Robust Omni-Modal Reasoning for Arbitrary Modality Combinations

Establish robust omni-modal reasoning methods that integrate arbitrary combinations of text, images, audio, and video, enabling reliable cross-modal integration beyond unimodal or pairwise settings.

Background

Current multimodal systems predominantly handle fixed modality pairs (e.g., text–image or text–video), and unified omni models trained jointly across modalities face performance trade-offs. The scarcity of datasets that require rich cross-modal integration further complicates progress toward general omni reasoning.

Agent-Omni proposes inference-time coordination of specialized foundation models to improve cross-modal reasoning, yet the broader field recognizes that achieving robust reasoning that seamlessly integrates arbitrary modality combinations remains unresolved.

References

Nevertheless, most existing work emphasizes unimodal or pairwise reasoning, and robust omni-modal reasoning, integrating arbitrary modality combinations, remains an open challenge.

— Agent-Omni: Test-Time Multimodal Reasoning via Model Coordination for Understanding Anything  (2511.02834 - Lin et al., 4 Nov 2025) in Section 4.1 (Multimodal Reasoning), Related Work

Nevertheless, challenges such as preserving fine-grained multimodal information, mitigating hallucination, and improving computational efficiency remain open, providing important directions for future research.

— Evolution of Multimodal Question Answering: From Modality-Adaptive Extraction to Unified Language Representation  (2609.08896 - Shafi, 8 Sep 2026) in Conclusion, Section Conclusion

Building architectures capable of performing temporally aware, multimodal commonsense reasoning—such as understanding a video scene and relating it to unseen textual commonsense—is a significant open challenge that demands new fusion techniques and pre-training paradigms.

— Commonsense Reasoning in Computer Vision: Foundations, Recent Advancements, and Future Directions  (2609.05257 - Mahmud et al., 4 Sep 2026) in Section 8.4, Unified Reasoning Across Modalities

Future work could investigate whether MoE-based alignment could facilitate cross-modal alignment and improve reasoning capabilities across modalities.

— Cross-Lingual Alignment for Decoder-Only Models using MoE Routers  (2610.01921 - Bandarkar et al., 1 Oct 2026) in Section 6, paragraph “Cross-Modal Representation Learning”

In conclusion, large-scale audio-visual and spatial reasoning can be solved by systems that orchestrate models with explicit tools, but remains an open problem for multimodal models used standalone.

— Perception Test 2026: Challenge Summary and Extension to City-scale Audio-Visual Reasoning  (2610.12081 - Kitashov et al., 8 Oct 2026) in Discussion