Dynamic Multimodal Integration

• Dynamic Multimodal Integration is the adaptive fusion of heterogeneous data sources that adjusts modality contributions based on reliability, context, and task demands.
• It employs methods like reliability-aware weighting, dynamic routing, and iterative refinement to optimize feature, decision, and feedback-level interactions.
• This adaptive approach enhances accuracy and efficiency in domains such as audio-visual speech recognition and sentiment analysis, addressing challenges of noise and missing data.

Dynamic multimodal integration denotes the adaptive combination of heterogeneous information sources in which modality contribution, fusion topology, or memory use varies with reliability, context, time, or task demands. In recent arXiv work, the term spans frame-wise state-posterior weighting in large-vocabulary audio-visual speech recognition, data-dependent expert or path selection in multimodal networks, iterative feedback from fused states to unimodal encoders, inference-time selection of recovered modalities under missing data, retrieval-conditioned knowledge injection in vision-language reasoning, and closed-loop 4D world modeling that couples generation, reconstruction, and persistent memory (Yu et al., 2020, Xue et al., 2022, Shankar et al., 2022, Perry et al., 15 Jan 2025, Du et al., 30 Jan 2026, Chen et al., 31 Dec 2025).

1. Conceptual scope and principal regimes

The literature does not treat dynamic multimodal integration as a single architectural primitive. Instead, it distinguishes several loci at which integration can occur: feature level, representation or attention level, posterior or decision level, iterative feedback across processing stages, and inference-time routing over modality subsets. A central recurring contrast is between static fusion, where all samples are processed with the same computation and the same modality combination, and dynamic fusion, where modality usage changes with input difficulty, noise, missingness, or task semantics (Xue et al., 2022, Tanaka et al., 15 Jun 2025, Luo et al., 16 Apr 2026).

In large-vocabulary audio-visual speech recognition, early integration is explicit feature concatenation,

$$o_t=[(o_t^\mathrm{A})^T,(o_t^\mathrm{VS})^T,(o_t^\mathrm{VA})^T]^T,$$

whereas late integration combines stream posteriors after separate modality-specific modeling (Yu et al., 2020). In multimodal sentiment classification on CMU-MOSEI, the same design space appears as late-stage majority voting, early-stage concatenation of learned modality states, and an attention-enhanced fusion model built on top of that early-fusion setup (Lee et al., 14 Jan 2025). In broader multimodal learning, “Progressive Fusion” reframes late fusion as an iterative refinement loop with backward connections from fused representations to earlier unimodal layers (Shankar et al., 2022), while “Zoom and Shift is All You Need” formulates fusion as latent-space calibration and displacement into a joint space (Qin, 2024).

Regime	Characteristic operation	Representative papers
Static feature fusion	Concatenate modality features before prediction	(Yu et al., 2020, Lee et al., 14 Jan 2025)
Decision or posterior fusion	Combine modality predictions or state posteriors after separate encoders	(Yu et al., 2020, Lee et al., 14 Jan 2025)
Progressive or aligned fusion	Feed fused context back to unimodal encoders or align spaces before fusion	(Shankar et al., 2022, Qin, 2024)
Dynamic routing or scheduling	Choose experts, fusion cells, modalities, or topologies per sample	(Xue et al., 2022, Tanaka et al., 15 Jun 2025, Luo et al., 16 Apr 2026, Du et al., 30 Jan 2026)

This design space also broadens the meaning of “modality.” Some papers use the conventional sensory triad of text, audio, and vision; others include structured and unstructured external knowledge, retrieved memory, world-state geometry, or longitudinal genetic, environmental, and neurobiological signals as distinct informational sources to be integrated over time (Perry et al., 15 Jan 2025, Jang et al., 31 May 2025, Wei et al., 2 May 2026). The common theme is that integration is treated as conditional rather than uniform.

2. Reliability-aware weighting and routing

A major line of work treats dynamic integration as reliability-sensitive weighting. In large-vocabulary AVSR, the central late-fusion rule is

$$\log \widetilde{p}(s |o_t)=\sum_{i}^{}\lambda_t^i\cdot \log {p}(s |o_t^{i}),$$

where $\lambda_t^i$ is predicted frame by frame by a stream integration net from model-based and signal-based reliability indicators (Yu et al., 2020). Those indicators include entropy, dispersion, posterior-difference measures, temporal divergence, MFCC-derived audio cues, estimated SNR, soft VAD, mouth IDCT coefficients, lighting, blur, and head pose. This makes fusion explicitly conditional on modality reliability rather than fixed by architecture alone.

