LogiStory: A Logic-Aware Framework for Multi-Image Story Visualization
Authors: Chutian Meng
Fan Ma
Chi Zhang
Jiaxu Miao
Yi Yang
Yueting Zhuang
Venue: ICLR 2026 Poster (2026)
Keywords: Visual Storytelling
Multi-Image Sequence Generation
Story Planning
Visual Logic Consistency
Causal Reasoning
Narrative Coherence
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose LogiStory, a logic-aware story visualization framework that enhances visual and narrative coherence via multi-agent planning and logic verification. We also introduce LogicTale for evaluation.

Abstract: Generating coherent and communicative visual sequences, such as image sequences and videos, remains a significant challenge for current multimodal systems. Despite advances in visual quality and the integration of world knowledge, existing models still struggle to maintain logical flow, often resulting in disjointed actions, fragmented narratives, and unclear storylines. We attribute these issues to the lack of attention to visual logic, a critical yet underexplored dimension of visual sequence generation that we define as the perceptual and causal coherence among characters, actions, and scenes over time. 
To bridge this gap, we propose a logic-aware multi-image story visualization framework, LogiStory.
The framework is built around the central innovation of explicitly modeling visual logic in story visualization. To realize this idea, we design a multi-agent system that grounds roles, extracts causal chains, and verifies story-level consistency, transforming narrative coherence from an implicit byproduct of image generation into an explicit modeling objective. This design effectively bridges structured story planning with visual generation, enhancing both narrative clarity and visual quality in story visualization.
Furthermore, to evaluate the generation capacity, we construct LogicTale, a benchmark comprising richly annotated stories, emphasizing causal reasoning, and visual logic interpretability.
We establish comprehensive automatic and human evaluation protocols designed to measure both visual logic and perceptual quality. 
Experiments demonstrate that our approach significantly improves the narrative logic of generated visual stories. This work provides a foundational step towards modeling and enforcing visual logic in general image sequence and video generation tasks.

Published as a conference paper at ICLR 2026
LOGISTORY: A LOGIC-AWARE FRAMEWORK FOR MULTI-IMAGE STORY VISUALIZATION
Chutian Meng, Fan Ma, Chi Zhang, Jiaxu Miao, Yi Yang & Yueting Zhuang College Computer Science and Technology
Zhejiang University Hangzhou 310027, China (12321215, mafan, 12321216, jiaxumiao, yangyica, yzhuang) @zju eww..edu...e.....e...o..
Pitcher
None Banana
6PT
Ltarytary
Naro Banana
6PT
LogiStory
Cxpelity Breaidenn
thene Aebhey
Cauiiity Irseniion
Ohar Seen Depicion
Smarth Cimatity
Oleir Gvert Doakkt
Aah
Logi:
Lagic
Copassienass
Dhnncter Depcatierass
Gostrnacc
Gherster Dorcsivenc
diciattia Fill..
Cancriing
Figure 1: Comparison of the state-of-ie tf-tte-rrtttttt multimodal models alongside proposed approach LogiStory in the visualization of the simple story "The Crow and the Pitcher." The results highlight the challenges of visual reasoning in the process of visual sequence generation, while demonstrating the effectiveness of LogiStory.
ABSTRACT
Generating coherent and communicative visual sequences, such as image sequences and videos, remains significant challenge for current multimodal systems. De- spite advances in visual quality and the integration of world knowledge, existing models still struggle maintain logical flow, often resulting disjointed actions, fragmented narratives, and unclear storylines. We attribute these issues to the lack of attention to visual logic, a critical yet underexplored dimension of visual sequence generation that define as the perceptual and causal coherence among characters, actions, and scenes over time. To bridge this gap, we propose a logic- aware multi-image story visualization framework, LogiStory. The framework is built around the central innovation explicitly modeling visual logic in story visualization. To realize this idea, design multi-agent system that grounds roles, extracts causal chains, and verifies story-level consistency, transforming narrative coherence from implicit byproduct of image generation into explicit modeling objective. This design effectively bridges structured story planning with visual generation, enhancing both narrative clarity and visual quality story visual- ization. Furthermore, to evaluate the generation capacity, we construct Logic'alee benchmark comprising richly annotated stories, emphasizing causal reasoning. and visual logic interpretability. We establish comprehensive automatic and hu- man evaluation protocols designed to measure both visual logic and perceptual
"Corresponding author.

