Visual Room Argument in Multimodal AI

• Visual Room Argument is a framework demonstrating that multimodal models can accurately describe visual elements but fail to grasp deeper context, emotion, or intent.
• Hierarchical benchmarks decouple perception from cognition, revealing consistent gaps—such as an average 8-16% drop in cognition accuracy compared to visual recognition.
• Empirical findings stress that achieving high visual accuracy does not ensure cognitive success, prompting calls for advanced symbolic and affective reasoning in next-gen MLLMs.

The Visual Room argument formalizes a contemporary extension of Searle’s Chinese Room, positing that multi-modal large models (MLLMs/MLMs) can process and describe every visible aspect of an image or video yet still lack genuine comprehension of its underlying context, intention, or affect. Evidence supporting this claim is provided by hierarchical benchmarks that systematically decouple perception from cognition, demonstrating a persistent empirical gap between surface-level visual mastery and deeper understanding across tasks such as emotion recognition, sarcasm detection, and causal reasoning (Li et al., 17 Nov 2025, Zhang et al., 29 May 2025).

1. Conceptual Basis and Formalization

The Visual Room argument adapts Searle’s insight—that manipulating symbols does not equate to understanding—into the multi-modal domain. In this setting, a model is said to be in the "Visual Room" if it can enumerate and label all visual entities and attributes with high accuracy but fails to answer questions about deeper meaning, emotion, or intention (“seeing ≠ understanding”) (Li et al., 17 Nov 2025, Zhang et al., 29 May 2025).

Given a multi-modal input $x = (I, T)$ (image or video $I$, text context $T$), the model outputs perceptual content $\hat{y}_P = f_P(x)$ (e.g., “the dog is smiling”) and cognitive judgments $\hat{y}_C = f_C(x)$ (e.g., “the dog is happy because …” or “the statement is sarcastic”). Ground-truths $y_P, y_C$ are human-annotated for respective facets. Key metrics are:

$\text{Acc}_P = \Pr(\hat{y}_P = y_P)$

$$\text{Acc}_C = \Pr(\hat{y}_C = y_C \mid \hat{y}_P = y_P)$$

$\Delta_{P,C} = \text{Acc}_P - \text{Acc}_C$

An empirically nonzero $\Delta_{P,C}$ operationalizes the gap between visual perception and genuine understanding (Li et al., 17 Nov 2025, Zhang et al., 29 May 2025).

2. Evaluation Frameworks and Datasets

Recent work introduces hierarchical evaluation frameworks that explicitly separate perception and cognition. Visual Room 2.0 (PCBench) (Li et al., 17 Nov 2025) structures the evaluation into three levels of both perception and cognition:

Perception (1,050 Qs)	Cognition (1,050 Qs)
Attribute recognition (328)	Textual entailment (103)
Sub-image detection (22)	Text matching (146)
Object detection (202)	Action recognition (101)
OCR (68)	Emotion recognition (159)
Scene classification (80)	Sarcasm detection (108)
Image captioning (196)	Humor understanding (83)
Scene understanding (154)	Commonsense reasoning (65)
	Causal reasoning (72)
	Intention recognition (167)
	Social-relation reasoning (46)

Each of 350 images receives six progressive questions, totaling 2,100 queries, ranging from surface-level perception (attributes, detection, OCR) to high-level cognition (causal, social, and emotional reasoning).

A parallel approach is seen in the MMSar dataset (Zhang et al., 29 May 2025), comprising 924 static image–text pairs and 100 dynamic video–text pairs, each annotated for “scene description” (objective, third-person) and binary sarcasm polarity, supporting robust analysis of the visual-to-cognitive pipeline.

3. Methodologies for Measuring Perception and Cognition

Evaluation of MLLMs is performed under zero-shot standard I/O prompting (Li et al., 17 Nov 2025, Zhang et al., 29 May 2025). Perception accuracy ($\text{Acc}_P$) is computed as the proportion of model responses matching human-annotated scene descriptions or visual facts above specified similarity thresholds (e.g., $\delta=0.75$ or $\delta=0.8$). Cognition accuracy ($\text{Acc}_C$) is measured over the subset of samples for which the model’s perception was deemed fully correct.

