Modality-Asymmetric Encoding Principles

• Modality-asymmetric encoding is a representation strategy that assigns different capacities and processing to modalities based on their semantic and statistical differences.
• It adapts fusion, routing, and compression methods to accommodate modality-specific traits, as seen in remote sensing, personality assessment, and multimodal content moderation.
• These approaches improve performance by tailoring architectural design and training signals to reflect uneven information density, uncertainty, and task relevance.

Modality-asymmetric encoding denotes a family of representation-learning strategies that reject the assumption that heterogeneous inputs should be encoded, fused, aligned, or compressed symmetrically. Instead, modalities or roles are assigned unequal representational capacity, unequal interaction operators, unequal routing policies, or unequal optimization signals because their semantic content, statistical structure, temporal redundancy, uncertainty, or downstream utility differ. In multimodal content moderation, this principle is invoked to preserve information that is common, modality-specific, and only available through multimodal intersection (Yuan et al., 2023); in personality assessment it appears as trait-specific modality and fusion selection (Li et al., 9 Jun 2026); and in remote sensing it appears as heavier RGB encoding and lighter DSM encoding (Ye et al., 22 Jul 2025). The literature therefore suggests that modality-asymmetric encoding is best understood as a general design principle rather than a single canonical architecture.

1. Sources of asymmetry

A first source of asymmetry is semantic non-equivalence. In multimodal moderation, image and text are not treated as two redundant views of the same latent variable; some harmful intent may only be conveyed through the intersection of both modalities, while other evidence remains modality-specific. AM3 was introduced precisely to address this asymmetry in semantics between vision and language, combining asymmetric fusion with a cross-modality contrastive loss intended to learn knowledge that only appears in multimodality (Yuan et al., 2023).

A second source is task-conditioned heterogeneity of modality utility. In personality assessment, the argument is not merely that modalities differ globally, but that different outputs prefer different modalities. “Traits Run Deeper” states that most prior systems use a uniform multimodal fusion strategy across all personality dimensions, thereby overlooking trait-specific modality preferences and introducing cross-modal interference. Its core claim is that Extraversion, Agreeableness, Honesty-Humility, and Conscientiousness are not best predicted by one shared modality composition (Li et al., 9 Jun 2026).

A third source is statistical and geometric non-equivalence between sensing channels. AMMNet formulates RGB imagery as information-dense and semantics-rich, whereas DSM contributes complementary but structurally sparse elevation cues. On that view, equal-capacity dual encoders are inefficient and potentially suboptimal, because the two branches do not require the same representational depth and do not play the same semantic role (Ye et al., 22 Jul 2025).

A fourth source is availability and reliability asymmetry. A2MAML addresses multi-agent settings in which each agent may observe only a subset $\mathcal{M}_i \subseteq \mathcal{M}$ and where corruption is agent- and modality-specific. Asymmetry here is not restricted to “text versus image”; it includes uneven sensor suites, missing modalities, and per-agent per-modality uncertainty (Liu et al., 4 Feb 2026).

A broader, role-centered version also appears outside classical multimodal fusion. AVSS for many-class few-shot learning uses different encoding precision for query vectors and stored support vectors, preserving high precision on the support side while collapsing the query side to code word length $1$. This is role asymmetry rather than semantic cross-modality, but it instantiates the same principle that the two sides of an interaction need not share the same representation budget (Chiang et al., 2024).

2. Structural forms of asymmetric encoding

One structural form is output-conditioned pathway selection. In “Traits Run Deeper,” each trait $k$ receives its own modality subset $\mathcal{S}_k \subseteq \{v,a,t\}$ and fusion function $g_k$, yielding

$$\mathbf{u}_k = g_k(\{\mathbf{z}_m \mid m \in \mathcal{S}_k\}).$$

The same framework explicitly permits unimodal branches, concatenation-based fusion, attention pooling, and a text-centered cross-modal attention mechanism in which text provides the query and non-text modalities supply keys and values (Li et al., 9 Jun 2026).