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quality. Experiments demonstrate that approach significantly improves the narrative logic of generated visual stories. This work provides a foundational step towards modeling and enforcing visual logic in general image sequence and video generation tasks.
INTRODUCTION
Recent advances in generative models have enabled machines to produce high quality visual content from structured or unstructured inputs, such as text, sketches, or semantic layouts Zhou et al. (2025)
Zhuang al. (2024); [i al. (2024). From image synthesis Liu et al. (2024a); Suo al. (2025) to long-form video generation Dalal et al. (2025); Weijia Wu (2025); Guo et al. 2025), the ability to automatically construct visual narratives has opened exciting opportunities in creative domains
ranging from illustration and education to filmmaking and simulation. Despite advances in high- fidelity content generation, ensuring that visual sequences evolve in a logically coherent manner that aligns with human expectations continues to be key challenge.
Story visualization is a challenge task in visual sequence generation, where the goal is to generate sequence of images that together depict given narrative. Compared single-image generation, this task requires not only high-quality visual outputs but also coherent storytelling across multiple images. Prior work has devoted substantial attention improving visual quality and entity consistency Maharana et al. (0022); Pan et al. (2022); Rahman et al.] (2022); Zhou et al. (2024); Tewel et al. (2024); Singh al. (2025); Liu al. (2025), which neglects narrative interpretability. resulting in sequences that resemble isolated image snapshots rather than causally connected visual narratives. Although significant progress has been made in textual logic modeling and planning [Fnn al. (2023); Yao et al. (2019); Peng et al. (2022). there remains a substantial gap between textual and visual expression.
To address these challenges, we formally introduce the concept of visual logic. Visual logic refers to whether the progression of visual content across time or spatial layout forms an interpretable, causally sound, and semantically plausible experience for the viewer. As shown in Figure D failures related to visual logic in story visualization can manifest in many forms: abrupt object state changes without explanation, inconsistencies, emotionless contradictory character behaviors. Building this, we propose a structured generation framework LogiStory, which explicitly models visual logic through components. First, introduce Logic-Aware Multi-agent System that transforms the input narrative into structured visual representations, including character definitions, object attributes, scene layouts, and panel-wise events. Second, design Visual Logic Enhancement Module that reinforces consistency through a Global Causal Verifier (which builds action-state graphs over the full story) and Local Causal Monitor (which simulates human step-by-ster stes comprehension during generation).
To support the development and evaluation of visual logic in generative models, we construct new benchmark, Logic'Tale, featuring causal annotations, action-state flows, and panel-level story breakdowns. We further design visual logic-aware evaluation metrics that assess narrative coherence and interpretability beyond surface-level visual quality. In summary, the contribution of this paper includes:
(1) We define the concept of visual logic as critical yet under-addressed dimension of generative modeling, and instantiate through the task of multi multi-image story visualization.
(2) We propose LogiStory, novel logic-aware framework for multi-image story visualization. LogiStory reframes storytelling as logic-aware reasoning problem rather than isolated image syn- thesis, bridging structured narrative understanding with visual generation and explicitly strengthening story-level logic.
(3) We introduce LogicTale new benchmark dataset with annotated causal chains and structured visual representations, along with novel evaluation metries that target visual logic consistency and narrative interpretability in visual sequences generation tasks.
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2 RELATED WORK
2.1 STORY VISUALIZATION
Story visualization aims to generate a sequence of images that visually depict the progression of a narrative. Early work in this domain relied on GAN-based models [i et al. (2019); Feng et al. (2023), which generated one image per sentence with limited consideration for global coherence. The introduction of diffusion models significantly improved visual fidelity and control, enabling
(222) to methods Zhou et al. (002)); Tewel et al. (2024); [Shen& & Elhoseiny (2025); Wang et al.
maintain character consistency across frames via identity conditioning. More recently, large language
models have driven the development of fully automated pipelines. Agent- basedd systems Hu et al.
(2024);Xu et al. (2025); Yang et al. (2024) perform explicit story planning to guide image generation, while multimodal large language models (MLLMs) such as GPT-4o OpenAl
(2024) and Gemini 2.0 Flash DeepMind (024)) show strong performance in short-form comic generation. Despite these advances, existing methods often lack mechanisms for modeling and enforcing narrative logic across sequences. Our work addresses this gap by introducing visual logic as a core objective, and proposing the LogiStory framework to enhance the interpretability of story visualization.
