Multimodal Information Prompt Fusion

• Multimodal Information Prompt Fusion is a paradigm that converts signals from different modalities into prompt-like cues for conditioned downstream processing.
• It leverages dynamic, mapped, and expert-routed prompts within frozen backbones to fuse visual, LiDAR, and textual information efficiently.
• Empirical studies demonstrate significant parameter efficiency and improved performance in tasks like semantic occupancy prediction and multimodal sentiment analysis.

Searching arXiv for papers directly relevant to Multimodal Information Prompt Fusion and related prompt-based multimodal fusion frameworks. Across the works considered here, Multimodal Information Prompt Fusion (MIPF) can be understood as a prompt-mediated multimodal fusion paradigm in which side information from one or more modalities is compressed, projected, calibrated, or otherwise transformed into prompt-like conditioning signals before or during downstream inference. In the strictest sense, the term explicitly names VoxelHound’s BEV-space fusion module for panoramic multimodal semantic occupancy prediction (Zhao et al., 13 Mar 2026). More broadly, the surrounding literature suggests a family of methods that inject multimodal prompts into Transformer layers, frozen backbones, diffusion conditioning stacks, or task-specific prompt templates rather than relying only on direct concatenation or symmetric feature fusion (Jiang et al., 2023).

1. Terminological scope and research lineage

The most explicit use of the name appears in VoxelHound, where MIPF is a BEV-space asymmetric fusion module for quadruped semantic occupancy prediction. There, LiDAR supplies stable geometry, panoramic RGB, thermal, and polarization supply complementary semantics, and the module is designed around the principle of “geometry dominance with semantic supplementation” (Zhao et al., 13 Mar 2026).

A broader prompt-fusion lineage is visible across earlier and contemporaneous work. “Modular and Parameter-Efficient Multimodal Fusion with Prompting” introduces PromptFuse and BlindPrompt, where learned prompt vectors align frozen unimodal encoders with a frozen pretrained LLM (Liang et al., 2022). “Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts” defines unified multimodal prompts and then fuses predictions from multiple prompt views probabilistically (Yang et al., 2022). “Conditional Prompt Tuning for Multimodal Fusion” and “MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal Fusion” move from static prompts to conditional and expert-routed prompts (Jiang et al., 2023, Jiang et al., 2024). DPLNet reframes RGB-D/T dense prediction as multimodal prompt generation plus internal prompt adaptation inside a frozen RGB backbone (Dong et al., 2023). MultiFusion shows that multilingual, arbitrarily interleaved image-text prompts can be fused into a single conditioning representation for diffusion (Bellagente et al., 2023).

This suggests that “MIPF” is best treated as both a specific named module and a wider design pattern: multimodal information is not merely merged, but converted into prompts, prompt-like memories, or prompt-conditioned calibration signals that steer a downstream encoder or generator.

2. What “prompt” denotes in multimodal fusion

The literature does not use a single prompt ontology. In PromptFuse and BlindPrompt, prompts are $N$ randomly initialized trainable vectors placed in the embedding layer of the PLM and optimized on the downstream multimodal task; they are task-specific continuous prompt parameters rather than textual instructions (Liang et al., 2022). In Multimodal Prompt Transformer for emotion recognition, prompt information is described as filtered, emotion-relevant multimodal guidance that participates in encoding textual features at each attention layer (Zou et al., 2023).

Conditional Prompt Tuning decomposes the vanilla prompt into three specialized components: a static prompt $\mathbf{P}_s$, a dynamic prompt $\mathbf{P}_d$, and a mapped prompt $P_m$. The dynamic prompt is conditioned on the complementary modality, while the mapped prompt directly injects complementary information into the main encoder (Jiang et al., 2023). MoPE keeps the same decomposition but replaces a single dynamic prompt with a mixture of prompt experts selected by multimodal routing (Jiang et al., 2024).