A second form is unequal encoder capacity with directional fusion. AMMNet’s Asymmetric Dual Encoder uses a deeper encoder for RGB and a lighter encoder for DSM, then projects DSM features upward to the RGB channel dimension with a Channel Matching module. Its Asymmetric Prior Fuser further reserves semantic enhancement for RGB and uses DSM as a structural prior source, while the Distribution Alignment module explicitly aligns DSM latent distributions toward RGB rather than symmetrically in both directions (Ye et al., 22 Jul 2025).

A third form is one-way conditional compression. OmniSIFT compresses video and audio with different operators and in a fixed order: Spatio-Temporal Video Pruning first removes visual redundancy using cosine-distance saliency over two-frame chunks, and Vision-Guided Audio Selector then filters audio tokens conditioned on the retained video tokens. The asymmetry is therefore not just different retention ratios but different inductive biases: video is pruned from internal spatio-temporal redundancy, whereas audio is selected through visually conditioned relevance (Ding et al., 4 Feb 2026).

A fourth form is expert specialization with asymmetric routing. AsyMoE separates expert groups into intra-modality visual experts, evidence-priority language experts, and shared inter-modality experts. Visual routing uses

$$g_V(\mathbf{h}_v)=\text{Softmax}(\mathbf{W}_V \cdot \mathbf{h}_v),$$

whereas language routing adds an evidence-aware bias,

$$g_L(\mathbf{h}_l)=\text{Softmax}(\mathbf{W}_L \cdot \mathbf{h}_l + s_{evd}\cdot \mathbf{m}_{evd}),$$

reflecting the claim that deeper language processing is more vulnerable to context dilution and parametric-memory drift (Zhang et al., 16 Sep 2025).

A fifth form is source-to-target translation under asymmetric supervision. In face PAD, Asymmetric Modality Translation learns a fixed one-way mapping $T_G : x^S \mapsto x'^T$ such that genuine samples are reconstructed toward their paired target modality, while attacks are not trained to match their target-modality images. The translated source image is then fused with the real target image, so the PAD classifier operates on cross-modal consistency for genuine faces and cross-modal discrepancy for attacks (Li et al., 2021).

3. Objectives and optimization principles

A central optimization theme is that asymmetric architectures usually require asymmetric training signals, not only asymmetric topology. AM3 exemplifies this logic at a high level: its asymmetric fusion is coupled with a cross-modality contrastive loss intended to learn unique knowledge that only appears in multimodality, because harmful meaning may be expressed by the joint image-text configuration rather than by either modality alone (Yuan et al., 2023).

ARM makes this principle explicit through mutual-information-based contribution valuation. Its lower-bound joint contribution is defined as

$$\phi^{MI}(\mathcal X) = p(f_{\mathcal Y}\rightarrow y)\min_i I(f_{\mathcal Y};f_{x^i}),$$

and its asymmetric marginal contribution uses conditional mutual information,

$1$0

These quantities drive dynamic fusion weights

$1$1

together with the total loss

$1$2

The objective is not simple balancing: it raises the weakest modality’s lower-bound contribution while also narrowing contribution disparities without discarding dominant modalities (Gao et al., 2 Jan 2025).

A2MAML instead encodes asymmetry through uncertainty-aware selection and Bayesian aggregation. Each agent-modality pair produces a feature and uncertainty,

$1$3

a scalar uncertainty token

$1$4

and a learned accept/reject decision via Gumbel-softmax. Accepted features are then fused by inverse-variance weighting,

$1$5

This yields a double asymmetry: coarse exclusion by $1$6 and fine-grained downweighting by $1$7 (Liu et al., 4 Feb 2026).

AMMNet uses a different optimization logic, combining supervised segmentation with directed distribution matching. Its alignment loss is written as

$1$8

and the total loss is

$1$9

The alignment is explicitly asymmetric: the paper states that it aligns $k$0 to $k$1, again treating RGB as the dominant semantic reference (Ye et al., 22 Jul 2025).

4. Routing, compression, and role asymmetry

Modality-asymmetric encoding frequently appears as budget allocation rather than only feature fusion. OmniSIFT is exemplary: it fixes separate removal ratios $k$2 and $k$3, with retention ratios $k$4 and $k$5, and imposes a two-stage policy in which video is compressed first and audio second. In its ablations, replacing the vision-guided audio selector with an audio-only selector degrades accuracy by $k$6 on DailyOmni and $k$7 on WorldSense, supporting the claim that audio saliency is context-dependent and benefits from visual anchors (Ding et al., 4 Feb 2026).