2.2 CAUSAL REASONING IN TEXT AND VISUAL GENERATION
Maintaining causal coherence is critical for generating content that is logically understandable and narratively complete. In text-based story generation, early models often relied on superficial event orderings and struggled to capture causal dependencies between events Yamin et al. (2024). To address this, recent approaches Huot et al. (2024): Xi et al. (2025) incorporate structured planning, common sense inference, or intermediate causal representations such as event graphs, enabling models to better model cause-effect relations and produce more coherent narratives. In the visual domain, causal reasoning poses greater challenges due to the need for temporal consistency and interpretable transitions across images or video frames. Early story visualization methods Li et al. (2019); Liu et al. (2024a) lacked explicit mechanisms for cross-frame logic, while more recent approaches leverage character tracking Wu al. (2024). structured representations (Singh et al. (2025), or diffusion-basse continuity. However, most of these methods focus on appearance or motion consistency rather than story readability and interpretability. Our work addresses this limitation by explicitly modeling visual logic, defined as the perceptual and causal coherence across a visual narrative, and introducing a generation framework that integrates structured planning with causal reasoning to ensure the intended story is both interpretable and logically sound.
conditioning Zhou et al. (2024); Tewel et al. (2024) to maintain visual
2.3 EVALUATION OF VISUAL SEQUENCES
Evaluation of Visual Sequences remains a largely open problem, especially with regard to assessing narrative logic. Existing evaluations for visual storytelling often rely standard image-level metries such as FID for realism Heusel et al. (2018), CLIPScore for semantic alignment Radford et al. (2021) -INOObased embedding distances for visual consistency Oquab al. (2024). A Alternatively, some works adopt generic benchmarks designed for image generation | Huang et al. (202)2 Ku et al. (2024); Wiles et al. (2024): Cho al. (2024). While effective for assessing individual image quality, these approaches largely overlook inter- frame relationships, failing to evaluate whether the image sequence conveys coherent narrative or maintains causal and temporal dependencies. In video generation, several benchmark suites have been proposed Liu et al. (2024b): Huang et al. (2024a b). introducing a set of standardized metrics that assess frame quality, temporal alignment, and object continuity. However, even such benchmarks lack explicit evaluation of narrative-level logic causal flow across scenes. To our knowledge, while VinaBench Gao et al. (2025) incorporates commonsense links and visual consistency, logical coherence in visual story sequences, which motivates our emphasis on visual logic and causal reasoning.
not aware of an existing benchmark that explicitly evaluates narrative-level
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Story Input
Logic-oware Story Agent
Serry jost The ink you lame
SceneCrufter
LagloMiner
Shatfloaner
offiur Enusta
noull ane aadlepu di
Scere Blueprint
Story Skeleton
Visual Scripts
biiduation aadlage hiiks
Chorater
docriioee Wollamde bnkoitoass
Tivee
lonening charartenc par PIC..
Event Slar
sulicg
newalbntt baak streng gip borkar taikk
Reatitnn tteas doasc book
sediaal an axdalaiike pat Locail Eal
chieeyy spwar expossiiie
sult in cale Encogllitle deeeer....
velivitaut liow ennatte took,
Nette Now fourning lill pacteeb
Scato Noyte
da officiin Wel
Coean enegle boy levd
Global Verifier
Shate Recorder
Merery Buffer
Chareetee State
Kee ebject State Rrod (Tomeecee))
The pig nig atoo the sooo woodd cocedo barkkmbbikkngg beikkay came
Befereacs
Ny you
Pethie Signal
Edit
QWvniyy
Jiletre Iratmaciion
Images
Figure 2: Overview of LogiStory frameworR. Given an input story, our system first applies a multi-agent story planner to decompose the story into structured panels with detailed scripts. In the generation process, the Local Causal Monitor simulates a reader's s linear understanding by evaluating each frame for inconsistencies and generating refinement signals. Then, the Global Causal Verifier applies the causal graph to produce concrete refinement instructions to correct errors and maintain narrative flow.
