Title: Lyapunov-Stable Adaptive Control for Multimodal Concept Drift
Venue: NeurIPS 2025
Accepted: yes
Keywords: multimodal learning; concept drift; adaptive control; online gradient descent; modality-specific fusion
Abstract: Multimodal learning systems often struggle in non-stationary environments due to concept drift, where changing data distributions can degrade performance. Modality-specific drifts and the lack of mechanisms for continuous, stable adaptation compound this challenge. This paper introduces LS-OGD, a novel adaptive control framework for robust multimodal learning in the presence of concept drift. LS-OGD uses an online controller that dynamically adjusts the model's learning rate and the fusion weights between different data modalities in response to detected drift and evolving prediction errors. We prove that under bounded drift conditions, the LS-OGD system's prediction error is uniformly ultimately bounded and converges to zero if the drift ceases. Additionally, we demonstrate that the adaptive fusion strategy effectively isolates and mitigates the impact of severe modality-specific drift, thereby ensuring system resilience and fault tolerance. These theoretical guarantees establish a principled foundation for developing reliable and continuously adapting multimodal learning systems.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper tackles the challenge of supervised online learning in multimodal streams by detecting modality‐specific drift via prediction errors and then dynamically adapting both the learning rate and fusion weight of each modality. By defining a Lyapunov function on the squared prediction error, the authors derive adaptation laws that guarantee bounded error under bounded drift and convergence to zero in stationary phase.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThis paper explores supervised online learning for multi-modal data, addressing a significant gap in current research. Furthermore, it provides a thorough theoretical analysis tailored to this setting, offering valuable insights into the learning dynamics.\n\nWeaknesses:\nDrift detection via prediction loss monitoring is a well-established technique in single-modal online supervised learning. While extending this idea to multimodal data is a promising direction, the authors' implementation lacks depth. Both loss-based drift detection and adaptive learning-rate adjustment are standard practices in the literature (Supplementary A.2), and this work merely repurposes them in a multimodal context using a simplistic fusion strategy. Specifically, the proposed method fuses modalities through a mere convex combination of their embeddings - an approach that falls short when compared to modern multimodal architectures (e.g., ViLBERT, LXMERT), which model intricate cross-modal interactions. Consequently, the paper's core contribution amounts to a minimal adaptation of established single-modal techniques to a basic multimodal setting, without introducing meaningful methodological innovation.\n\nFurthermore, the authors derive modality-specific “imagined” predictions by simply applying a sigmoid to each branch’s embedding, but this does not guarantee well-calibrated probabilities. During joint multimodal training, neither the image nor the text encoder is ever optimized in isolation to produce true posterior estimates, since no per-modality classification loss is applied. As a result, the computed error contributions from each modality can be entirely arbitrary, thereby undermining the central motivation of the method. Moreover, the manuscript provides no ablation or calibration study to justify this choice.\n\nMoreover, the method’s effectiveness hinges on accurately detecting drift and the drift detection is only focused on prediction loss. In real-world deployments where labels are often noisy or even erroneous, fluctuations in loss may reflect label noise rather than genuine distribution shifts. This can cause false alarms or missed detections, undermining the entire adaptation process.\n\nThe approach depends on various hyperparameters, however, the authors provide no ablation experiments to evaluate how sensitive the results are to those settings, leaving the method’s robustness unverified.\n\nQUESTIONS:\nThe concerns and questions raised in the weakness section should be addressed to improve the overall evaluation of the paper.\n\nAdditionally, I have a minor question. Recently, a more practical learning paradigm known as test-time adaptation (TTA) has gained attention. This setting enables models to adapt to distribution shifts in streaming data without requiring ground-truth labels, making it well-suited for real-world deployment. TTA has also been extended to multimodal scenarios [a], and prior works have explored effective mechanisms for detecting distribution shifts within this framework [b]. In contrast, the online supervised learning setup discussed in this paper assumes access to labels for the streaming data, which is often unrealistic in practice. Given the growing relevance and practicality of unsupervised test-time adaptation, how does the supervised online learning framework presented in this paper offer a more practical solution?\n\n[a] Shin, Inkyu, et al. \"MM-TTA: Multi-modal Test-Time Adaptation for 3D Semantic Segmentation.\" CVPR, 2022.\n[b] Niloy, Fahim Faisal, et al. \"Effective Restoration of Source Knowledge in Continual Test-Time Adaptation.\" WACV, 2024.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

comments: SUMMARY:
The authors propose an adaptive control framework for robust multimodal learning under concept drift. The framework dynamically adjusts the model’s learning rate and the fusion weights between different data modalities in response to detected drift and evolving prediction errors. Experimental results validate the effectiveness of the proposed algorithm.