AVSS extends the concept beyond semantic modalities to query-database asymmetry. In symmetric vector similarity search, search iterations scale as

$k$8

where $k$9 is embedding dimension and $\mathcal{S}_k \subseteq \{v,a,t\}$0 is code word length. AVSS sets the query code word length to $\mathcal{S}_k \subseteq \{v,a,t\}$1, reducing the required iterations to

$\mathcal{S}_k \subseteq \{v,a,t\}$2

The support vectors remain high precision while the query is deliberately coarsened. This broader interpretation suggests that asymmetric encoding can be role-defined even when both representations live in the same embedding space (Chiang et al., 2024).

Directed graph generation pushes the principle into ordered relational structure. Directo uses asymmetric positional encodings such as Magnetic Laplacian,

$\mathcal{S}_k \subseteq \{v,a,t\}$3

and Directed RRWP,

$\mathcal{S}_k \subseteq \{v,a,t\}$4

together with dual attention using separate source and target projections. Its directional attention maps are

$\mathcal{S}_k \subseteq \{v,a,t\}$5

Although not a multimodal model in the conventional sense, it shows that asymmetric encoding is also a way to preserve non-interchangeable relational roles (Carballo-Castro et al., 19 Jun 2025).

The same role logic appears in mechanistic studies of multimodal ICL. In the synthetic testbed of “Dissecting Multimodal In-Context Learning,” a primary modality $\mathcal{S}_k \subseteq \{v,a,t\}$6 first installs the induction-style circuit through unimodal pretraining, and a secondary modality $\mathcal{S}_k \subseteq \{v,a,t\}$7 is later mapped into the decoder’s embedding space via a projector. The reported asymmetry is curriculum-induced and can reverse under early fusion, implying that “primary” and “secondary” need not be ontological modality labels; they can be consequences of training order and sequence geometry (Huang et al., 28 Jan 2026).

5. Empirical patterns across application domains

In personality assessment, trait-specific asymmetry was not merely conceptual. On the AVI Challenge 2026 validation set, the best fixed multimodal setting under simple concatenation achieved average MSE $\mathcal{S}_k \subseteq \{v,a,t\}$8, whereas the trait-specific configuration reduced this to $\mathcal{S}_k \subseteq \{v,a,t\}$9. Under the same asymmetric fusion setting, DCPR further reduced average five-fold MSE from $g_k$0 with raw labels to $g_k$1 with calibrated labels, and the reported official test-set MSE was $g_k$2, ranking first in the challenge leaderboard (Li et al., 9 Jun 2026).

In remote sensing segmentation, AMMNet’s encoder asymmetry is directly validated by encoder-pair ablation. On Vaihingen, RGB Base + DSM Small achieved mOA $g_k$3, mF1 $g_k$4, and mIoU $g_k$5, outperforming both RGB Base + DSM Base, which reached mIoU $g_k$6, and RGB Base + DSM Tiny, which reached mIoU $g_k$7. At the whole-model level, AMMNet reported $g_k$8 mIoU with $g_k$9 G FLOPs, $$\mathbf{u}_k = g_k(\{\mathbf{z}_m \mid m \in \mathcal{S}_k\}).$$0 M parameters, and $$\mathbf{u}_k = g_k(\{\mathbf{z}_m \mid m \in \mathcal{S}_k\}).$$1 MB memory, compared with FTransUNet at $$\mathbf{u}_k = g_k(\{\mathbf{z}_m \mid m \in \mathcal{S}_k\}).$$2 mIoU, $$\mathbf{u}_k = g_k(\{\mathbf{z}_m \mid m \in \mathcal{S}_k\}).$$3 G FLOPs, $$\mathbf{u}_k = g_k(\{\mathbf{z}_m \mid m \in \mathcal{S}_k\}).$$4 M parameters, and $$\mathbf{u}_k = g_k(\{\mathbf{z}_m \mid m \in \mathcal{S}_k\}).$$5 MB (Ye et al., 22 Jul 2025).

