Modality Inflation in Multimodal Models

• Modality inflation is the phenomenon where adding non-text modalities to language models increases energy consumption and computational workload.
• Empirical analysis reveals that extra encoding stages can raise inference energy by up to 94%, with performance gains often driven by dominant text inputs.
• Mitigation strategies include rigorous ablation studies, dynamic voltage scaling, and architecture-specific configurations to balance efficiency and performance.

“Modality inflation” refers to the phenomenon in multimodal machine learning systems—especially in multimodal LLMs (MLLMs)—where the addition of new input modalities (typically beyond text, such as images, audio, or sensor streams) leads to a disproportionate increase in inference workload, energy consumption, and reported performance metrics. This arises from both the technical cost of extra encoding stages and increased sequence lengths, as well as from methodological pitfalls in task evaluation that may overstate the value of added modalities even when their information contribution is marginal or redundant. The term encompasses inefficiencies in computation and energy, as well as the inflation of performance metrics due to improper ablation and reporting practices.

1. Definition and Formal Properties

Modality inflation manifests when extending a LLM to process non-text modalities such as images. In these architectures, each non-text input must be encoded (e.g., via a vision transformer or other modality-specific backbone) into a sequence of tokens compatible with the LLM. These visual or non-text tokens are then concatenated with any text tokens, expanding the total context and necessitating extra computation in the downstream transformer. This chain yields increased energy for inference and can artificially boost performance metrics if unimodal baselines are neglected.

The net energy expenditure for one inference request is decomposed as:

$$E_{\mathrm{total}} = E_{\mathrm{vision}} + E_{\mathrm{prefill}} + E_{\mathrm{decode}}$$

where $E_{\mathrm{vision}}$ covers modality-specific encoding (e.g., ViT or Q-Former), $E_{\mathrm{prefill}}$ is the forward pass over the expanded token sequence, and $E_{\mathrm{decode}}$ is the energy for autoregressive output generation.

The relative energy overhead from modality inflation is:

$$\Delta E_{\mathrm{modality}} = \frac{E_{\mathrm{multimodal}} - E_{\mathrm{text}}}{E_{\mathrm{text}}} \times 100\%$$

where $E_{\mathrm{multimodal}}$ is the energy per request for image + text inputs, and $E_{\mathrm{text}}$ is for text-only baselines matched on total token count (Moghadampanah et al., 27 Dec 2025).

2. Energy and Computational Analysis in MLLMs

Stage-level analysis in MLLMs demonstrates that modality inflation can result in substantial energy overheads, highly sensitive to architectural choices:

Model	Encoder Energy (J)	Prefill Energy (J)	$\Delta E_{\text{modality}}$ (%)
Qwen2.5-VL	20.81	Large ($\sim$2K–3K visual tokens)	94
LLaVA-1.5	$\sim$3	~25	25
InternVL3	8.12	8.12	18
LLaVA-OneVision	9.52	95.78 (~3.7K visual tokens)	17

Prefill energy approximately scales linearly with the total input sequence length; in LLaVA-OneVision, each extra 1,000 visual tokens adds $\sim$30 J to $E_{\mathrm{prefill}}$. In models where vision encoding is costly and emits very long sequences, the overall energy cost from modality inflation can exceed 90% (Moghadampanah et al., 27 Dec 2025).

GPU utilization studies reveal that vision encoding introduces mid-power phases (100 W–250 W) with significant underutilization, contrasting with the near-peak sustained power during text-only inference. Complexity scaling by image resolution and number of images further accentuates these energy costs, with sharp jumps observed at resolution thresholds that increase tile/token counts (e.g., in Qwen2.5-VL above 1024×1024).

3. Methodological Drivers and Performance Metric Inflation

“Modality inflation” is also used to describe the misleading elevation of reported performance metrics when additional modalities are included without rigorous ablation. Empirical studies show that naive multimodal fusion—such as the addition of audio or visual streams to text—can result in minor absolute performance gains, often driven by already-dominant modalities.

