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

• URL: http://arxiv.org/abs/2512.01816v1
• Date: Mon, 01 Dec 2025 15:52:31 GMT
• Title: Envision: Benchmarking Unified Understanding & Generation for Causal World Process Insights
• Authors: Juanxi Tian, Siyuan Li, Conghui He, Lijun Wu, Cheng Tan,
• Abstract summary: Current models aim to transcend the limitations of single-modality representations by unifying understanding and generation.<n>Their reliance on static single-image generation in training and evaluation leads to overfitting to static pattern matching and semantic fusion.<n>We propose Envision-a causal event progression benchmark for chained text-to-multi-image generation.
• Score: 41.385614383367205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• UniG2U-Bench: Do Unified Models Advance Multimodal Understanding? [50.92401586025528]
  Unified multimodal models have recently demonstrated strong generative capabilities, yet whether and when generation improves understanding remains unclear.<n>We introduce UniG2U-Bench, a comprehensive benchmark categorizing generation-to-understanding (G2U) evaluation into 7 regimes and 30 subtasks.
  arXiv  Detail & Related papers  (2026-03-03T18:36:16Z)
• ITO: Images and Texts as One via Synergizing Multiple Alignment and Training-Time Fusion [14.791563751107502]
  ITO is a framework addressing the limitation through two synergistic mechanisms.<n>We show that ITO consistently outperforms strong baselines across classification, retrieval, and multimodal benchmarks.<n>Our analysis reveals that while multiple alignment drives discriminative power, training-time fusion acts as a critical structural regularizer.
  arXiv  Detail & Related papers  (2026-03-03T09:08:53Z)
• The Trinity of Consistency as a Defining Principle for General World Models [106.16462830681452]
  General World Models are capable of learning, simulating, and reasoning about objective physical laws.<n>We propose a principled theoretical framework that defines the essential properties requisite for a General World Model.<n>Our work establishes a principled pathway toward general world models, clarifying both the limitations of current systems and the architectural requirements for future progress.
  arXiv  Detail & Related papers  (2026-02-26T16:15:55Z)
• RISE-Video: Can Video Generators Decode Implicit World Rules? [71.92434352963427]
  We present RISE-Video, a pioneering reasoning-oriented benchmark for Text-Image-to-Video (TI2V) synthesis.<n>RISE-Video comprises 467 meticulously human-annotated samples spanning eight rigorous categories.<n>We propose an automated pipeline leveraging Large Multimodal Models (LMMs) to emulate human-centric assessment.
  arXiv  Detail & Related papers  (2026-02-05T18:36:10Z)
• Architecture Decoupling Is Not All You Need For Unified Multimodal Model [64.19284951218098]
  We propose Attention Interaction Alignment (AIA) loss, which explicitly learns Task-Specific multimodal interaction patterns during training.<n>AIA not only refines cross-modal attention patterns, but also boosts both generation and understanding performance.
  arXiv  Detail & Related papers  (2025-11-27T17:55:25Z)
• Beyond Words and Pixels: A Benchmark for Implicit World Knowledge Reasoning in Generative Models [15.983959465314749]
  We introduce PicWorld, the first comprehensive benchmark that assesses the grasp of implicit world knowledge and physical causal reasoning of T2I models.<n>This benchmark consists of 1,100 prompts across three core categories.<n>We conduct a thorough analysis of 17 mainstream T2I models on PicWorld, illustrating that they universally exhibit a fundamental limitation in their capacity for implicit world knowledge and physical causal reasoning to varying degrees.
  arXiv  Detail & Related papers  (2025-11-23T03:44:54Z)
• Visual Bridge: Universal Visual Perception Representations Generating [27.034175361589572]
  We propose a universal visual perception framework based on flow matching that can generate diverse visual representations across multiple tasks.<n>Our approach formulates the process as a universal flow-matching problem from image patch tokens to task-specific representations.<n>Our model achieves competitive performance in both zero-shot and fine-tuned settings, outperforming prior generalist and several specialist models.
  arXiv  Detail & Related papers  (2025-11-11T06:25:30Z)
• Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark [69.8473923357969]
  Unified multimodal models aim to jointly enable visual understanding and generation, yet current benchmarks rarely examine their true integration.<n>We present Uni-MMMU, a comprehensive benchmark that unfolds the bidirectional synergy between generation and understanding across eight reasoning-centric domains.
  arXiv  Detail & Related papers  (2025-10-15T17:10:35Z)
• RealUnify: Do Unified Models Truly Benefit from Unification? A Comprehensive Benchmark [71.3555284685426]
  We introduce RealUnify, a benchmark designed to evaluate bidirectional capability synergy.<n>RealUnify comprises 1,000 meticulously human-annotated instances spanning 10 categories and 32 subtasks.<n>We find that current unified models still struggle to achieve effective synergy, indicating that architectural unification alone is insufficient.
  arXiv  Detail & Related papers  (2025-09-29T15:07:28Z)
• TinyTim: A Family of Language Models for Divergent Generation [0.0]
  We introduce a family of language models, TinyTim, to serve as sources of divergent generation within broader systems.<n> Quantitative analysis of both an unsupervised fine-tuned model (TinyTim-V1) and a new instruction-tuned variant (TinyTim-V2) demonstrates a profound capacity for lexical invention.<n>This work establishes a methodology for engineering specialized divergent models that, when paired with convergent systems, can reframe problems and force breakthroughs beyond the reach of statistical optimization alone.
  arXiv  Detail & Related papers  (2025-08-15T17:14:29Z)
• Text-to-Image Diffusion Models Cannot Count, and Prompt Refinement Cannot Help [18.70937620674227]
  We introduce T2ICountBench, a novel benchmark designed to rigorously evaluate the counting ability of state-of-the-art text-to-image diffusion models.<n>Our evaluations reveal that all state-of-the-art diffusion models fail to generate the correct number of objects, with accuracy dropping significantly as the number of objects increases.
  arXiv  Detail & Related papers  (2025-03-10T03:28:18Z)
• Towards Enhanced Image Generation Via Multi-modal Chain of Thought in Unified Generative Models [52.84391764467939]
  Unified generative models have shown remarkable performance in text and image generation.<n>We introduce Chain of Thought (CoT) into unified generative models to address the challenges of complex image generation.<n>Experiments show that FoX consistently outperforms existing unified models on various T2I benchmarks.
  arXiv  Detail & Related papers  (2025-03-03T08:36:16Z)
• Separate-and-Enhance: Compositional Finetuning for Text2Image Diffusion Models [58.46926334842161]
  This work illuminates the fundamental reasons for such misalignment, pinpointing issues related to low attention activation scores and mask overlaps. We propose two novel objectives, the Separate loss and the Enhance loss, that reduce object mask overlaps and maximize attention scores. Our method diverges from conventional test-time-adaptation techniques, focusing on finetuning critical parameters, which enhances scalability and generalizability.
  arXiv  Detail & Related papers  (2023-12-10T22:07:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.