METHOD
3.1
TASK FORMULATION
Given text-form story S, the goal is to generate sequence images [2,,1,,... I2...., ,,T such that the sequence visually depicts the storyline in a coherent, interpretable and logically consistent manner. Unlike traditional text-to-image generation, this task requires maintaining framm- dependencies across multiple dimensions, including: (1) Instance consisteney: maintaining identity, appearance, and position of recurring instances. (2) Narrative causality: ensuring that actions and consequences follow coherent and plausible causal chain. (3) Story readability: clearly conveying the intended narrative via the generated image sequence.
3.2 LOGISTORY FRAMEWORK
We propose LogiStory shown in Figure 2 including two components: (1) Logic-Aware Multi-agent System constructs structured intermediate representations from the input story. Enhancement Module guides the synthesis and refinement of each image by incorporating both the story structure and multi-level visual logic verification. The detailed implementation((..e prompt templates) is provided in the Appendix D
(2) Visual Logic
3.2.1 LOGIC-AWARE MULTI-AGENT SYSTEM
The Logic-Aware Multi-agent transforms input story into interpretable intermediate repre- sentation. This system consists of three collaborative agents that work in stages: (1) SceneCrafter. an entity and context definition agent, (2) LogicMiner, a key event extraction agent, and (3) Shot- Planner, a shot-level planning agent. Together, they decompose the input narrative into structured components that guide the visual generation pipeline.
SceneCrafter. Given a story text S, the agent extracts and defines set of semantic entities for consistent visual generation:
E (S),
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where F-ma(()) defines attributes of entities in the story. The entity set & comprises characters = (cI,..., CK J. objects O = foi..... oM J. and scenes = { SN These structured definitions form a visual grounding vocabulary, reused across all panels to ensure stable and consistent representation throughout the story, ensuring stable representation of characters and objects across different scenes and supports downstream modules in maintaining entity consistency and contextual coherence.
LogieMiner. To guide visual composition, we extract key narrative events defined as tuples k;- (actor, action, target, result), capturing causally relevant or state-changing moments in the story. Given the input text S, we apply large language model to directly extract these structured events. The function can be formalized as:
K= (A1..... ks} Fmine (S,
where m-)ine-) identifies both explicitly stated and implicitly implied state transitions critical for visual grounding. This dual capability ensures that both direct narrative cues and essential inferred changes are captured for visual grounding. For example, from "the crow put pebbles into the cup," LogicMiner infers the rising water level. These events form the causal backbone, guiding subsequent shot planning and scene arrangement.
ShotPlanner. Given key events K. and entities E, the shot planning agent organizes them into sequence of panel specifications. The function can be formalized as:
p= (pr, = Shaaa E),
where F (-) designs detailed shot specifications, including characters, actions, objects, spatial relations, scenes, and camera parameters. ShotPlanner incorporates visual storytelling conventions (e.g., pacing, framing, perspective) to control narrative rhythm, emphasize key events, and ensure visually coherent, engaging sequences, ensuring the resulting image sequence not only captures the intended events but also presents them in visually engaging and narratively coherent manner.
3.2.2 VISUAL LOGIC ENHANCEMENT MODULE
Although structured planning provides high-level narrative scaffolding, ensuring that the generated image sequence aligns with numan-interpretabbe logic requires deeper modeling of visual logic. We propose Visual Logic Enhancement Module to bridge the gap between semantic planning and visual realization. This module comprises two complementary components as follows.
Algorithm 1 Visual Enhancement Module
Input: Panel list P = (pı..... Pr). story text S, key logic K. = (k) ........
Output: Image sequence ........ LT}
Construct causal graph Geausal from S and Ini- tialize memory butfer Mo for to T do
Generate draft image I₁ for panel Pt Com- pute causal score 4h Cp M,-1) if t/y < then Regenerate 7, else
Local Causal Monitor. To model the linear com- prehension process of human readers, we intro- duce Local Causal Monitor that evaluates each panel generation step by step. We maintain a text- form causal memory buffer M,-1 consisting of state snapshots and actions from {Pi..... Pt- I). Given the current panel image I and its local con- text M,-1, we simulate a human reading path by checking whether P. remains narratively plausible and coherent with respect to M,-1, rather than en- forcing deterministic prediction. Using MILMS simulate the reader's 's reasoning process, define a causal plausibility score 4iy as:
if c/e E [T1, .2) then Invoke Global Causal Verifier Edit
4g using visual tools
else
Accept Le
Update M, + M (Caption(Ie))
return [4....,,44
Cp (Ie M.-1)
where C₂ evaluates the degree to which the depicted state transitions are consistent with or reasonably extend the accumulated context. This allows the monitor to accommodate both expected progressions and surprising developments, as long as they preserve overall narrative logic and do not contradict prior states.