For a model trained on a set of modalities $\mathcal{M}$, let $Acc(\mathcal{S})$ denote performance when only subset $\mathcal{S}\subseteq\mathcal{M}$ is used. Define the drop on masking modality $m$ as:

$$\Delta Acc_m = Acc(\mathcal{M}) - Acc(\mathcal{M} \setminus \{m\})$$

Results show that in sentiment and emotion recognition tasks, $\Delta Acc_\text{Text}$ greatly exceeds $\Delta Acc_\text{Audio}$ or $\Delta Acc_\text{Video}$, and text-only models achieve within 99% of the full multimodal model’s accuracy. In certain setups, adding weak video modalities can degrade performance; yet, multimodal results are sometimes still reported as significant breakthroughs due to comparison with weak unimodal baselines rather than the strongest one (Haouhat et al., 2023).

4. Quantifying and Mitigating Modality Inflation

Systematic ablation and transfer experiments are essential to reveal when multimodal integration is genuinely synergistic versus merely inflated. The “leave-one-out” methodology, where each modality is masked and $\Delta Acc_m$ reported, provides a transparent approach to discern unique information contribution.

Recommendations to mitigate modality inflation in performance reporting include:

• Always report unimodal baselines matched in architecture and capacity.
• Conduct and publish leave-one-modality-out ablation studies ($\Delta Acc_m$).
• Evaluate robustness to missing or corrupted modalities during inference.
• Analyze whether improvements over strong baselines merit the complexity of fusion mechanisms (Haouhat et al., 2023).

5. Information-Theoretic Approaches to Multimodality

Beyond empirical ablations, information-theoretic metrics have been proposed for systematic quantification of modality and interaction heterogeneity. The High-Modality Multimodal Transformer (HighMMT) framework introduces:

• Modality heterogeneity, $d(X_1; X_2)$, measuring transfer difficulty between two modalities $X_1$, $X_2$ via task loss differences on fine-tuning.
• Interaction heterogeneity, $d(X_1,X_2; X_3,X_4)$, capturing differences in how pairs of modalities interact.

By clustering modalities and interaction pairs based on these metrics, HighMMT enables parameter sharing that is proportional to the unique information contributed, effectively controlling computational and parameter inflation (Liang et al., 2022). Empirical results demonstrate non-negative performance gains as modalities are added and strong parameter efficiency compared to naive fully-shared or per-task models, suggesting a scalable solution to the core challenges of modality inflation.

6. Optimization and Serving Implications

To address the energy and computational cost of modality inflation in inference, practical optimization strategies have been validated:

• Stage-wise dynamic voltage and frequency scaling (DVFS): Distinct GPU core frequencies for each inference stage (e.g., lower for vision encoding, higher for prefill) enable energy savings while managing latency. For example, in InternVL3, raising encoding stage core clock reduces latency by 11.8% with a 24.9% increase in energy (Moghadampanah et al., 27 Dec 2025).
• Model-specific serving configurations: Given the wide variation in modality inflation overheads (17%–94%), using tuned, architecture-aware policies is advocated over one-size-fits-all approaches.
• Input-aware scheduling: Monitoring input resolution and image count assists in batching strategies and resource allocation.
• Service-Level Objective (SLO) tracking: Integrating feedback for latency and throughput into energy control loops for real-time workload optimization.

These operational guidelines are necessary to contain modality inflation and ensure efficiency for real-world deployment (Moghadampanah et al., 27 Dec 2025).

7. Broader Implications, Controversies, and Future Directions

Modality inflation underscores a central tension in multimodal research: although integrating diverse modalities holds the promise of richer system understanding and greater robustness, naive or uncritical addition of modalities can lead to bloated models, excessive computation, and overstated gains. A holistic solution requires rigorous reporting, robust ablation, and principled architectural design.

Future research directions include: extending stage-wise energy optimization to real-time closed-loop controllers; exploring disaggregated pipelines for high-complexity inputs; and generalizing heterogeneity-based parameter sharing to audio, video, and sensor modalities. Addressing these open problems is critical to achieving the full potential of multimodal learning without succumbing to the inefficiencies and misrepresentations that define modality inflation (Moghadampanah et al., 27 Dec 2025, Liang et al., 2022, Haouhat et al., 2023).