Title: CyIN: Cyclic Informative Latent Space for Bridging Complete and Incomplete Multimodal Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multimodal representation learning

Incomplete multimodal learning

Missing modality issue

Information bottleneck

Cross-modal transaltion
Abstract: Multimodal machine learning, mimicking the human brain’s ability to integrate various modalities has seen rapid growth. Most previous multimodal models are trained on perfectly paired multimodal input to reach optimal performance. In real‑world deployments, however, the presence of modality is highly variable and unpredictable, causing the pre-trained models in suffering significant performance drops and fail to remain robust with dynamic missing modalities circumstances. In this paper, we present a novel Cyclic INformative Learning framework (CyIN) to bridge the gap between complete and incomplete multimodal learning. Specifically, we firstly build an informative latent space by adopting token- and label-level Information Bottleneck (IB) cyclically among various modalities. Capturing task-related features with variational approximation, the informative bottleneck latents are purified for more efficient cross-modal interaction and multimodal fusion. Moreover, to supplement the missing information caused by incomplete multimodal input, we propose cross-modal cyclic translation by reconstruct the missing modalities with the remained ones through forward and reverse propagation process. With the help of the extracted and reconstructed informative latents, CyIN succeeds in jointly optimizing complete and incomplete multimodal learning in one unified model. Extensive experiments on 4 multimodal datasets demonstrate the superior performance of our method in both complete and diverse incomplete scenarios.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes CyIN, a unified framework to jointly handle both complete and incomplete multimodal learning via a cyclic informative latent space. The core contributions include (1) a dual Information Bottleneck (IB) module at token- and label-levels to extract semantically rich yet compact features, and (2) a cyclic cross-modal translation module that enables forward and reverse reconstruction of missing modalities using cascaded residual autoencoders. The method is evaluated on four datasets under complete, fixed missing, and random missing settings, demonstrating state-of-the-art performance. However, the paper does not clearly distill the core problem it aims to address, and further refinement is needed to improve its clarity and focus.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength:**\n* CyIN elegantly bridges the gap between complete and incomplete modality inputs with one model, avoiding the need to retrain for each missing-modality configuration.\n* The use of token-level and label-level Information Bottlenecks effectively filters noise and preserves task-relevant information, leading to better fusion and reconstruction.\n* CyIN achieves SOTA or near-SOTA performance on MOSI, MOSEI, IEMOCAP, and MELD, consistently outperforming baselines in both complete and incomplete setups.\n\n**Weaknesses:**\n* The authors state in the abstract that the proposed method aims to address the challenge of missing modalities. However, in the fourth paragraph, they enumerate several limitations of existing methods—such as insufficient exploitation of missing information, lack of unified modeling, and poor generalization and robustness. Despite listing these issues, the proposed method does not explicitly address **all of them**, which may lead to confusion regarding the scope and objectives of the paper.\n* While Information Bottleneck is a sound principle, the theoretical grounding of combining IB with cyclic autoencoding for modality reconstruction is not rigorously established.\n* Parameter count, FLOPs, and training time are not reported. Furthermore, all experiments focus on affective/emotion datasets; effectiveness on large-scale or non-affective tasks remains unknown.\n* Typo: builds -> build (in line 9)\n\nQUESTIONS:\n* The motivation of the paper.\n* What theoretical guarantees, if any, does the proposed method offer?\n* Training overhead analysis.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: SUMMARY:
This paper addresses the gap between complete and incomplete multimodal learning by proposing the CyIN (Cyclic INformative Learning) framework. The core idea is to construct an “information-purified latent space” via a two-stage Information Bottleneck (IB) mechanism—Token-level IB and Label-level IB—and then perform cross-modal cyclic translation to reconstruct missing modalities within this space. The authors evaluate their approach on four datasets under three settings.

STRENGTHS AND WEAKNESSES:
Strengths
1. Unified handling of complete and arbitrarily missing inputs with a single model, avoiding the common practice of training separate models for different missing patterns.