For complex outputs such as image captioning or scene descriptions, hybrid semantic similarity metrics combine cosine similarity and LLM-based judgment to determine correctness. Cognition evaluations cover tasks including sarcasm detection, emotional and social reasoning, and commonsense inference, all conditioned on perfect perception (Li et al., 17 Nov 2025, Zhang et al., 29 May 2025).

4. Empirical Findings

a. Perception–Cognition Gap

Experiments with ten SOTA MLLMs (e.g., GPT-5, GPT-4V, GLM-4V-Plus, Gemini 2.5 Pro) confirm a stable gap: mean perception accuracy exceeds cognition accuracy by approximately 8.0 points ($$\Delta_{P,C}^{\mathrm{avg}} = 79.09\% - 71.10\% \approx 8.0\%$$) on image-based PCBench (Li et al., 17 Nov 2025). Conditioning on perfect perception, cognition accuracy remains nontrivial—models still fail in ~28.6% of cognitive cases overall.

On MMSar, image-based MLMs achieve perception accuracies above 80–90% (e.g., GPT-4V at 90.4%), but their cognition accuracy on sarcasm understanding is distinctly lower, with an average gap of ~16.1% across top models (Zhang et al., 29 May 2025). Video-based cognition is substantially weaker, with model gaps exceeding 60%.

b. Absence of Causality

No evidence is found that perfect perceptual ability causally guarantees cognitive success: cognition failure rates persist even when perception is flawless (Li et al., 17 Nov 2025, Zhang et al., 29 May 2025). The joint distribution $P(y, S \mid V, T) = P(y \mid S, V, T) \cdot P(S|V)$ illustrates that maximization of $P(S|V)$ does not entail robust $P(y|S,V,T)$—the core Visual Room challenge.

c. Scaling Trends

Scaling model size yields marked improvement in cognition (Pearson $r=0.81, p<0.01$), while perception saturates quickly. For example, in the Qwen3-VL series (2B–32B), perception accuracy marginally rises (0.80→0.84), but cognition improves by 10 points (0.65→0.75) (Li et al., 17 Nov 2025).

d. Error Analysis

Three primary deficits underpin the perception–cognition gap:

• Emotional reasoning: Difficulty inferring affect or attitude from visuals.
• Pragmatic/commonsense inference: Mapping visual scenes to implied real-world expectations.
• Context integration: Aligning image, text, and broader discourse for nonliteral intent (e.g., sarcasm).

These are evidenced by consistent failures in tasks demanding higher-level social, causal, or affective interpretation (Zhang et al., 29 May 2025).

5. Theoretical and Practical Implications

The Visual Room argument and its operationalizations offer several key implications:

• Decomposition of “understanding”: Perception and cognition are empirically and functionally dissociable in current MLLM architectures, necessitating separate evaluation and architectural consideration (Li et al., 17 Nov 2025).
• Architectural bottlenecks: Saturated perceptual modules contrast with cognitive reasoning, which benefits from scale and likely richer cross-modal or symbolic integration.
• Benchmarking standards: Benchmarks must incorporate hierarchical tasks, conditional cognition metrics, and report perception–cognition gaps. Claims of “understanding” require multi-faceted validation.
• Research directions: Approaches beyond scaling vision encoders—such as deeper symbolic or causal reasoning layers, emotion-aware modules, and pragmatics integration—are implicated as necessary for closing the gap (Li et al., 17 Nov 2025, Zhang et al., 29 May 2025).

A plausible implication is that future MLLM architectures may adopt hybrid neural-symbolic systems or dedicated affective and pragmatic subsystems to address these deficits, given that current purely connectionist scaling does not yield proportional cognitive gains.

6. Relation to Historical and Contemporary Discourse

The Visual Room argument reinvigorates philosophical discussion (cf. Searle) in empirical terms, grounding the debate in measurable model behaviors. By providing datasets and metrics that explicitly disentangle “seeing” from “understanding,” it challenges the field to move beyond surface-level evaluation and towards operational definitions of multi-modal intelligence (Li et al., 17 Nov 2025, Zhang et al., 29 May 2025).

Persistent perception–cognition gaps suggest that superficial visual fluency in MLLMs or MLMs is not a sufficient proxy for semantic, pragmatic, or social understanding—a result with broad ramifications for both AI evaluation and cognitive modeling.