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Guiding Mixture-of-Experts with Temporal Multimodal Interactions

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Multimodal InteractionMixture-of-ExpertsTransformer
Abstract:

Mixture-of-Experts (MoE) architectures have become pivotal for large-scale multimodal models. However, their routing mechanisms typically overlook the informative, time-varying interaction dynamics between modalities. This limitation hinders expert specialization, as the model cannot explicitly leverage intrinsic modality relationships for effective reasoning. To address this, we propose a novel framework that guides MoE routing using quantified temporal interaction. A multimodal interaction-aware router learns to dispatch tokens to experts based on the nature of their interactions. This dynamic routing encourages experts to acquire generalizable interaction-processing skills rather than merely learning task-specific features. Our framework builds on a new formulation of temporal multimodal interaction dynamics, which are used to guide expert routing. We first demonstrate that these temporal multimodal interactions reveal meaningful patterns across applications, and then show how they can be leveraged to improve both the design and performance of MoE-based models. Comprehensive experiments on challenging multimodal benchmarks validate our approach, demonstrating both enhanced performance and improved interpretability.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a framework that guides mixture-of-experts routing using quantified temporal multimodal interaction dynamics, formulated through directed information decomposition. It resides in the 'Temporal Interaction-Guided Routing' leaf, which contains only three papers total, including this work. This leaf sits within the broader 'Multimodal Fusion and Routing Mechanisms' branch, indicating a relatively sparse research direction focused specifically on leveraging time-varying cross-modal relationships for expert selection, rather than static fusion or domain-specific applications.

The taxonomy reveals that neighboring leaves address related but distinct challenges: 'Adaptive Modality Handling with MoE' focuses on incomplete or asynchronous modalities through dynamic activation, while 'Graph-Augmented and Hierarchical Routing' integrates relational structures and multi-scale representations. The paper's emphasis on temporal interaction dynamics distinguishes it from these directions, which either handle modality availability issues or impose structural priors without explicitly modeling evolving cross-modal relationships. The broader 'Spatiotemporal Forecasting with MoE' branch applies similar architectures to prediction tasks, but excludes non-forecasting multimodal fusion scenarios like the one addressed here.

Among the three contributions analyzed, the temporal multimodal interaction framework examined zero candidates, while the multi-scale BATCH estimator and RUS-aware router examined six and ten candidates respectively, with none identified as clearly refutable. The literature search scope covered sixteen candidates total, drawn from top-K semantic search and citation expansion. This limited examination suggests that within the accessible prior work, no direct overlaps were detected for the specific combination of temporal interaction quantification and interaction-guided routing losses, though the small search scale means substantial related work may exist beyond these candidates.

Given the sparse taxonomy leaf and limited search scope, the work appears to occupy a relatively underexplored niche at the intersection of temporal multimodal interaction modeling and mixture-of-experts routing. However, the analysis is constrained by examining only sixteen candidates and does not constitute an exhaustive literature review. The absence of refutable pairs within this scope suggests potential novelty in the specific technical approach, but broader field coverage would be necessary to assess whether similar interaction-based routing strategies exist in adjacent research communities.

Taxonomy

Core-task Taxonomy Papers
28
3
Claimed Contributions
16
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Guiding mixture-of-experts routing with temporal multimodal interaction dynamics. The field explores how to effectively route information through specialized expert networks when dealing with multiple modalities that evolve over time. The taxonomy reveals four main branches: Multimodal Fusion and Routing Mechanisms focuses on designing routing strategies that leverage cross-modal interactions and temporal dependencies, with works like Fusemoe[1] and Dual Routing MoE[7] developing sophisticated gating mechanisms; Spatiotemporal Forecasting with MoE applies these architectures to prediction tasks in urban computing and time-series domains, exemplified by ST-MoE Forecasting[26] and STMMOE Urban[18]; Domain-Specific MoE Applications tailors mixture-of-experts to specialized settings such as emotion recognition, survival analysis, and reinforcement learning; and Omnimodal and Large-Scale Multimodal Models scales these ideas to handle diverse input types simultaneously, as seen in Uni-MoE-2.0-Omni[16]. These branches collectively address the challenge of dynamically selecting and combining expert knowledge based on both modality characteristics and temporal context. A particularly active line of work centers on temporal interaction-guided routing, where the key question is how to make routing decisions sensitive to evolving multimodal relationships rather than treating modalities as static inputs. Temporal Multimodal MoE[0] sits squarely within this cluster, emphasizing how temporal dynamics between modalities should inform which experts are activated at each time step. This contrasts with approaches like Hierarchical Time MoE[5], which structures experts hierarchically across temporal scales but may not explicitly model cross-modal interaction patterns, and Temporal MoE VideoQA[9], which applies temporal routing primarily to video question-answering tasks with a narrower scope. The trade-off across these works involves balancing routing complexity against computational efficiency: more sophisticated temporal and cross-modal routing can improve specialization but risks increased overhead and training instability, a challenge that remains central to advancing mixture-of-experts architectures in multimodal temporal settings.

Claimed Contributions

Temporal multimodal interaction framework using directed information decomposition

The authors introduce a formulation of temporal multimodal interactions based on directed information that decomposes multi-source information flow into redundancy, uniqueness, and synergy (RUS) components across multiple time lags. This framework captures time-varying interaction dynamics between modalities with respect to target outcomes.

0 retrieved papers
Multi-scale BATCH estimator for efficient temporal RUS computation

The authors develop an efficient computational method that extends the BATCH estimator to handle high-dimensional temporal data by training a single model to predict temporal RUS values at multiple time lags simultaneously, achieving significant speedup while maintaining accuracy.

6 retrieved papers
RUS-aware MoE router with interaction-guided auxiliary losses

The authors design an interaction-aware routing mechanism that incorporates temporal RUS sequences through attention and recurrent modules, combined with auxiliary loss functions that enforce routing strategies aligned with redundancy, uniqueness, and synergy principles to improve expert specialization.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Temporal multimodal interaction framework using directed information decomposition

The authors introduce a formulation of temporal multimodal interactions based on directed information that decomposes multi-source information flow into redundancy, uniqueness, and synergy (RUS) components across multiple time lags. This framework captures time-varying interaction dynamics between modalities with respect to target outcomes.

Contribution

Multi-scale BATCH estimator for efficient temporal RUS computation

The authors develop an efficient computational method that extends the BATCH estimator to handle high-dimensional temporal data by training a single model to predict temporal RUS values at multiple time lags simultaneously, achieving significant speedup while maintaining accuracy.

Contribution

RUS-aware MoE router with interaction-guided auxiliary losses

The authors design an interaction-aware routing mechanism that incorporates temporal RUS sequences through attention and recurrent modules, combined with auxiliary loss functions that enforce routing strategies aligned with redundancy, uniqueness, and synergy principles to improve expert specialization.

Table of Contents

Guiding Mixture-of-Experts with Temporal Multimodal Interactions | Novelty Validation