2. Clear writing, with Figure 1 providing an intuitive breakdown of the framework and loss components.

3. The experiments are relatively comprehensive, and the results demonstrate their effectiveness.

Weaknesses
1. Both the Information Bottleneck and cyclic reconstruction approaches (CRA) are established methods; the main contribution lies in their combination. The authors need to explain the connection between the two techniques and why their combination constitutes an optimal solution.

2. The paper does not quantify the cost introduced by the two-stage training, multiple translators, and multi-head cross-attention—specifically in terms of parameter count or inference latency.

QUESTIONS:
Refer to the above weaknesses.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a novel framework, CyIN, designed to address the challenge of multimodal learning in scenarios with both complete and incomplete multimodal inputs.  The authors introduce a cyclic informative latent space that enables efficient cross-modal interaction and multimodal fusion.  This space is constructed using token-level and label-level Information Bottlenecks (IB) to capture task-relevant features and filter out noise.  Additionally, the paper proposes a cross-modal cyclic translation mechanism to reconstruct missing modalities through forward and reverse propagation, enhancing robustness in incomplete input scenarios.  Extensive experiments on four datasets demonstrate that CyIN achieves state-of-the-art performance in both complete and diverse incomplete multimodal learning settings, highlighting its effectiveness and generalization ability.  The contributions include the development of the informative latent space, the cyclic interaction and translation mechanisms, and the unified framework for joint optimization of complete and incomplete multimodal learning.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. The paper proposes a novel Cyclic Informative Learning framework (CyIN) that bridges the gap between complete and incomplete multimodal learning by constructing an informative latent space using token-level and label-level Information Bottlenecks.\n\n2. The introduced cross-modal cyclic translation mechanism enhances the model's robustness and performance in scenarios with missing modalities by reconstructing the missing information through forward and reverse propagation.\n\nWeaknesses\n\n1. The paper assumes that all modalities contribute equally to the final task, but in practice, different modalities may have different importance to the task. For example, in some sentiment analysis tasks, text modalities may be more important than audio and visual modalities. This unbalanced contribution between modalities may affect the performance and generalization ability of the model\n\n2. Although the paper proposes recurrent interaction and translation mechanisms based on information bottlenecks, the computational efficiency and scalability of these mechanisms can be a potential issue when dealing with large-scale multimodal data. For example, when the number of modalities increases or the data dimension is high\n\n3. The experiments mainly focus on sentiment analysis and emotion recognition, and lack corresponding experimental verification for some broader multimodal tasks, such as multimodal question answering and multimodal recommendation. In addition, although the paper provides detailed hyperparameter Settings and training details, no in-depth analysis of the sensitivity of different hyperparameters is performed in the experiments. For example, the impact of the β parameter in the information bottleneck on the model performance and how to adjust β for the best performance under different missing rates can be further explored.\n\nQUESTIONS:\nsee above\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
The authors develop  a novel Cyclic INformative Learning framework (CyIN) to bridge the gap between complete and incomplete multimodal learning, which builds an informative latent space by adopting token- and label-level Information Bottleneck (IB) cyclically among various modalities.

STRENGTHS AND WEAKNESSES:
1. the paper is well written
2. the technique details are sound
3. the experimental results are convincing
4. the novelty is not enough

QUESTIONS:
Multimodal latent representation learning was once a popular research topic in AI, with classical methods such as CCA widely explored. However, recent progress in this area appears limited. The main change lies primarily in the backbone architecture—shifting from traditional deep neural networks to today’s large language models (LLMs).

I did not see significant contributions from this work to the multimodal learning community. Its primary contribution appears to be the straightforward application of existing multimodal learning techniques—specifically those involving the information bottleneck—on modern LLMs.

The following is a more subjective comment from my personal perspective: VAE and the information bottleneck framework often feel like "one-size-fits-all" solutions that can be applied to virtually any representation learning problem. While they are well-suited for academic exploration and publishing, the complex training procedures involved—like those described in this paper—are unlikely to scale effectively for solving real-world AI tasks. For this reason, I personally do not prefer this type of work.

LIMITATIONS:
N/A
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03