Dynamic Modality Scheduling generalizes this logic to multimodal large models. For modality $m$, it computes confidence from predictive entropy, epistemic uncertainty from Monte Carlo dropout variance, and semantic consistency from inter-modal cosine similarity, then converts them into soft weights

$$\omega_m(x) = \frac{\exp(\alpha c_m(x) - \beta u_m(x) + \gamma s_m(x))}{\sum_{j=1}^{M} \exp(\alpha c_j(x) - \beta u_j(x) + \gamma s_j(x))},$$

and fuses modality embeddings by

$$h = \sum_{m=1}^{M} \omega_m(x) \cdot f^{(m)}(x^{(m)}).$$

A second component, the Modality Weight Consistency Loss,

$\mathcal{L}_{\text{mwcl} = \sum_{m=1}^{M} \omega_m(x) \cdot \| h - f^{(m)}(x^{(m)}) \|_2^2,$

regularizes the fused representation toward the embeddings that the scheduler itself judged reliable (Tanaka et al., 15 Jun 2025).

Dynamic Multimodal Fusion, or DynMM, makes routing decisions discrete. At modality level it chooses one expert $E_i$ from a bank of experts over different modality subsets,

$y = \sum_{i=1}^B g_i E_i(\mathbf x_i),$

with one-hot $\mathbf g$, while at fusion level it chooses one operation inside each fusion cell,

$$\log \widetilde{p}(s |o_t)=\sum_{i}^{}\lambda_t^i\cdot \log {p}(s |o_t^{i}),$$0

A resource-aware loss,

$$\log \widetilde{p}(s |o_t)=\sum_{i}^{}\lambda_t^i\cdot \log {p}(s |o_t^{i}),$$1

or its fusion-level analogue, makes routing cost-sensitive rather than purely accuracy-driven (Xue et al., 2022). DyMo moves the same idea to incomplete multimodal classification: missing modalities are first recovered, then greedily selected only if their calibrated reward $$\log \widetilde{p}(s |o_t)=\sum_{i}^{}\lambda_t^i\cdot \log {p}(s |o_t^{i}),$$2 is positive, so recovered modalities are treated as candidates rather than mandatory inputs (Du et al., 30 Jan 2026).

Taken together, these works define a spectrum from soft weighting to hard routing. The unifying principle is that reliable modalities should contribute more, unreliable recovered modalities should be rejected, and costly multimodal computation should not be uniformly applied when a cheaper path is sufficient.

3. Iterative refinement, hierarchical fusion, and training-time modulation

A second line of work makes integration dynamic by allowing fused information to reshape earlier representations. Progressive Fusion starts from a standard multimodal model $$\log \widetilde{p}(s |o_t)=\sum_{i}^{}\lambda_t^i\cdot \log {p}(s |o_t^{i}),$$3, augments it with a fused context $$\log \widetilde{p}(s |o_t)=\sum_{i}^{}\lambda_t^i\cdot \log {p}(s |o_t^{i}),$$4, initializes $$\log \widetilde{p}(s |o_t)=\sum_{i}^{}\lambda_t^i\cdot \log {p}(s |o_t^{i}),$$5, and iterates

$$\log \widetilde{p}(s |o_t)=\sum_{i}^{}\lambda_t^i\cdot \log {p}(s |o_t^{i}),$$6

$$\log \widetilde{p}(s |o_t)=\sum_{i}^{}\lambda_t^i\cdot \log {p}(s |o_t^{i}),$$7

before predicting from $$\log \widetilde{p}(s |o_t)=\sum_{i}^{}\lambda_t^i\cdot \log {p}(s |o_t^{i}),$$8 (Shankar et al., 2022). This does not process new sensory input at each step; rather, it repeatedly refines the same sample so that fused multimodal context can alter unimodal feature extraction before information is irreversibly compressed.

SUMMER applies dynamicity inside a conversational emotion-recognition pipeline. Its Sparse Dynamic Mixture of Experts computes

$$\log \widetilde{p}(s |o_t)=\sum_{i}^{}\lambda_t^i\cdot \log {p}(s |o_t^{i}),$$9

then refines routing with Gumbel noise and sparse activation, so that expert participation varies with the current utterance (Li et al., 31 Mar 2025). Hierarchical Cross-Modal Fusion first fuses text with audio,

$\lambda_t^i$0

then fuses the result with vision,

$\lambda_t^i$1

rather than collapsing text, audio, and visual streams in one flat step. Interactive Knowledge Distillation adds a frozen unimodal teacher and optimizes

$\lambda_t^i$2

so multimodal fusion is guided by a stable unimodal prior as well as labels.