Global Causal Verifier. Based on the extracted key events IC and story text S, we construct a directed causal graph Geausal representing the narrative's logical backbone, where nodes represent key
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states (e.g.. character status, object conditions), and edges represent causal temporal dependencies inferred from the event semantics. A state recorder tracks the current states of all instances, updating dynamically the story progresses to provide reference for verification. For each generated image Pt. the verifier checks whether the visualized state transitions match the expected causal links the graph. Specifically, for action at, define its pre-condition state St and post-condition state S.
S _ Sp
These transitions validated against the causal graph ensure consistency with the established narrative chains:
Vt, (S Sp E Paths(Geasal)-
Any inconsistency is flagged and sent to image editing tools for correction.
Image Refinement. To ensure logical coherence during generation, each panel Pt is immediately evaluated upon creation. Given the logic confidence score the E [0, 1] using the Local Causal Monitor. Refinement decisions are made according to Algorithm [] The thresholds тΙ = 0.4 and 72 = 0.7 marks the boundary below which the generated image is considered unrelated to the narrative, while T2 indicates sufficient alignment with the intended meaning. If refinement is needed (r1 v T2), we invoke the Global Causal Verifier, which maintains a structured causal graph of actions and states. It provides explicit revision instructions, guiding targeted editing via image editing tools.
are empirically set based preliminary experiments,
LOGICTALE BENCHMARK
Since existing story visualization benchmarks lack direct evaluation of story-level logic, we design dataset and evaluation suite, LogicTale to explicitly assess visual logic multi-image narratives and facilitate the development of logic-aware visual content generation. The detailed dataset composition and evaluation protocol definition are provided in the Appendix
4.1 DATASET CONSTRUCTION
Dataset Composition. The dataset contains 60 meticulously annotated stories. To ensure both diversity and generalization, we include balanced mix of well-known classic stories and human- authored original stories, in a 3:2 ratio. The inclusion of well-known classic stories ensures the fundamental quality and reliability of the dataset, while original stories introduce unseen structures that posing greater challenges for the task. Stories are labeled as easy, medium, or hard based on visual reasoning difficulty.
Data Annotation. Each story contains the following components: (1) A story title and its source. (2) The full narrative story text. (3) A character list specifying key and supporting entities. (4) A set of visual logic chains annotated as tuples (action, result, weight), where each tuple represents a causally important event, and >> weight, = 1 to normalize importance. (5) A difficulty label reflecting the expected complexity in visual story modeling.
Dataset Scale. Regarding evaluation scale, dataset is comparable larger than prior works: StoryDifflssion (20 short single-character stories) Zhou et al. (2024), ConsiStory (20 hand- crafted scenes) Tewel Tewel al. (2024), MM-StoryAAget (100 LLM --M-eeerrated stories) Xu al. (2025). MovieAgent (12 authored examples) Weijia Wu (2025), and ViStoryBench (80 test cases) Zhuang al. (023). Thus, LogicTTal''s 60 richly annotated, multi-character stories with varied difficulty provide a solid, representative benchmark.
4.2 EVALUATION PROTOCOL.
To evaluate the performance, we consider two complementary dimensions: visual logic and percep- tual quality. Together, these evaluation components form comprehensive benchmark that captures both the logical interpretability and visual quality of generative systems in visual storytelling.
Visual Logic Evaluation. We evaluate visual logic from three aspects. (1) Instance Consistency metrics evaluate whether key elements are preserved across frames via MLLMs. Character consis- tency examines whether character appearance, clothing, and identity remain visually stable. Object

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consistency checks whether the appearance and transformation of key objects follow a plausible state evolution. Scene consistency ensures that background environments remain locally coherent unless disrupted by explicit narrative cues. (2) Narrative Causality depends
whether the generated images accurately express the annotated key events. Each event eş = (action, result, weight) is assigned quality score based on its clarity, coherence, and plausibility. The overall event-based score is computed as:
CausalScore EventScore(e))-wwe wweghht,
(3) Story Readability represents how well the generated image sequence conveys the intended narrative. To do this, we first apply a captioning model (e.g.. BLIP-2) to produce textual description for the entire image sequence. We then provide an LLM with two inputs: the story's character list and the generated caption. The model is prompted to infer the underlying story based on this information. Finally, we compute semantic similarity between the inferred story and the original ground-truth narrative to assess global story alignment.