In collaborative accident detection, A2MAML reported strong gains precisely in regimes with uneven modality quality. With corruption probability $$\mathbf{u}_k = g_k(\{\mathbf{z}_m \mid m \in \mathcal{S}_k\}).$$6, it improved ADR over the single-agent baseline by $$\mathbf{u}_k = g_k(\{\mathbf{z}_m \mid m \in \mathcal{S}_k\}).$$7, $$\mathbf{u}_k = g_k(\{\mathbf{z}_m \mid m \in \mathcal{S}_k\}).$$8, and $$\mathbf{u}_k = g_k(\{\mathbf{z}_m \mid m \in \mathcal{S}_k\}).$$9 in overtaking, left turn, and red-light violation scenarios. Its ablations further showed that removing both active selection and Bayesian fusion caused drops of up to $$g_V(\mathbf{h}_v)=\text{Softmax}(\mathbf{W}_V \cdot \mathbf{h}_v),$$0 ADR and $$g_V(\mathbf{h}_v)=\text{Softmax}(\mathbf{W}_V \cdot \mathbf{h}_v),$$1 EIR, indicating that asymmetric modality-level selection and uncertainty-aware aggregation are complementary rather than interchangeable (Liu et al., 4 Feb 2026).

In omni-modal LLM compression, OmniSIFT shows that asymmetry can improve both compute and accuracy. For Qwen2.5-Omni-7B on WorldSense at $$g_V(\mathbf{h}_v)=\text{Softmax}(\mathbf{W}_V \cdot \mathbf{h}_v),$$2 retention, the full-token model used $$g_V(\mathbf{h}_v)=\text{Softmax}(\mathbf{W}_V \cdot \mathbf{h}_v),$$3 GB GPU memory, $$g_V(\mathbf{h}_v)=\text{Softmax}(\mathbf{W}_V \cdot \mathbf{h}_v),$$4 s end-to-end latency, and obtained accuracy $$g_V(\mathbf{h}_v)=\text{Softmax}(\mathbf{W}_V \cdot \mathbf{h}_v),$$5, whereas OmniSIFT used $$g_V(\mathbf{h}_v)=\text{Softmax}(\mathbf{W}_V \cdot \mathbf{h}_v),$$6 GB, $$g_V(\mathbf{h}_v)=\text{Softmax}(\mathbf{W}_V \cdot \mathbf{h}_v),$$7 s, and achieved $$g_V(\mathbf{h}_v)=\text{Softmax}(\mathbf{W}_V \cdot \mathbf{h}_v),$$8. At $$g_V(\mathbf{h}_v)=\text{Softmax}(\mathbf{W}_V \cdot \mathbf{h}_v),$$9 retention, it added only $$g_L(\mathbf{h}_l)=\text{Softmax}(\mathbf{W}_L \cdot \mathbf{h}_l + s_{evd}\cdot \mathbf{m}_{evd}),$$0 M parameters and, on several benchmarks, matched or exceeded full-token performance while using substantially fewer FLOPs (Ding et al., 4 Feb 2026).

In industrial e-commerce retrieval, SMAR formalizes modality asymmetry as text-only query versus multimodal item. On the overall dataset it achieved R@50 $$g_L(\mathbf{h}_l)=\text{Softmax}(\mathbf{W}_L \cdot \mathbf{h}_l + s_{evd}\cdot \mathbf{m}_{evd}),$$1, P@50 $$g_L(\mathbf{h}_l)=\text{Softmax}(\mathbf{W}_L \cdot \mathbf{h}_l + s_{evd}\cdot \mathbf{m}_{evd}),$$2, and F1@50 $$g_L(\mathbf{h}_l)=\text{Softmax}(\mathbf{W}_L \cdot \mathbf{h}_l + s_{evd}\cdot \mathbf{m}_{evd}),$$3, compared with DPSR at R@50 $$g_L(\mathbf{h}_l)=\text{Softmax}(\mathbf{W}_L \cdot \mathbf{h}_l + s_{evd}\cdot \mathbf{m}_{evd}),$$4, P@50 $$g_L(\mathbf{h}_l)=\text{Softmax}(\mathbf{W}_L \cdot \mathbf{h}_l + s_{evd}\cdot \mathbf{m}_{evd}),$$5, and F1@50 $$g_L(\mathbf{h}_l)=\text{Softmax}(\mathbf{W}_L \cdot \mathbf{h}_l + s_{evd}\cdot \mathbf{m}_{evd}),$$6. Online, the reported A/B test on $$g_L(\mathbf{h}_l)=\text{Softmax}(\mathbf{W}_L \cdot \mathbf{h}_l + s_{evd}\cdot \mathbf{m}_{evd}),$$7 of traffic yielded GMV $$g_L(\mathbf{h}_l)=\text{Softmax}(\mathbf{W}_L \cdot \mathbf{h}_l + s_{evd}\cdot \mathbf{m}_{evd}),$$8 and UCVR $$g_L(\mathbf{h}_l)=\text{Softmax}(\mathbf{W}_L \cdot \mathbf{h}_l + s_{evd}\cdot \mathbf{m}_{evd}),$$9, with larger gains in fashion categories (Zhou et al., 25 Jun 2025).