A different notion of dynamicity appears in the brain-inspired Inverse Effectiveness driven Multimodal Fusion framework. Here the key variable is not a per-sample inference-time gate but a batch-level coefficient

$\lambda_t^i$3

which scales only the fusion-module gradient,

$\lambda_t^i$4

so fusion is learned more aggressively when unimodal cues are weak and more conservatively when they are already strong (He et al., 15 May 2025). This makes dynamic integration an optimization-level mechanism rather than an inference-time routing rule.

These approaches share a structural insight: multimodal integration need not be a single merge operator. It can be a refinement process, a hierarchy of pairwise interactions, or a training-time controller on how strongly the model should learn to couple modalities.

4. Knowledge, memory, prototypes, and world-state integration

Dynamic multimodal integration increasingly extends beyond sensory fusion into knowledge retrieval, memory retrieval, prototype evolution, and persistent world state. In knowledge-intensive vision-language reasoning, Adaptive Knowledge-Guided Pretraining for Large Vision-LLMs forms visual and textual embeddings

$\lambda_t^i$5

fuses them into $\lambda_t^i$6, retrieves external knowledge by

$\lambda_t^i$7

and injects the result through

$\lambda_t^i$8

A contrastive alignment loss then shapes a shared multimodal-knowledge space during pretraining (Perry et al., 15 Jan 2025).

Dynamic Multimodal Prototype Learning in vision-LLMs makes class representations themselves dynamic. ProtoMM represents each class prototype as a discrete distribution over textual descriptions and visual particles,

$\lambda_t^i$9

matches test-image distributions to prototypes with entropic optimal transport, and updates visual particles online by

$m$0

Here dynamic integration is test-time adaptation: class semantics shift as unlabeled test images arrive (Zhu et al., 4 Jul 2025).

In multimodal conversation, the $m$1 framework turns dynamic integration into memory retrieval over text, image, and audio. The Retriever Module scores current context $m$2 against stored multimodal memory $m$3 by

$m$4

retrieves relevant memories, and feeds them back into the Dialogue Module together with current conversation and multimodal inputs (Jang et al., 31 May 2025). D2R introduces an external multimodal scratchpad for dynamic spatial reasoning: an MLLM emits a reasoning increment $m$5, a scheduler selects a tool, and the tool produces an updated visual draft $m$6, so text reasoning writes back into visual state and then reads from that state in the next iteration (Ou et al., 22 May 2025).

TeleWorld moves this logic to world modeling. Its generator produces sparse planning frames $m$7, reconstruction inserts them into a dynamic 4D spatio-temporal representation, and rendered guidance is concatenated with target tokens,

$m$8

inside the next diffusion-transformer step (Chen et al., 31 Dec 2025). In a different retrieval-and-knowledge-grounding setting, DIM uses ChatGPT to build richer KB-side entity descriptions, BLIP-2 to derive image-dependent expert text $m$9, and then fuses expert-guided text and image features before ranking candidate entities by cosine similarity (Song et al., 2024).

Across these systems, dynamic multimodal integration is no longer limited to deciding how current modalities interact. It includes deciding what prior knowledge to retrieve, what memory to reactivate, how class prototypes should drift, and how a persistent world model should guide future generation.

5. Empirical patterns across application domains

The strongest quantitative evidence for dynamic multimodal integration often appears when one modality is weak, noisy, missing, or intermittently useful. In large-vocabulary AVSR on LRS2, static early fusion averaged 41.68% WER, worse than the audio-only baseline’s 39.84%, while late state-posterior fusion with learned dynamic weights achieved 29.89% average WER and a 24.97% relative WER reduction; the oracle-weight baseline reached 23.23%, indicating substantial remaining headroom (Yu et al., 2020). This is a direct demonstration that in a regime with weak visual streams and noisy audio, reliability-aware late fusion can recover gains that static feature fusion does not.

In multimodal sentiment analysis on CMU-MOSEI, the empirical message is different. Early-stage fusion reached 71.87% accuracy, late-stage fusion 66.23%, and the attention-based model 72.39% (Lee et al., 14 Jan 2025). Here the main gain came from feature-level joint learning rather than from a strongly dynamic attention mechanism, and the reported attention improvement over early fusion was only 0.52 percentage points. This suggests that the benefits of dynamicity are domain-specific: in some tasks the decisive factor is shared representation learning, whereas in others it is reliability-aware adaptation.

Dynamic routing and scheduling yield explicit accuracy-efficiency tradeoffs. DynMM reduced CMU-MOSEI computation by 46.5% with only a negligible accuracy loss in one setting, and on NYU Depth V2 semantic segmentation it reduced depth-encoder MAdds by 21.1% while improving mIoU, or by 55.1% with only a minor loss depending on the variant (Xue et al., 2022). Dynamic Modality Scheduling improved BLIP-2 static fusion from 72.1 to 74.4 on VQA, and under image noise achieved 65.9 versus 56.7 VQA accuracy, while under text noise it achieved 67.4 versus 59.1 (Tanaka et al., 15 Jun 2025).