Perceptual Quality Evaluation. Perceptual quality focus on the qualities of image presentation. (1) Aesthetic Quality is assessed using HPSv2 to evaluate the visual appeal of the image sequence. (2) Style Consistency examines whether the entire sequence maintains coherent visual rendering style, estimated via DIÑOv2 embedding distance. (3) Character Expressiveness evaluates how well the emotions, poses, and gestures of characters align with the narrative events and is rated by MLLMs.
5 EXPERIMENTS
5.1 EXPERIMENT SETTING
Dataset. We conduct experiments on the proposed LogicTalee dataset, providing a diverse testbed for evaluating visual narrative generation. For fairness and generality, we also compare against datasets used in prior works such as ViStoryBench and StoryDiffusion, and report corresponding evaluations in the Appendix B
Metrics. We evaluate performance using both automatic and human assessments. Automatic evaluation follows LogicTalee evaluation protocol. Human evaluation is designed to closely align with our automatic evaluation protocol. To rigorously assess story-level logical coherence, we include three dimensions: Instance Consistency, Narrative Causality, and Story Readability. This design enables human judgment to provide a fine-grained evaluation of the core narrative logic aspects targeted in our framework. In contrast, perceptual quality is evaluated using a single dimension, Aesthetic Appeal, which captures overall visual attractiveness and stylistic coherence. We adopt this simplified form because perceptual quality is not the central focus of this work and to reduce annotation cost. For each test case, annotators are presented with the full story text together with the generated image sequence. The detailed implementations are provided in the Appendix C
LogiStory Settings. The agent module leverages DeepSeek-V3 the base LLM. The image generation module utilizes Flux for initial panel synthesis. For image refinement and editing, we integrate inpainting models and MLLMs, including GPT-image-1 and Gemini 2.0 Flash, support targeted edits guided by the verifier's feedback.
Baselines. We compare our method with both closed-source and open-source baselines. For closed models, we evaluate the most capable publicly accessible systems to date: Nano Banana Google
DeepMind|| (2025). Gemini 2.0 Flash DeepMind (2024) and GPT-44+GPP--magee OpenAl (2024) both of which support end-to-end generation from story text to panel planning and visual sequence rendering. For open-source baselines, we select models capable of open-ended and general-purpose story visualization, as opposed to methods fine-tuned on specific datasets (e.g.. Story Shhn & Elhoseiny (2025)). We employ the end-to-end agent-based framework MM-StoryAgem Xu et al. (2025). While existing works such Story Zhou et al. (2024) and ConsiStory Tewel et al. (2024) lack built-in story planning modules. To ensure fair comparison, we pair these methods with DeepSeek-Re R1 DDepSeek-AA DeppSSeee-RRDepSSeeeee...e
(2025) planning backbone to generate panel-level scene descriptions. We also evaluate the performance of standard generative models including SDXL Podell et al. (2023)
and Flux Labs 2024) under the same setup.
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Table 1: Performance of Automatic metric story visualization methods. "ICons.", "NCausal." "SRead.", "AesthQ.", ". "SCons.", "CExpr.", "App." refer to "Instance Consisteney", "Narrative Causal- ity", "Story Readability", " "Aesthetic Quality", "Style Consistency"", "Character Expressiveness' "Aesthetic Appeal". The first rank is highlighted in bold, while the second rank is underlined.
Visual Logic
Perceptual Quality
User study
Method
ICons.
NCausal.
SRead.
AesthQ.
SCons.
CExpr.
ICons.
NCausal.
SRead.
App.