In DPLNet, prompts are dense stage-wise tensors rather than short token prefixes. The auxiliary modality is embedded as an initial prompt $P^0$, recursively fused with RGB features by the Multimodal Prompt Generator, and then injected into the frozen RGB backbone by residual addition (Dong et al., 2023). In VFPTrack, prompts are modality-related visual prompts in the spatial domain together with frequency-domain prompts obtained from FFT, plus a fused modality prompt produced by the Modality Fusion Prompt Generator (Yang et al., 24 Sep 2025). In VoxelHound, a prompt is neither a text token nor a static learned vector, but a modality-specific compact semantic embedding produced by global average pooling and an MLP and then used as key/value memory for LiDAR-guided attention (Zhao et al., 13 Mar 2026).

The prompt concept also extends to hybrid prompt templates. MultiPoint combines projected image slots, image captions, text, aspects, and <mask>-based verbalizers inside unified multimodal prompts (Yang et al., 2022). AMPLE combines a manual masked template with trainable continuous <head> and <tail> tokens and then fuses prompt representations with multimodal cross-attentive features (Xu et al., 2024). MultiFusion serializes text fragments and image-token blocks into one autoregressive multimodal prompt sequence (Bellagente et al., 2023).

A stable conclusion is that, in this literature, a prompt is not synonymous with natural-language instruction. It may be a trainable vector bank, a dynamic cross-modal conditioner, a modality summary, a fused BEV key/value memory, a dense feature prompt, or a hybrid prompt-template interface.

3. Architectural realizations

The main architectural variants can be organized by where prompt-mediated fusion occurs.

Representative system	Task/domain	Prompt-fusion mechanism
PromptFuse / BlindPrompt (Liang et al., 2022)	VQAv2, MUStARD	Shared prompt bank fused with modality embeddings inside a frozen PLM
Conditional Prompt Tuning (Jiang et al., 2023)	Image-text classification	Complementary modality conditions all layers of the main frozen encoder
DPLNet (Dong et al., 2023)	RGB-D/T segmentation	Multi-stage multimodal prompt generation plus feature adaptation in a frozen RGB backbone
MultiFusion (Bellagente et al., 2023)	Multilingual multimodal image generation	Interleaved image-text prompts fused in a multimodal LM, then used as diffusion conditioning
VFPTrack (Yang et al., 24 Sep 2025)	RGB-T tracking	Spatial/Fourier prompts plus a fused modality prompt injected at each layer
VoxelHound MIPF (Zhao et al., 13 Mar 2026)	Panoramic occupancy prediction	Image-modality prompts attend into LiDAR BEV and modulate geometry residually

Several structural themes recur. One is asymmetric fusion: one modality is the anchor and the others act as conditioning signals. Text is the main stream in multimodal ERC and conditional prompt tuning, while LiDAR is the main stream in VoxelHound (Zou et al., 2023, Zhao et al., 13 Mar 2026). Another is deep prompt injection: prompts are inserted not once, but at each attention layer or at all stages of a hierarchy, as in Multimodal Prompt Transformer, DPLNet, VFPTrack, Conditional Prompt Tuning, and MoPE (Dong et al., 2023, Jiang et al., 2024). A third is parameter-efficient frozen-backbone reuse: the backbone remains mostly frozen, and expressiveness is shifted into prompt generators, prompt experts, adapters, or prompt-conditioned attention (Liang et al., 2022, Jiang et al., 2023).

A nearby but prompt-adjacent development is pre-fusion calibration. “Before Fusion, Ask What to Keep” proposes contextual calibration of modality features before they are merged by a downstream predictor, using cross-source support and conflict cues to generate instance-wise and dimension-wise modulation signals (Liu et al., 1 Jun 2026). This suggests a pre-prompt stage in which a system estimates what should be retained before prompt-mediated fusion occurs.

4. Recurrent mathematical patterns

No single equation defines MIPF across the literature, but several recurrent formulations appear.

A standard prompt-conditioned encoder input is

$$\mathbf{\hat{x}^i = [x^i_0,\mathbf{P}_s,R(\psi_y), f_m(\psi_y),\mathbf{T}^{i-1}]}$$

where the complementary modality representation $\psi_y$ conditions both a dynamic prompt and a mapped prompt for every layer of the main encoder (Jiang et al., 2023). MoPE retains this structure and makes the dynamic prompt an expert mixture,

$$\mathbf{P}_d = \sum_{j=1}^k \mathbf{r}_j \mathbf{E}_j,$$

with routing weights computed from both the complementary representation and the current main-modality state (Jiang et al., 2024).