STRENGTHS AND WEAKNESSES:
Strengths:
1.This is the first study to integrate concept drift theory into a multimodal learning setting.
2.A Lyapunov function is developed for the adaptive learning system.
Weakness:
1.Lack of quantitative experimental results.

QUESTIONS:
1. Will the algorithm be affected by different types of multimodal data? The authors should, at a minimum, explicitly discuss the generalization performance of the algorithm in the discussion section.  

2. The experiments lack comparisons with other methods. Even if no existing work specifically addresses multimodal concept drift, it is still necessary to compare the proposed algorithm with other approaches to validate its effectiveness.  

3. Although the authors demonstrate the effectiveness of the results through curves, the work lacks quantitative statistical analysis.  

4. While the authors mention the lightweight nature of the algorithm, its computational advantages should be detailed and quantified.

LIMITATIONS:
The authors should emphasize the limitations of the algorithm in application scenarios and parameter design.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a general framework for analyzing distribution drift in multimodal networks from a control systems perspective, as well as presenting a corresponding novel controller that aims at regulating the learning rate and contributions between modalities in response to detected distribution shift (or lack thereof). Extensive theoretic results are presented for this system that establish its being Lyapunov stable and that it can adequately adapt to scenarios where individual modalities potentially drift significantly independent of others doing the same. The proposal is empirically validated with a single experiment using a multimodal fake-news detection dataset with artificially induced distribution shifts.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n1. The framework proposed allows for capturing individual shifting of distributions across modalities in an actionable and interpretable way through the learned modality contribution parameter $\\alpha$. \n2. Extensive theoretical results were shown establishing stability and various operating conditions of the proposed controller. I did find the proofs presented in the main paper to be fairly clear and for the most part easy to follow.\n3. The problem area of distribution shift in multimodal networks that this paper tackles is very relevant and significant in the broader landscape.\n\n**Weaknesses:**\n1. The method appears to have quite a few hyperparameters at play. While the theoretical findings are able to assume that these are set correctly, obviously in practice these will need to be tuned. As far as I can tell, the paper is lacking in sensible recommendations for these values, and more importantly is lacking any ablation studies on the effects that these hyperparameters can have on the results.\n2. At times, I found the explanations a bit verbose and lacking clarity. Most of the time I believe this was due to attempting to keep some specifics of the approach more general to increase the applicability of the theoretical results; however, I found this to come at the detriment of concrete understanding of the actual proposed algorithm used in practice. For instance, on line 164 the paper states \"A simple rule could be $\\Delta \\eta_t = k_\\eta(e_t - e_{t-1})$ when $e_t > e_{t-1}$, for some gain $k_\\eta > 0$.\" I could have missed a more concrete definition of the update rule for $\\eta$, but I do feel that the ambiguous language hurts more than helps.\n\nQUESTIONS:\n1. In line 140, it states \"The rest of $\\theta$ encompasses the weights of $f^{(1)}, f^{(2)},$ and possibly any additional layers (e.g., if the features are concatenated and passed to an MLP, those MLP weights are in $\\theta$).\" Could you expand a bit more on the latter part of this statement? My understanding is that the system is a late-fusion approach, explicitly keeping the modalities separate up until the very end where they combine their decisions via Equation 1. These two ideas do not seem compatible with one another unless I am missing something here.\n\n2. The model proposed is defined as a late-fusion multimodal network over two modalities. To this end, I am curious to the authors' opinions on the following:\n    1. Is it possible to generalize this setup for more than two modalities? \n    2. Are there any direct lessons or inspiration that can be taken from the proposed work to be applied towards an early-fusion approach?