DeepSock R1
2,02
DeepSesk-R1 + Flux
3.87
3,43
0.6872
0.3047
0.8329
4.20
3.53
3,25
3.20
3.8
DeepSeck R1 + StoryDiff'
3.07
2,06
0.5409
0.2909
0.8276
2.07
2.70
2.12
2.35
2.6
DeepSeck-R1 ConSistory
3.23
1,98
0.5623
0.2939
0.8129
2.26
2.87
1.73
1.87
2.4
MM StM-SttryApe SteryAgent
3.03
2.63
0.5787
0.2942
0.8023
2.54
2.67
3.04
2.43
2.6
Gemini flash
3.88
3.65
2.8
Nano Banana
4.20
4.08
0.7440
0.3102
0.90134
4.26
4.20
3,96
3.75
4.4
GPT-4n + GPT-image-1
4.67
3.96
0.7622
0,2829
0.8984
4.15
4.38
3.78
3.65
4.6
Ours
4.23
4.45
0.8267
0.3088
0.8572
4.32
4.25
4.26
3.83
4.2
Flux
StoryDiffusion
AM-SStryAAgett
Gemini 2.0 flash
Naro
40
LogiStory
Quality analysis of generated stories across different methods. Representative key scenes are shown here. Complete sequences can be found in the Appendix
Figure 3:
5.2
PERFORMANCE COMPARISONS AND ANALYSIS
5.2.1 AUTOMATIC EVALUATION
The results are shown in Table Our approach achieves the highest score in Narrative Causality, Story Readability, Aesthetic Quality and Character Expressiveness. In terms of Instance Consistency and Style Consistency, our method slightly trails behind GPT-Image-l, which benefits from its strong ability to edit reference images. Nevertheless, our method shows competitive performance in this regard. F presents a qualitative comparison generated results on the story Three Little Pigs. Open-source methods, including Flux (basic diffusion model), Story Siffusiifs generation model), and MMMStoryAAeen attribute mismatches and inconsistent visual elements, resulting in low narrative clarity and weak interpretability. Nano Banana and Gemini 2.0 Flash demonstrates a relatively clear planning of story rhythm. However, it still suffers from instance omissions and attribute errors. Similarly, GPT- shows attribute confusion issues, such as mixing the clothing the three pigs. In contrast, LogiStory generates story sequences with clearer logic, stronger narrative readability, and higher consistency between characters, actions, and scenes, effectively addressing the challenges of visual logic in multi-image story visualization.
seaaracterconsisttee lagent-based system), all exhibit common errors such as
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w/o logic-awor aware multi-agent system
w/o local causal monitor
w/o glocal causal verifier
Sudden uppearance of pebbles, thougia lie causality aligns with
Caualitt failure. The water level rises with pubbles inside.
Inconsistency and
Figure 4:
Ablation study key components of LogiStory. Qualitative comparisons representa- tive examples demonstrate the impact of each module.
5.2.2 HUMAN RATING
To comprehensively assess subjective quality, we conducted a user study with 30 participants. Each was shown randomly shuffled outputs from all methods for the same story, without knowing their identities to avoid bias. The results in Table [ show that our method outperforms others on the three logic-focused dimensions: Instance Consistency, Narrative Causality, and Story Readability. It also achieves high scores in Aesthetic Appeal, indicating both logical clarity and visual attractiveness. We further measured alignment between user study and automatic evaluation using Pearson correlation, yielding strong results (Instance Consistency: 0.959, Narrative Causality: 0.978, Story Readability: 0.909, Perceptual Quality: 0.695). The lower correlation for perceptual quality is mainly due stylistic homogeneity among SDXL-based methods such as StoryDiffusion and ConsisStory, which reduces discriminative power.
5.3 ABLATION STUDY
To better understand the contribution of each module our framework, we conduct ablation experi- ments on logic-aware multi-agent system and visual logic enhancement module. Since our work primarily focuses on enhancing visual logic, we restrict the evaluation to the visual logic metrics in our benchmark. Qualitative results are shown in Figure
Effect of Planning Methods: evaluate the effectiveness of our multi-agent planning module, we conduct an ablation study by replacing with an L.LM-based planner, where the LLM directly outputs a sequence of actions based on the story description. All other components of the LogiStory frame- work remain unchanged. As shown in Table compared directly prompting large models such as DeepSeek-V33 Voeek-33/eeppppe DeepSeek All 2024),, DeepSeek-FR RI DeepSeek-ALl (22255)) or Qwen2.5--22 Team (2024), the inclusion of our multi-agem agent story planning system significantly improves performance across all visual logic metrics. This demonstrates the effectiveness of decomposing the narrative into structured roles and key events, which provides strong priors for coherent image sequence generation.
