Modality Reasoning Gap

• Modality reasoning gap is defined as the discrepancy in integrating heterogeneous inputs, resulting in degraded model performance across modalities.
• It is measured by differences in representation alignment and inference accuracy, with concrete metrics highlighting failures in models like CLIP and MLLMs.
• Remediation strategies include augmented loss functions, fusion-aware reasoning, and structured representation learning to bridge the gap.

The "modality reasoning gap" refers to the systematic failure of computational models—spanning from natural language processing systems to modern multimodal LLMs—to integrate, fuse, or infer correctly across heterogeneous input modalities (e.g., text, speech, vision, audio) or modal linguistic constructs (e.g., epistemic, deontic, dynamic). This term encompasses both surface-level representation alignment deficits (e.g., in vision-language embedding spaces) and failures at the level of integrated modal logic reasoning. The gap manifests as degraded accuracy, lack of interpretability, illogical inference chains, or vulnerability to adversarial input when compared to unimodal reasoning or human cognitive performance. Quantitative metrics for this gap vary, but always encode a discrepancy between expected or unimodal performance and the multimodal or modal-inference case.

1. Formal Definitions and Taxonomy

Modality reasoning gaps must be precisely defined according to context:

• Linguistic modality reasoning gap: In natural language, modality marks the speaker's attitude toward a proposition (possibility, necessity, obligation, etc.). The gap is seen when models, despite recognizing modal words, cannot robustly extract modal sense, contextual inference, or perform nontrivial reasoning (e.g., modal implications, uncertainty propagation) (Shukla, 2015).
• Representational modality gap: In multi-modal contrastive learning (e.g., CLIP), the modality gap is a geometric separation of embedding centers between modalities:

$$\Delta_{\mathrm{CD}} = \left\|\frac{1}{n}\sum_{i} x_i - \frac{1}{n}\sum_{i} y_i\right\|_2$$

where $\{x_i\}$ and $\{y_i\}$ are image and text embeddings, respectively (An et al., 2024, Liang et al., 2022).

• Cross-modal fusion/logic gap: In MLLMs, the modality reasoning gap is the decrement in reasoning accuracy or coherence when facts/queries are distributed across modalities rather than presented in a single one (Wang et al., 28 Sep 2025, Jiang et al., 14 Dec 2025).
• Speech/text reasoning gap: For models that accept both modalities, the gap is typically measured as

$$\text{MRR}(\pi_\theta) = \frac{E_{q \sim D}[S(y_{\text{speech}})]}{E_{q \sim D}[S(y_{\text{text}})]} \times 100\%$$

where $S(\cdot)$ is a task metric (e.g., accuracy), and MRR near 100% implies no gap (Wang et al., 9 Jan 2026).

Gaps can also be defined in terms of error types (e.g., consistency loss, hallucination rates, logical contradictions) and are often stratified by domain (linguistic, vision-language, audio captioning, etc.).

2. Manifestations Across Systems and Benchmarks

Empirical studies reveal the modality reasoning gap in a range of architectures and tasks:

• NLP/Modal logic: Taggers capture modal triggers but not sense; annotated classifiers lack generalization; rule-based parsers fail to perform context-sensitive modal inference (e.g., probability scoring for "perhaps," actual modal entailments across conditionals) (Shukla, 2015, Holliday et al., 2024).
• Vision-LLMs: In contrastive two-tower models, CLIP’s image and text encoders populate distinct cones in embedding space, with large centroid distances (e.g., $\Delta_{\mathrm{CD}} > 0.7$). This segmentation prohibits calibration of similarity scores across modalities and impedes accurate zero-shot cross-modal retrieval (An et al., 2024, Yamashita et al., 27 Nov 2025, Fahim et al., 2024).
• Continual Learning: CLIP-based class-incremental learning shows that deviation from the original modality gap correlates with catastrophic forgetting; retention is maximized by actively preserving the pre-trained modality gap through gap-aware early stopping and classifier ensembling (Huang et al., 12 Jul 2025).
• Audio/LLM Integration: In LLM-driven audio captioning, unaligned acoustic-text embeddings reduce caption quality and restrict reasoning, remediated by cross-modal alignment via symmetric Cauchy–Schwarz divergence and mutual information maximization (Lee et al., 8 Jan 2026).
• Complex Reasoning Tasks: MLLMs degrade substantially when critical information is spread across modalities; e.g., in FysicsWorld, top models display a $>20$ percentage point drop (from uni-modal VQA to video+audio contextual reasoning) (Jiang et al., 14 Dec 2025), and in MMLU-Reason, a nontrivial gap exists between answer accuracy and the coherence or consistency of cross-modal chain-of-thought traces (Tie et al., 22 May 2025).

3. Underlying Causes and Theoretical Analysis

The modality reasoning gap arises from multiple, often interacting, sources:

• Geometry and Initialization: Randomly initialized deep encoders naturally produce “narrow cones” in embedding space for each modality, and training with standard contrastive losses preserves a repulsive separation (the gap) (Liang et al., 2022, Fahim et al., 2024).
• Loss Function Limitations: Standard contrastive objectives with many negatives and low temperature explicitly encourage separation between image and text manifolds; lack of within-modality uniformity exacerbates this (Fahim et al., 2024).
• Fusion Bottlenecks: MLLMs preserve modality identity deep into the attention stack; relevant features are not fused or weighted for cross-modal salience, so composition and joint reasoning fail (Wang et al., 28 Sep 2025).
• Cascade Effects in Sequence Tasks: In sequence generation (e.g., speech translation), small initial differences in representations between speech and text inputs compound over time, leading to an increasing gap during inference—mirroring exposure bias (Fang et al., 2023).
• Unimodal Bias and Preference: When conflicting cues are presented in different modalities, models systematically follow the modality with lower predicted entropy, modulated by a stable balance point that reflects learned bias (Zhang et al., 4 Nov 2025).
• Reasoning Mode Vulnerability: Systems that pursue depth-first (System II) chain-of-thought reasoning are particularly vulnerable to hallucination or mirage effects when input modalities are noisy, incomplete, or adversarial (Ji et al., 26 May 2025).

