Envision: Benchmarking Unified Understanding & Generation for Causal World Process Insights (2512.01816v1)

Abstract: Current multimodal models aim to transcend the limitations of single-modality representations by unifying understanding and generation, often using text-to-image (T2I) tasks to calibrate semantic consistency. However, their reliance on static, single-image generation in training and evaluation leads to overfitting to static pattern matching and semantic fusion, while fundamentally hindering their ability to model dynamic processes that unfold over time. To address these constraints, we propose Envision-a causal event progression benchmark for chained text-to-multi-image generation. Grounded in world knowledge and structured by spatiotemporal causality, it reorganizes existing evaluation dimensions and includes 1,000 four-stage prompts spanning six scientific and humanities domains. To transition evaluation from single images to sequential frames and assess whether models truly internalize world knowledge while adhering to causal-temporal constraints, we introduce Envision-Score, a holistic metric integrating multi-dimensional consistency, physicality, and aesthetics. Comprehensive evaluation of 15 models (10 specialized T2I models, 5 unified models) uncovers: specialized T2I models demonstrate proficiency in aesthetic rendering yet lack intrinsic world knowledge. Unified multimodal models bridge this gap, consistently outperforming specialized counterparts in causal narrative coherence. However, even these unified architectures remain subordinate to closed-source models and struggle to overcome the core challenge of spatiotemporal consistency. This demonstrates that a focus on causally-isolated single images impedes multi-frame reasoning and generation, promoting static pattern matching over dynamic world modeling-ultimately limiting world knowledge internalization, generation.

• The paper introduces the Envision benchmark, a novel evaluation suite for causal event progression using chained text-to-multi-image generation.
• It employs a comprehensive four-stage schema to assess consistency, physicality, and aesthetics, revealing significant gaps in spatiotemporal reasoning of current models.
• Comparative experiments show closed-source models outperform open-source ones but still struggle with maintaining continuous causal coherence in dynamic simulations.

Envision: Benchmarking Unified Understanding & Generation for Causal World Process Insights

Motivation and Theoretical Foundation

The paper "Envision: Benchmarking Unified Understanding & Generation for Causal World Process Insights" (2512.01816) systematically critiques existing paradigms in text-to-image (T2I) and unified multimodal model (UMM) evaluation, demonstrating their incapacity for causal world modeling due to reliance on static single-image tasks. Whereas prevailing benchmarks in multimodal generation emphasize semantic fidelity and aesthetic rendering, they obscure fundamental deficits in dynamic event modeling, spatiotemporal reasoning, and internalization of world knowledge beyond pattern matching.

To enable rigorous analysis of these limitations, the authors introduce the Envision benchmark—a causal event progression evaluation suite that operationalizes chained text-to-multi-image (T2MI) generation as a direct probe of world process internalization and dynamic causal reasoning. Envision formalizes the four-stage event sequence schema, aligning with cognitive and narrative theories of event segmentation, and comprehensively stratifies prompts into natural sciences and humanities, each with explicit causal structure (continuous or discrete).

Figure 1: The idealized progression from T2I to multi-image and video generation highlights increasing complexity in requirements for causal sequence modeling.

Multi-Image Framework and Event Sequence Construction

The Envision multi-image framework transcends static pattern synthesis by establishing a format where visual narrative construction depends on causal, spatiotemporal progression across four distinct stages: Initial State, Early Interaction, Progressive Transformation, and Final Resolution. This protocol compels models to maintain entity consistency, uphold physical laws, and reason over both continuous and discontinuous temporal evolutions.

Figure 2: Overview of Envision's Multi-Image Framework, illustrating the protocol for constructing multi-image event sequences under diverse spatiotemporal constraints.

By leveraging prompt templates and exhaustive domain coverage—physics, chemistry, biology, meteorology, geography, and culture/history—the dataset supports fine-grained evaluation of knowledge internalization and narrative competency. Each sequence is meticulously annotated and generated under expert supervision, ensuring that event chains are non-trivial and explicitly probe causal modeling capability.

Figure 3: Representative cases span six disciplines, explicitly cataloging event processes as discrete or continuous over time.

Evaluation Methodology: Envision-Score and Diagnostic Protocol

Envision's evaluation suite is anchored by Envision-Score, a holistic composite metric partitioned into three primary dimensions: Consistency, Physicality, and Aesthetics—weighted 0.4, 0.4, and 0.2, respectively. Each dimension is decomposed into specific sub-metrics, systematically scored on a 0–5 scale using a robust multi-trial protocol with GPT-4o as the scorer. Consistency aggregates semantic, spatiotemporal, and factual sub-scores; Physicality enforces basic properties, dynamic interaction, and physical reliability; Aesthetics rates expressiveness, quality, and authenticity.

Figure 4: Event pipeline demonstrates multi-domain knowledge sourcing, prompt generation, sequence synthesis, and granular evaluation via automated scoring and human expert validation.

