Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs

• URL: http://arxiv.org/abs/2604.05522v1
• Date: Tue, 07 Apr 2026 07:19:42 GMT
• Title: Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs
• Abstract summary: Cross-modal coreference is a problem where a model must localize a referent in a source modality and re-identify it in a target modality.<n>Experiments on 13 Omni-LLMs reveal systematic weaknesses in cross-modal coreference, which we attribute to the absence of coreference-aware thinking patterns.<n>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.
• Score: 31.530786531708035
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities [34.35941675436447]
  RePercENT is a self-supervised framework designed to unlock scalable pairwise disentanglement beyond two modalities.<n>Our approach operates directly on pre-extracted embeddings, eliminating the need for extensive joint pre-training.<n>Across diverse modalities and tasks, RePercENT successfully recovers disentangled components while maintaining competitive performance.
  arXiv  Detail & Related papers  (2026-06-03T17:10:39Z)
• Embed-RL: Reinforcement Learning for Reasoning-Driven Multimodal Embeddings [44.77164359074224]
  Multimodal Large Language Models (MLLMs) have become pivotal for advancing Universal Multimodal Embeddings (UME)<n>Recent studies demonstrate that incorporating generative Chain-of-Thought (CoT) reasoning can substantially enhance task-specific representations.<n>We propose a reasoning-driven UME framework that integrates Embedder-Guided Reinforcement Learning (EG-RL) to optimize the Reasoner to produce evidential Traceability CoT.
  arXiv  Detail & Related papers  (2026-02-14T15:35:03Z)
• Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition [51.68340973140949]
  Multimodal Named Entity Recognition (GMNER) aims to extract text-based entities, assign them semantic categories, and ground them to corresponding visual regions.<n> MLLMs exhibit $textbfmodality bias$, including visual bias and textual bias, which stems from their tendency to take unimodal shortcuts.<n>We propose Modality-aware Consistency Reasoning ($bfMCR$), which enforces structured cross-modal reasoning.
  arXiv  Detail & Related papers  (2026-02-04T12:12:49Z)
• Coherent Multimodal Reasoning with Iterative Self-Evaluation for Vision-Language Models [4.064135211977999]
  Large language models (LLMs) and vision-language models (LVLMs) struggle with complex, multi-step, cross-modal common sense reasoning tasks.<n>We propose the Coherent Multimodal Reasoning Framework (CMRF), a novel approach that enhances LVLMs' common sense reasoning capabilities.<n>CMRF mimics human problem-solving by decomposing complex queries, generating step-by-step inferences, and self-correcting errors.
  arXiv  Detail & Related papers  (2025-08-04T20:33:58Z)
• HumanOmniV2: From Understanding to Omni-Modal Reasoning with Context [26.506057678587176]
  Insufficient context understanding can happen when a model misinterprets multimodal context, resulting in incorrect answers.<n>The shortcut problem occurs when the model overlooks crucial clues in multimodal inputs, directly addressing the query without considering the multimodal information.<n>We introduce a reasoning omni-modal benchmark, IntentBench, aimed at evaluating models in understanding complex human intentions and emotions.
  arXiv  Detail & Related papers  (2025-06-26T14:01:03Z)
• Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1) [66.51642638034822]
  Reasoning is central to human intelligence, enabling structured problem-solving across diverse tasks.<n>Recent advances in large language models (LLMs) have greatly enhanced their reasoning abilities in arithmetic, commonsense, and symbolic domains.<n>This paper offers a concise yet insightful overview of reasoning techniques in both textual and multimodal LLMs.
  arXiv  Detail & Related papers  (2025-04-04T04:04:56Z)
• Leveraging Weak Cross-Modal Guidance for Coherence Modelling via Iterative Learning [66.28872204574648]
  Cross-modal coherence modeling is essential for intelligent systems to help them organize and structure information. Previous work on cross-modal coherence modeling attempted to leverage the order information from another modality to assist the coherence recovering of the target modality. This paper explores a new way to take advantage of cross-modal guidance without gold labels on coherency.
  arXiv  Detail & Related papers  (2024-08-01T06:04:44Z)
• Cantor: Inspiring Multimodal Chain-of-Thought of MLLM [83.6663322930814]
  We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. We propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Our experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance.
  arXiv  Detail & Related papers  (2024-04-24T17:59:48Z)
• Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication [76.04373033082948]
  Large Language Models (LLMs) have recently made significant strides in complex reasoning tasks through the Chain-of-Thought technique. We propose Exchange-of-Thought (EoT), a novel framework that enables cross-model communication during problem-solving.
  arXiv  Detail & Related papers  (2023-12-04T11:53:56Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.