Breakthrough Research Exposes the Myth of AI 'Imagination' in Multimodal Models

In a groundbreaking preprint published on arXiv on February 27, 2026 (arXiv:2602.22766), researchers from Tsinghua University and Beijing Jiaotong University's School of Computer and Information Technology have delivered a sobering analysis of so-called latent visual reasoning (LVR) in multimodal large language models (MLLMs). These models, which integrate vision and language processing, have been touted for their ability to 'imagine' intermediate visual representations to tackle complex visual reasoning tasks. However, the study reveals this 'imagination' is little more than an illusion—a superficial facade that fails to contribute meaningfully to reasoning.

The collaborative effort challenges the hype surrounding LVR, a technique where MLLMs generate hidden 'latent tokens' as an internal step mimicking human mental imagery. Tested on prominent systems like Monet, LVR, and Mirage, the findings indicate these tokens are highly uniform, interchangeable, and devoid of substantive visual content, prompting a reevaluation of how we design and trust AI reasoning pathways.

Understanding Latent Visual Reasoning: The Promise and the Hype

Latent visual reasoning emerged as a promising advancement in MLLMs, aiming to replicate human-like cognition. When confronted with a visual query requiring multi-step analysis—such as identifying spatial relationships in an image—the model generates a sequence of latent tokens. These numerical vectors, produced in a high-dimensional 'latent space,' are hypothesized to encode imagined visual intermediates, bridging input images and textual outputs.

In practice, LVR operates like a three-stage pipeline: visual inputs and queries feed into latent token generation, which then informs the final verbal response. Proponents argued this internal 'imagination' enables sophisticated feats, such as mentally zooming into image regions or simulating geometric transformations. Systems like LLaVA and Qwen-VL have popularized this approach, fueling excitement in AI circles for more human-like visual intelligence.

Yet, as the Tsinghua-BJTU team notes, prior validations relied on correlational evidence, leaving causal roles unproven. Their work employs causal mediation analysis—a rigorous statistical framework akin to dissecting neural circuits—to test whether these tokens truly mediate reasoning.

Causal Mediation Analysis: Peering Inside the AI Black Box

The study's methodology centers on causal mediation analysis, borrowed from econometrics and neuroscience, to disentangle direct and indirect effects in the LVR pipeline. By intervening on latent tokens—observing how alterations propagate to outputs—the researchers quantify their true influence.

Key experiments included:

• Similarity Clustering: Computing cosine similarities across 100 diverse visual queries on datasets like HR-VQA and ScienceQA, revealing >90% uniformity in tokens.
• Token Interventions: Replacing tokens with constants, noise, zeros, or random values; performance drops were minimal (<1% on average).
• Probing Tasks: Repurposing tokens for 30 derivative questions per image; accuracy fell below random chance (e.g., 20% vs. 76% with full vision).

These interventions spanned models and tasks, confirming robustness. The analysis exposed LVR's latent tokens as non-causal placeholders, not genuine imaginative constructs.

Key Finding 1: Uniformity – AI 'Imagines' the Same Thing Every Time

Across vastly different inputs—from geographic maps to mechanical diagrams—the latent tokens clustered tightly, with similarities escalating from early (stage 1) to late (stage 4) reasoning phases. This convergence suggests collapse into a generic representation, independent of content.

In human cognition, imagination adapts dynamically; here, AI reverts to a stereotypical pattern, undermining claims of flexible visual simulation.

Key Finding 2: Interchangeability – Swap Tokens, Same Results

Radical manipulations—zeroing tokens, adding Gaussian noise, or substituting unrelated vectors—yielded negligible performance degradation on benchmarks like HR-Bench-8K (average drop <0.5%). Paradoxically, some interventions boosted scores slightly, hinting at regularization effects.

Only pathological cases (e.g., extreme underflow triggering loops) impacted outputs, affirming tokens' ornamental role.

Key Finding 3: Emptiness – No Encoded Visual Knowledge

Probing experiments repurposed tokens for novel queries on the same images, yielding dismal results (accuracy ~random). Linear classifiers trained on tokens failed to predict visual attributes, contrasting sharply with full-model performance.

This voids the premise that latent tokens capture 'imagined' visuals, positioning LVR as a correlative artifact rather than mechanistic driver.

CapImagine: Proving Textual Reasoning's Superiority

Rejecting latent illusions, the team introduced CapImagine: caption-based imagination via explicit textual descriptions of visual operations (e.g., 'Draw a red line connecting Chile to Greenland'). Trained on 17k curated samples from existing LVR data, it bypasses black-box latents.

CapImagine surged ahead: +4.0% on HR-Bench-8K, +4.9% on MME-RealWorld-Lite, +10%+ on spatial puzzles. Causal tests confirmed text tokens' pivotal mediation—interventions reliably altered outputs.

Inference efficiency matched LVR while doubling tool-based rivals' speed.

Why Textual Descriptions Trump Latent Vectors

• Semantic Clarity: Text leverages MLLMs' linguistic strengths for structured reasoning.
• Human Alignment: Mirrors verbalized thought processes.
• Trainability: Precise supervision yields robust patterns vs. latent ambiguity.
• Interpretability: Transparent for debugging and trust.

The study posits LVR forces immature latent capabilities, squandering textual prowess.

Implications for AI Research and Model Design

This illusion shatters LVR optimism, urging causal scrutiny over benchmarks. Developers should prioritize interpretable modules; educators, teach critical AI evaluation.

For higher education, it spotlights multimodal AI curricula needs. Explore crafting AI-savvy CVs amid evolving paradigms.

Tsinghua and BJTU: Pillars of China's AI Innovation Ecosystem

Tsinghua, with 4,986 AI patents (2005-2024), outpaces MIT+Stanford+Harvard combined. BJTU excels in intelligent systems. Their synergy advances global frontiers, fostering talent via higher ed jobs in AI.

Future Directions: Toward Genuine AI Cognition

CapImagine charts a transparent path, but challenges persist: scaling text for pixel-precision, hybrid latent-text fusion. Broader causal toolkits could demystify other 'reasoning' claims.

In China’s universities, this fuels AI education reforms, preparing students via professor reviews.

Photo by Bangyu Wang on Unsplash

Stakeholder Perspectives and Broader Impacts

Lead researchers emphasize verification: 'Surface success masks mechanistic flaws,' notes a team member. Implications span reliability in healthcare diagnostics to autonomous systems.

For academics, rate professors pioneering such work at Rate My Professor. Job seekers, browse faculty positions.