Dynamic methods also show gains in domains where memory, missingness, or semantic ambiguity dominate. SUMMER reached 79.11 weighted accuracy and 78.95 weighted F1 on IEMOCAP, and 68.78 weighted accuracy and 69.81 weighted F1 on MELD (Li et al., 31 Mar 2025). ProtoMM reported a 1.03% average accuracy improvement over state-of-the-art methods on ImageNet and its variant datasets (Zhu et al., 4 Jul 2025). DyMo improved prior incomplete-MDL results by 5.67% on PolyMNIST at 80% missingness and by 1.97% AUC on infarction with 70% missing tabular features (Du et al., 30 Jan 2026). TeleWorld achieved 78.23 WorldScore-Static and 66.73 WorldScore-Dynamic, with the strongest competing dynamic score in the reported table at 59.12 (Chen et al., 31 Dec 2025). In the SpeechWellness challenge, a lightweight three-branch system with weighted multimodal fusion reported a 78% reduction in model parameters and a 5% improvement in accuracy over the official challenge baseline (Sun et al., 25 Aug 2025).

These results do not point to one universally superior dynamic mechanism. They instead show a repeated pattern: dynamic integration is most valuable when modality informativeness is heterogeneous across samples, when one modality degrades or disappears, when knowledge or memory must be fetched selectively, or when long-horizon consistency requires an explicit state outside immediate token context.

6. Limitations, ambiguities, and research frontiers

The literature is explicit that not every method labeled “dynamic” implements full input-conditional multimodal control. The CMU-MOSEI study titled “Dynamic Multimodal Sentiment Analysis” mainly compares static late fusion, static early fusion, and an attention-enhanced model, and explicitly states that the implementation does not provide temporal adaptive fusion, reliability estimation, dynamic routing, or missing-modality adaptation (Lee et al., 14 Jan 2025). The SpeechWellness network uses learnable modality weights $$\omega_m(x) = \frac{\exp(\alpha c_m(x) - \beta u_m(x) + \gamma s_m(x))}{\sum_{j=1}^{M} \exp(\alpha c_j(x) - \beta u_j(x) + \gamma s_j(x))},$$0, but those weights are global trainable scalars rather than sample-dependent gates (Sun et al., 25 Aug 2025). The inverse-effectiveness framework is dynamic at training time, because it modulates fusion gradients batch by batch, but it does not define an inference-time dynamic gate (He et al., 15 May 2025). “Zoom and Shift is All You Need” is dynamic mainly in the sense of latent-space mutual transformation, while token-wise routing or explicit reliability-aware scheduling is not specified (Qin, 2024).

Several papers also report under-specification or unresolved gaps. In AKGP-LVLM, structured knowledge is formalized through a GNN, but the unstructured knowledge branch is asserted more clearly than it is mathematically specified (Perry et al., 15 Jan 2025). In Dynamic Modality Scheduling, the abstract mentions a “learnable or rule-based scheduler,” while the method section concretely specifies a softmax with scalar coefficients $$\omega_m(x) = \frac{\exp(\alpha c_m(x) - \beta u_m(x) + \gamma s_m(x))}{\sum_{j=1}^{M} \exp(\alpha c_j(x) - \beta u_j(x) + \gamma s_j(x))},$$1 rather than a separate learned scheduler network (Tanaka et al., 15 Jun 2025). In AVSR, the learned dynamic weighting system remains far from the oracle-weight upper bound, and the paper identifies better reliability estimation, better fusion-network topology and loss design, and deeper visual encoders as future directions (Yu et al., 2020). In Chain of Modality, the planner is prompt-based, there is no explicit topology scorer or learned routing confidence, and failures can occur when instruction following is weak (Luo et al., 16 Apr 2026). DyMo’s greedy test-time selection is explicitly sequential rather than globally optimal over modality subsets (Du et al., 30 Jan 2026). TeleWorld achieves real-time interaction, but the reported “Minimum Memory Peak Prediction” mode still incurs roughly one second of feedback latency (Chen et al., 31 Dec 2025).

A broader conceptual caution follows from these limitations. Dynamic multimodal integration can refer to at least five distinct mechanisms: input-conditional weighting, hard routing, iterative refinement, retrieval or memory activation, and training-time modulation. Treating them as interchangeable obscures real differences in what adapts, when it adapts, and what evidence is available to the controller. The current literature suggests that future progress will depend on sharper reliability estimation, richer sequence-level or token-level adaptation, stronger handling of missing and corrupted modalities, more explicit integration of structured and unstructured knowledge, and better coupling between persistent external memory and online fusion.