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization  (2609.10410 - Majumdar et al., 9 Sep 2026) in Section “Limitations”, third paragraph

How well LLMs cope with geospatial context is an open question: the Mil-SCORE benchmark shows current models struggle to combine maps, orders, and reports at scenario scale, which is why we hand the LLM a short, pre-digested terrain summary rather than raw map data.

— World-Model-Grounded LLM Planning for AUV and ASV Navigation Near Offshore Wind Farms  (2608.19661 - Buchholz et al., 20 Aug 2026) in Section II, subsection “Semantic mapping for sensor-cheap navigation (ASV)”