Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs
arXiv:2604.05522v1 Announce Type: new Abstract: Omni Large Language Models (Omni-LLMs) have demonstrated impressive capabilities in holistic multi-modal perception, yet they consistently falter in complex scenarios requiring synergistic omni-modal reasoning. Beyond understanding global multimodal context, effective reasoning also hinges on fine-grained cross-modal alignment, especially identifying shared referents across modalities, yet this aspect has been largely overlooked. To bridge this gap, we formalize the challenge as a cross-modal coreference problem, where a model must localize a referent in a source modality and re-identify it in a target modality. Building on this paradigm, we introduce CrossOmni, a dataset comprising nine tasks equipped with human-designed reasoning rationales to evaluate and enhance this capability. Experiments on 13 Omni-LLMs reveal systematic weaknesses in cross-modal coreference, which we attribute to the absence of coreference-aware thinking patterns
arXiv:2604.05522v1 Announce Type: new Abstract: Omni Large Language Models (Omni-LLMs) have demonstrated impressive capabilities in holistic multi-modal perception, yet they consistently falter in complex scenarios requiring synergistic omni-modal reasoning. Beyond understanding global multimodal context, effective reasoning also hinges on fine-grained cross-modal alignment, especially identifying shared referents across modalities, yet this aspect has been largely overlooked. To bridge this gap, we formalize the challenge as a cross-modal coreference problem, where a model must localize a referent in a source modality and re-identify it in a target modality. Building on this paradigm, we introduce CrossOmni, a dataset comprising nine tasks equipped with human-designed reasoning rationales to evaluate and enhance this capability. Experiments on 13 Omni-LLMs reveal systematic weaknesses in cross-modal coreference, which we attribute to the absence of coreference-aware thinking patterns. To address this, we enhance cross-modal alignment via two strategies: a training-free In-Context Learning method and a training-based SFT+GRPO framework designed to induce such thinking patterns. Both approaches yield substantial performance gains and generalize effectively to collaborative reasoning tasks. Overall, our findings highlight cross-modal coreference as a crucial missing piece for advancing robust omni-modal reasoning.
Executive Summary
The article addresses a critical gap in Omni-LLMs' capabilities by formalizing cross-modal coreference alignment as a foundational challenge for robust multi-modal reasoning. The authors introduce CrossOmni, a nine-task benchmark dataset with human-designed rationales, to evaluate and improve models' ability to localize and re-identify referents across modalities. Their experiments on 13 Omni-LLMs reveal systemic weaknesses in cross-modal coreference, attributing these to the absence of coreference-aware thinking patterns. To mitigate this, they propose two complementary approaches: a training-free In-Context Learning method and a training-based SFT+GRPO framework, both of which significantly enhance performance and generalize to collaborative reasoning tasks. The study underscores cross-modal coreference as an essential component for advancing holistic multi-modal reasoning in AI systems.
Key Points
- ▸ Cross-modal coreference alignment is identified as a critical but overlooked challenge in Omni-LLMs, essential for synergistic multi-modal reasoning.
- ▸ CrossOmni, a nine-task benchmark with human-designed rationales, is introduced to evaluate and improve cross-modal coreference capabilities in models.
- ▸ Systematic weaknesses in Omni-LLMs' cross-modal coreference are demonstrated, linked to the absence of coreference-aware thinking patterns, which are addressed through training-free and training-based strategies yielding substantial performance improvements.
Merits
Novelty and Theoretical Rigor
The article formalizes cross-modal coreference as a distinct challenge in Omni-LLMs, introducing a rigorous framework that bridges a significant gap in multi-modal reasoning research. The formalization of cross-modal coreference and the introduction of the CrossOmni dataset represent a substantial contribution to both theory and practice in AI.
Comprehensive Empirical Validation
The study evaluates 13 Omni-LLMs on the CrossOmni benchmark, providing robust empirical evidence of systemic weaknesses in cross-modal coreference. The inclusion of human-designed rationales in the dataset enhances the interpretability and educational value of the research.
Practical Solutions with Generalizability
The proposed solutions—In-Context Learning and SFT+GRPO—are both training-free and training-based, respectively, offering flexibility in deployment. Their demonstrated generalizability to collaborative reasoning tasks underscores their practical utility and scalability.
Interdisciplinary Relevance
The research intersects with linguistics, cognitive science, and computer science, offering insights that could inform advancements in natural language processing, computer vision, and AI ethics, particularly in the context of multi-modal decision-making systems.
Demerits
Limited Task Diversity in CrossOmni
While the CrossOmni dataset comprises nine tasks, the diversity of modalities and reasoning scenarios may still be constrained. Expanding the dataset to include more modalities (e.g., tactile, olfactory) and edge cases could further validate the robustness of the proposed methods.
Dependence on Human Rationales
The reliance on human-designed rationales for evaluating cross-modal coreference introduces a potential bias, as these rationales may not fully capture the complexity or variability of real-world multi-modal reasoning scenarios. Automated methods for generating or validating rationales could mitigate this limitation.
Scalability of Training-Based Approaches
The SFT+GRPO framework, while effective, may face scalability challenges when applied to larger or more complex Omni-LLMs. The computational and resource-intensive nature of training-based methods could limit their accessibility for smaller research teams or organizations.
Generalization to Real-World Scenarios
The study's findings are based on controlled experiments and benchmarks. The extent to which the proposed methods generalize to real-world, noisy, or adversarial multi-modal environments remains an open question, warranting further field testing.
Expert Commentary
The article by the authors represents a seminal contribution to the field of multi-modal AI, addressing a long-overlooked challenge in Omni-LLMs: cross-modal coreference alignment. By formalizing this challenge and introducing the CrossOmni benchmark, the authors have not only highlighted a critical weakness in current models but also provided actionable solutions that bridge the gap between perception and reasoning in multi-modal systems. The dual approach of training-free and training-based methods is particularly commendable, as it offers flexibility and scalability for different deployment scenarios. However, the reliance on human-designed rationales, while valuable for interpretability, may introduce biases that warrant further scrutiny. Additionally, the study’s focus on controlled benchmarks raises questions about the real-world applicability of the findings, which should be addressed through broader field validation. Overall, this work sets a new standard for evaluating and enhancing the cognitive capabilities of Omni-LLMs, and its interdisciplinary implications could resonate across AI, cognitive science, and ethics.
Recommendations
- ✓ Expand the CrossOmni dataset to include a broader range of modalities and more diverse, real-world scenarios to validate the robustness and generalizability of the proposed methods.
- ✓ Develop automated tools for generating and validating rationales to reduce bias and improve the scalability of the CrossOmni benchmark for large-scale evaluations.
- ✓ Conduct further research on the integration of cross-modal coreference alignment in high-stakes domains, such as healthcare and autonomous systems, to assess its impact on safety, fairness, and ethical compliance.
- ✓ Collaborate with standard-setting bodies to establish guidelines for evaluating cross-modal coreference in AI systems, ensuring consistency and transparency in multi-modal AI development.
- ✓ Explore the potential of neurosymbolic AI frameworks to enhance cross-modal coreference alignment, leveraging symbolic reasoning to improve the interpretability and reliability of multi-modal decision-making.
Sources
Original: arXiv - cs.CL