\n\n3. The empirical results shown demonstrate that after training, the model quickly learns to rely on one modality over the other due to the shifts detected. Is it possible to perform a similar experiment where halfway through Phase 2 the quality of the two modalities shift again -- either to being equal in quality or reversing their standing? I would be curious to know if the method is able to return back to equilibrium / quickly adapt again later in training as it did earlier on.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper study the problem of concept drift in machine learning. Their main contribution appears to be the adjustment of the learning rate and fusion coefficient adaptively based on prediction error. In addition, they provide theoretical results analyzing how the system's error can be bounded. To empirically evaluate their method, they conducted very limited experiments using M3A on a classification problem.\n\nSTRENGTHS AND WEAKNESSES:\nWhile concept drift is an important and critical problem in machine learning, there are several issues with the proposed method in this paper:\n\n1) The paper assumes access to ground truth labels at test time in order to calculate prediction error (eq. 2), which is then used to adjust the learning rate and fusion coefficient. This is a highly unrealistic assumption. In real-world deployments of machine learning methods, acquiring labels, especially at scale, is often expensive, if not entirely infeasible.\n\n2) Even if we assume that access to test labels is realistic, one might question the motivation behind this specific method. Why not simply retrain continuously on all available data, or apply higher weighting to recent samples? like methods which are discussed in [2,3,4]. This paper might argue that it addresses concept drift by handling different modalities through the use of fusion coefficients. However, there is nothing in prior work that prevents those approaches from being applied to problems involving multiple input types. In fact, this could be viewed as a limitation of the current work, as it likely only functions effectively in settings with more than one modality. Ultimately, what matters is how well the proposed model performs in such scenarios not type of modality and whether any privileged information (such as test labels) is used to adapt or tune the fusion coefficients, an aspect that appears to be present in this paper but not in prior works.\n\n3) Recent papers have discussed approaches to address similar distribution shift problems without requiring access to (every and all) test-time labels [1,2,3,4]. Even methods that assume access to a small amount of labeled test data make significantly weaker assumptions than those made in this paper. Using full test-time labels to compute error and guide adaptation is a particularly strong and impractical assumption.\n\n4) The experimental results is limited and lacks rigor. The authors only evaluate on a single dataset (M3A) and provide no comparisons to existing methods. While the paper claims to contribute theoretical results as well, this does not justify the absence of a thorough empirical evaluation, especially when the paper also asserts that their method constitutes “a significant advancement in adaptive AI.”\n\nOverall, I remain unconvinced that the proposed approach offers a significant improvement in addressing concept drift, for the reasons outlined above. Most critically, the assumption of access to test-time labels for calculating prediction error is highly unrealistic. Moreover, alternative methods exist that do not rely on such strong assumptions.\n\n[1] Bayesian online changepoint detection. Ryan Prescott Adams and David JC MacKay. arxiv:0710.3742, 2007.\n\n[2] Continuous meta-learning. James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone. NeurIPS 2020\n  \n[3] Online adaptation to label distribution. Ruihan Wu, Chuan Guo, Yi Su, Kilian Q. Weinberger. NeurIPS 2021. \n\n[4] Time-Varying Propensity Score to Bridge the Gap between the Past and Present, Rasool Fakoor, Jonas Mueller, Zachary Chase Lipton, Pratik Chaudhari, Alex Smola. ICLR 2024\n\nQUESTIONS:\nN/A.\n\nLIMITATIONS:\nI'd suggest considering and revising this method so that it (especially the error prediction component) doesn't depend on the strong assumption of access to test data labels, e.g.,  they can relax this assumption by assuming access to only limited labels data during test time to calculate the error. In addition, in order to show that a method leads to significant results for concept drift, it requires comprehensive evaluation and comparison with other methods. Finally, works like [1,2,3,4] should be discussed and compared with in this paper. In summary, in its current format, this paper unfortunately is not anywhere near the NeurIPS acceptance bar.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 1, "ORIGINALITY": 1, "DATE": "2025-07-11"}