4. Quantitative Evaluation and Diagnostic Methodologies

A shared feature of modality reasoning gap scholarship is the development of precise diagnostics:

Gap Type	Formalization	Typical Metrics
Embedding (CLIP)	$\|\bar{x} - \bar{y}\|_2$	$\Delta_{\mathrm{CD}}$, linear separability
Retrieval/QA	Score normalization, Recall@k	Recall@20, MRR@20
Cross-modal Reason	$P_{\text{uni}} - P_{\text{cross}}$	ACC, BERTScore, OS
Consistency	Step-wise trace quality, entropy difference	RTQ, RTA, RSC, $\Delta H$
Chain-of-Thought	Consistency, pathologies (inconsistency, repetition)	Overall Score (OS)
Speech/Text	Modality Recovery Rate (MRR)	Audio/Text ACC, MRR
Abstract Reasoning	Output Accuracy, Rule-Capture Delta	$\Delta_{\text{acc}}$, $\Delta_{\text{rule}}$

Notable frameworks and benchmarks aimed at quantifying the gap include MMLU-Reason for reasoning trace quality (Tie et al., 22 May 2025), FysicsWorld for any-to-any multimodal evaluation with CMCS ablation (Jiang et al., 14 Dec 2025), and ConceptARC for rule-level abstraction gap (Beger et al., 2 Oct 2025). Controlled fusion-dependence and entropy-based preference metrics reveal latent bias and internal decision switching (Zhang et al., 4 Nov 2025, Jiang et al., 14 Dec 2025).

5. Remediation Strategies and Architectural Innovations

Addressing the modality reasoning gap encompasses both algorithmic and architectural methods:

• Post-hoc and Trainable Standardization: Centering and normalization (e.g., I0T_post, I0T_async) collapse embedding centroids and reduce separability, aligning cross-modal distributions without retraining base encoders (An et al., 2024).
• Augmented Losses: Uniformity and alignment terms (e.g., $L_{\text{uniform}}$, $L_{\text{align}}$, $L_{\text{XUniform}}$) supplement contrastive loss to foster more isotropic, interleaved spaces (Fahim et al., 2024).
• Gap-Preserving Continual Training: Adaptive early stopping (MG-CLIP) or classifier ensembling (visual+text) stabilize transfer and retention in incremental tasks (Huang et al., 12 Jul 2025).
• Representation and Behavior Alignment: RL strategies with asymmetric reward and group-based relative normalization (TARS) enforce layer-wise hidden-state and output alignment between speech and text conditionings, nearly closing the reasoning gap (Wang et al., 9 Jan 2026).
• Explicit Modality Conversion: Training models to convert images or audio into symbolic/textual representations before reasoning improves hard generalization, as does chain-of-thought prompting (Park et al., 5 Jan 2025).
• Fusion-Aware Reasoning and Prompting: Two-step prompting (separate fact recognition and reasoning), softening of early attention to avoid premature modality bias, and composition-aware loss functions all yield measurable improvements in cross-modal tasks (Wang et al., 28 Sep 2025).
• Structured and Causal Representation Learning: Joint graphs, object-action-sound world models, and explicit causal links address fusion bottlenecks in highly complementary cross-modal reasoning (Jiang et al., 14 Dec 2025).

6. Open Challenges and Future Directions

Persistent modality reasoning gaps reflect enduring challenges in cognitive alignment, dataspace stratification, and integration complexity. Dominant unresolved areas include:

• Complex fusion-demanding reasoning: Tasks with high interdependence (e.g., video+audio+text action prediction) continue to exhibit $>20$ percentage point gaps in top models (Jiang et al., 14 Dec 2025).
• Modal logic and epistemic inference: Even frontier LLMs violate possible-world semantics, overgeneralize Boolean rules, and lack global consistency in modal-conditional inference (Holliday et al., 2024).
• Explainability and Abstraction: High answer accuracy does not guarantee human-like abstraction; models exploit surface-level shortcuts, especially in textual settings (Beger et al., 2 Oct 2025).
• Calibration and Hallucination: System II models are especially prone to over-commit on misleading inputs, underscoring the need for regularization (e.g., honesty losses) and branch-hedging mechanisms (Ji et al., 26 May 2025).
• Broadening Evaluation: New benchmarks targeting dense layouts, temporal reasoning, and multi-party multimodal QA are essential to track progress and highlight integration bottlenecks (Yan et al., 22 Feb 2025, Jiang et al., 14 Dec 2025).
• Neurosymbolic and white-box architectures: Multi-layered, interpretable modules tracing from lexical triggers to world model updates—mirroring human cognition—are increasingly advocated for closing deep reasoning gaps (Shukla, 2015).

Ensuing research aims to blend white-box interpretability, explicit fusion and composition objectives, and scalable cross-modal curriculum learning to systematically dissolve the modality reasoning gap and approach modality-invariant, human-aligned reasoning.