Scoring reliability is substantiated by empirical analysis: inter-trial variance is low, alignment with domain experts is strong, and the metric discriminates failure modes in temporal reasoning, causality, and world model induction.

Figure 5: Data domain coverage and evaluation dimensions for Envision, affirming a multidimensional assessment of world knowledge and generative fidelity.

(Figures 10, 11, 12)

Figure 6: Scoring for Consistency detects narrative and factual incoherence.

Figure 7: Physicality criteria assess physical law adherence and process plausibility.

Figure 8: Aesthetics benchmarks visual expressiveness and narrative quality.

Comparative Model Evaluation and Quantitative Results

A comprehensive experiment involving fifteen models reveals strong categorical separation:

• Closed-Source T2I Models (e.g., GPT-4o, Gemini-2.5-Flash-Image): Consistently dominate across all axes, with Envision-Scores 73.81/67.18, and peak dimension scores (Consistency up to 73.88, Physicality up to 72.28, Aesthetics up to 76.70), outperforming both open-source T2I and UMMs.
• Open-Source T2I Models (e.g., FLUX, Stable Diffusion 3.5): Specialized proficiency in static fidelity and artistic quality (artistic scores up to 76.73), but exhibit pronounced deficits in physical reliability and factual consistency, undermining world model formation.
• Unified Multimodal Models (e.g., Qwen-Image, Seedream 4.0, Bagel): Achieve significant gains in knowledge-intensive domains (Biology up to 76.92), better causal consistency than open-source T2I, but remain consistently inferior to closed-source models in global spatiotemporal reasoning.

  Figure 9: Average model scores across key dimensions (Consistency, Physicality, Aesthetics), categorically segmented by model class.

Fine-grained analyses per domain (Physics, Chemistry, Biology, Geography, Meteorology, Culture/History) confirm these trends, with UMMs particularly excelling in domains with rich causal structure, but universally bottlenecked on spatial-temporal coherence.

Key Findings: Foundational Deficits and the Understanding-Generation Paradox

Two central deficits emerge:

1. Failure in Dynamic Event Modeling: Even state-of-the-art models cannot maintain coherent spatiotemporal causality over event sequences. Most notably, continuous event chains expose breakdowns in physical law induction and memory scaffolding—models do not use generated visual states to guide subsequent evolution, unlike human event cognition.
2. Understanding-Generation Paradox: UMMs fail to reconcile static understanding of world states (optimized on single-image pattern matching) with policy required for continuous generation. Generation neither validates nor interrogates underlying causal logic, producing high visual fluency without causal fidelity in discrete scenarios, and generative collapse in continuous chains.

   Figure 10: Causal event progression and failure analysis: multi-model comparative sequence outputs in discrete and continuous scenarios highlight fundamental deficits and divergences.

Exemplars and Diagnostic Visualization

Concrete event exemplars—from scientific (whale fall decomposition, chemical precipitation) to historical (Apple Computer founding)—illustrate robust diagnostic utility, highlighting where models align or deviate from real causal processes.

Figure 11: Example four-stage visual narratives demonstrate sequential modeling strengths and failure modes for representative scientific and historical events.

Comprehensive visualization of individual scoring rubrics and overall model performance consolidate these findings, confirming poor generalization to multi-image causal generation across all current model architectures.

(Figure 12, Figure model_performance_comprehensive)

Figure model_performance_comprehensive: Comparison of aesthetic, consistency, and physicality sub-metrics across all model classes validates persistent and quantifiable gaps in event-level reasoning.

Implications and Future Directions

The Envision benchmark not only exposes architectural limitations, but also diagnoses a conceptual flaw: the inductive bias produced by static, causally-isolated data and loss functions in current model training. This reveals a need for native multi-image or video-based data modalities and training regimens which enforce explicit spatiotemporal induction bias, as well as architectures where semantic and physical reasoning constraints are interleaved with generative policy across sequential frames.

The explicit transition from event state modeling to process simulation necessitates a paradigm shift—optimizing for world models rather than static synthesis—and suggests chain-of-thought and frame-wise reasoning architectures (e.g. CoT, chain-of-frames [ghazanfari2025chain_frame], VChain [huang2025vchain]) as promising bridges.

Conclusion

Envision rigorously demonstrates that contemporary benchmarks and model architectures in text-to-image and unified multimodal generation are fundamentally insufficient for world process understanding, constrained by their design to static pattern matching and disconnected reasoning. Closed-source models dominate on all evaluated axes but still exhibit critical bottlenecks in spatiotemporal and causal coherence; unified approaches offer progress but remain conceptually and practically limited. Systematic advances will require not only larger and more diverse data, but also explicit architectural and loss function design centered on process simulation and dynamic causal reasoning. The benchmark sets a new direction for future research in multimodal AI, urging holistic evaluation and integrated world model learning.