Large vision-language MoE models show a similar pattern. AsyMoE reports accuracy improvements of $T_G : x^S \mapsto x'^T$0 over vanilla MoE and $T_G : x^S \mapsto x'^T$1 over modality-specific MoE, while using $T_G : x^S \mapsto x'^T$2 fewer activated parameters than dense models. Its ablations indicate that removing evidence-priority experts, hyperbolic inter-modality experts, or intra-modality separation each reduces average benchmark performance, implying that the asymmetry is distributed across routing, geometry, and expert specialization rather than localized to one component (Zhang et al., 16 Sep 2025).

6. Boundaries, misconceptions, and open questions

A common misconception is that modality-asymmetric encoding always means hard-coding one modality as globally dominant. The literature does not support so narrow a definition. In some systems the asymmetry is fixed by sensing physics or representation density, as in RGB–DSM segmentation; in others it is output-dependent, as in trait-specific fusion; in others it is uncertainty-dependent, as in A2MAML; and in mechanistic multimodal ICL, the primary modality is a consequence of pretraining order and sequence geometry, not an intrinsic property of the modality itself. The same study also reports that RoPE increases the data complexity threshold for ICL, which further implies that modality asymmetry can depend on architectural biases as much as on data semantics (Huang et al., 28 Jan 2026).

Another misconception is that asymmetry merely compensates for missing information in “weak” modalities. Some results suggest a subtler picture. In visio-linguistic brain encoding, VisualBERT outperformed image-only and text-only alternatives on BOLD5000 and Pereira, and the gains were stronger in higher-order visual regions than in EarlyVis. This suggests that an auxiliary modality can improve encoding even when the experimental stimulus is nominally visual, so the optimal representational space need not match the task’s apparent primary modality (Oota et al., 2022).

Current formulations also differ in granularity. “Traits Run Deeper” is asymmetric at the trait level rather than the instance level, since modality subsets and fusion operators are selected by validation-based model selection for each trait, and the paper explicitly lists instance-level adaptation as future work (Li et al., 9 Jun 2026). This suggests that some present systems implement coarse asymmetric routing rather than fully dynamic conditional computation.

Several limitations recur across the literature. AMMNet frames modality misalignment as a core challenge, but does not provide a dedicated robustness benchmark against explicit geometric misregistration; ARM motivates dynamic dominance and reports gains under multimodal imbalance, yet does not include a formal missing-modality or OOD corruption benchmark; and AsyMoE offers explicit routing equations and hyperbolic order constraints, but omits a fully specified global training objective and a complete hyperbolic parameterization. These limitations indicate that the concept is empirically productive but not yet unified at the level of theory, benchmark design, or formal implementation standards (Ye et al., 22 Jul 2025).

Taken together, the field suggests several persistent open questions. One concerns where asymmetry should live: encoder depth, fusion, routing, loss design, or sampling policy. Another concerns when asymmetry should be static, task-conditioned, or instance-conditioned. A third concerns how to distinguish beneficial asymmetric specialization from hidden capacity inflation or dataset-specific bias. The strongest current evidence favors a restrained conclusion: when modalities or roles are semantically unequal, statistically heterogeneous, or differently reliable, explicitly asymmetric encoding is often more faithful than symmetric homogenization; but the most effective form of that asymmetry remains domain-specific and only partially theorized.