A hierarchical dense-prediction variant appears in DPLNet:

$P^i = \mathtt{MPG}(Z_{\text{RGB}^{i-1}, P^{i-1}), \qquad Z^{i-1}=Z_{\text{RGB}^{i-1}+P^i.$

Here the auxiliary modality becomes an initial prompt, the prompt is updated stage by stage, and the fused prompt is added residually to the RGB backbone before frozen encoding (Dong et al., 2023).

VoxelHound’s explicit MIPF module compresses each image-modality BEV feature into a prompt

$\mathbf{p}_{m}=\mathcal{M}_{m}\left(\operatorname{GAP}\left(\tilde{\mathbf{F}_{c}^m\right)\right),$

stacks the prompts, and lets LiDAR BEV queries attend to them:

$\mathbf{P}_s$0

The attended semantic signal is converted into a modulation mask and applied back to LiDAR features by residual reweighting rather than direct replacement (Zhao et al., 13 Mar 2026).

MultiFusion uses a different interface: the interleaved multimodal prompt is encoded by a multimodal LLM, and the last hidden layer $\mathbf{P}_s$1 is used directly as diffusion conditioning, $\mathbf{P}_s$2. Because one image corresponds to 144 token embeddings, it additionally modifies attention scores by

$\mathbf{P}_s$3

to rebalance text and image influence at inference time (Bellagente et al., 2023).

MultiPoint adds a distinct probabilistic fusion layer across prompt variants. After obtaining per-prompt class posteriors $\mathbf{P}_s$4, it fuses them by

$\mathbf{P}_s$5

This is decision-level prompt fusion rather than token-level or hidden-state fusion (Yang et al., 2022).

Taken together, these formulations suggest that MIPF is not a single operator. It is a family of prompt-conditioned transformations that may act by token concatenation, residual addition, attention key/value injection, prompt recursion, expert routing, or posterior fusion.

5. Empirical behavior, efficiency, and scaling

The strongest recurring empirical claim is parameter efficiency. PromptFuse and BlindPrompt train about 15K parameters, compared with 86M for fine-tuning and 1M for JointProj, while remaining competitive in low-resource VQAv2 and MUStARD (Liang et al., 2022). Conditional Prompt Tuning reports 2.6M trainable parameters against roughly 196M–197M for full fine-tuning, i.e. about 0.7%, while beating prompt baselines and matching or surpassing fine-tuning on SNLI-VE, UPMC_Food101, and MM-IMDB (Jiang et al., 2023). MoPE reports state-of-the-art prompt-fusion performance across six multimodal datasets spanning four modalities while requiring only 0.8% of the trainable parameters (Jiang et al., 2024).

Dense prediction shows a similar trend. DPLNet introduces 3.88M trainable parameters for multimodal feature fusion and learning, 4.4% of the pretrained backbone parameters, plus a 3.27M-parameter decoder. It reports 59.3 mIoU on NYUDv2, 52.8 mIoU on SUN RGB-D, and 86.7 mIoU on PST900 while using far fewer trainable parameters than heavier multimodal segmentation systems (Dong et al., 2023). Its ablations show that removing the Multimodal Prompt Generator or the Multimodal Feature Adapter reduces NYUDv2 from 58.3 mIoU to 57.4 mIoU, and that using MPG and MFA across all stages is best (Dong et al., 2023).

Tracking and robotics supply explicit MIPF-style ablations. VFPTrack reaches 58.5 SR, 73.5 PR, and 69.8 NPR on LasHeR, and its component study shows baseline $\mathbf{P}_s$6, +MFPG $\mathbf{P}_s$7, and +Visual Fourier Prompt $\mathbf{P}_s$8. The best Fourier prompt ratio is $\mathbf{P}_s$9, and all-layer prompt fusion performs best (Yang et al., 24 Sep 2025). VoxelHound’s MIPF module raises mIoU from 22.74 to 23.14 when added alone, and together with VJC reaches 23.34. Its best reported MIPF setting uses prompt channel dimension $\mathbf{P}_d$0 and $\mathbf{P}_d$1 attention heads (Zhao et al., 13 Mar 2026).

Generative work shows that prompt fusion can transfer capability rather than only reduce parameters. MultiFusion claims less than 5% of the training compute needed to build a comparable diffusion model from scratch, while allowing a diffusion model trained only on English monomodal conditioning to respond to multilingual, interleaved multimodal prompts. On MCC-250, MultiFusion multimodal prompting reaches 58.35% two objects with correct colors versus 29.92% for Stable Diffusion (Bellagente et al., 2023). In few-shot sentiment analysis, MultiPoint improves over prior few-shot multimodal prompt methods across six datasets and reports that CDS-based sampling significantly outperforms equal-per-class sampling constructed from the same number of instances (Yang et al., 2022).

A broader inference suggested by these results is that prompt fusion scales best when prompt capacity is increased structurally rather than by naively lengthening prompts. MoPE argues that increasing expert diversity is more effective than increasing prompt length (Jiang et al., 2024), and DPLNet similarly finds that moderate prompt length and prompt dimension outperform both smaller and larger settings (Dong et al., 2023).

6. Limitations, misconceptions, and unresolved directions

Several misconceptions recur in this area. One is that prompt fusion is equivalent to text prompting. The surveyed systems show otherwise: prompts may be learned vectors, BEV semantic summaries, dense stage-wise feature prompts, spatial/Fourier prompt sets, or multimodal template-plus-verbalizer constructions (Yang et al., 24 Sep 2025, Zhao et al., 13 Mar 2026). Another is that multimodal prompting must be symmetric. In practice, many systems are deliberately asymmetric: text is guided by non-text prompts in ERC, one modality conditionally prompts the other in Conditional Prompt Tuning and MoPE, and LiDAR queries image prompts in VoxelHound (Zou et al., 2023, Jiang et al., 2024).

The literature also exposes concrete limitations. Conditional Prompt Tuning explicitly relies on a single global-level representation $\mathbf{P}_d$2 of the complementary modality, which may ignore spatial structure and token-level alignment (Jiang et al., 2023). MultiFusion requires attention manipulation because one image contributes 144 token embeddings, and its outputs are sensitive to prompt order and modality imbalance (Bellagente et al., 2023). VFPTrack is sensitive to the Fourier prompt ratio $\mathbf{P}_d$3; $\mathbf{P}_d$4 and $\mathbf{P}_d$5 are both suboptimal, and the method uses only the real-valued Fourier representation rather than explicit amplitude/phase decomposition (Yang et al., 24 Sep 2025). VGMR’s pre-fusion calibration relies on summary-level comparisons and may therefore miss local conflicts or short-lived mismatches (Liu et al., 1 Jun 2026).

Some works also remain underspecified. Self-MI provides a useful objective-level idea—maximize mutual information between fused and unimodal representations and generate pseudo-labels for auxiliary unimodal tasks—but does not fully specify the final weighted joint objective (Nguyen et al., 2023). “Neural Dependency Coding inspired Multimodal Fusion” argues for synergy-maximizing regularization, but the exact multivariate synergy functional used in experiments is not written explicitly in the paper body (Shankar, 2021). “Deep Equilibrium Multimodal Fusion” provides a strong shared-state refinement perspective that is suggestive for prompt memory design, but it is not itself a prompt-token method (Ni et al., 2023).

A final bibliographic caution concerns nomenclature. Although arXiv entry “Efficient Multimodal Fusion via Interactive Prompting” appears thematically adjacent, the supplied source content is described as the CVPR author guidelines template rather than a substantive PMF paper, so it does not support a faithful technical reconstruction of PMF or its relation to MIPF (Li et al., 2023).

The open direction most strongly suggested by the assembled literature is an overview of three ideas: prompt-mediated fusion, pre-fusion contextual calibration, and objective-level dependency regularization. This suggests future MIPF systems in which multimodal prompts are not only generated adaptively, but are also calibrated by cross-modal support and conflict signals before fusion and regularized to preserve useful multimodal dependence (Liu et al., 1 Jun 2026, Shankar, 2021).