To evaluate the role of the Global Causal Verifier in ensuring story-level causal coherence, we conduct an ablation study where the original verifier is replaced by an LLM-based baseline. Specifically, we remove the Global Causal Verifier module and instead use an LLM to directly generate refinement instructions based solely on the story text without structured causal reasoning. As shown in Table the integration of the Global Causal Verifier leads to notable gains in Narrative Causality scores. We attribute this its ability explicitly monitor and enforce state-action-result chains throughout the story, ensuring that critical logical transitions are preserved the visual narrative.
Effect of Global Causal Verifier:
We further assess the contribution of the Local Causal Monitor by removing this module from the pipeline. In this ablation setting, the system bypasses the local monitor and instead directly evaluates the alignment between each generated image and its corresponding shot-level script using a simple matching score. As shown in Table 3, adding the Local Causal Monitor brings consistent improvements in Story Readability. By simulating the human reading process and performing incremental causal validation during generation, this module enhances the overall clarity and interpretability of the story.
Effect of Local Causal Monitor:

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Published as a conference paper at ICLR 2026
Table 2: Ablation study story scripts planning methods,
Table 3: Ablation study on visual logic hancement module.
Planning Method
ICons.
NCausal.
SRead.
Method
ICons.
NCausal.
SRead.
DeepSeck- V3
3.92
3.64
0.7233
Ours (w/ none)
4.16
3.93
0.6527
DeepSeek-R
3.87
3.86
0.7159
Ours (w/ global)
4.12
4.27
0.7283
Qwen2.5-72B
3.66
3.52
0,6614
Ours (w/ local)
4.24
4.10
0.7732
Ours
4.23
4.45
0.8267
Ours (w/ both)
4.23
4.45
0.8267
5.4 COMPLEX CASE AND FAILURE ANALYSIS
In this subsection, present representative hard-level case featuring multi-task narrative struc- ture, flashback usage, and subtle emotional implications. We provide the fully rendered causal graph alongside key panel comparisons for illustration. As shown in Figure the framework con- structed causal graph (b) effectively decomposes the core logical dependencies and developmental relationships within the story.
For such complex scenarios, we observe that the primary bottleneck lies on the generation side rather than the planning stage. Our framework demonstrates higher character consistency and better narrative alignment compared with representative baselines such as StoryDiffusion and GPT- 40 GPT-image- However, certain challenges remain: for instance, visual emotional expression may be insufficiently delicate, and event-level coherence can occasionally degrade. This is partly due the inherent difficulty of visually presenting fine-gra amn-ggrined emotional trajectories, especially when narrative intent relies strongly on subtle affective cues.
StoryDiffusion
BOD
GPT-4o8
-mo-ee
00
Ours
(a)
(b)
Figure 5: Quality analysis on Complex Case. Memory Lasts: An old painter takes his student to distant sunftower field, once shared with his younger brother before the war. As they paint, the narrative shifts briefly to a childhood flashback, two boys painting under the sunflower sky, one waving goodbye. Upon returning home, the student donates commemorative painting to local school, where it remains as the old painter's 's lasting memory.
CONCLUSION
In this work, we introduce visual logic as central objective in story visualization, tackling the under- explored challenge of ensuring causally coherent and semantically plausible storytelling across image sequences. We present LogiStory, structured framework that combines multi-agent planning with causal reasoning, producing sequences that are both visually appealing and narratively coherent. To enable rigorous evaluation, construct LogicTale, benchmark with causal annotations, difficulty levels, and protocols for both automatic and human assessment. Experiments show that our approach surpasses existing baselines, especially in modeling complex visual causality. We believe this work lays a foundation for logic-aware generation and offers insights for advancing story visualization and video synthesis.
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7 ETHICS STATEMENT
This work focuses on logic-aware story visualization framework LogiStory and the construction of the LogicTTale dataset. The dataset is composed of publicly available narratives (e.g.. classic literature, fables, and human-authored short stories) that do not contain private or sensitive information. All human annotations were collected from consenting participants, who were informed of the purpose of the study and compensated fairly. We ensured that annotators' personal data were not collected or stored, thus preserving privacy. The proposed framework, LogiStory, is general-purpose purpose research system and does not target harmful or sensitive applications. Nevertheless, as with other generative models, there risk of producing biased or culturally inappropriate content. To mitigate this, we include stories from diverse cultural backgrounds and explicitly annotate logical structures to encourage fairer and more interpretable evaluations. To support transparency and reproducibility, we plan to release both the dataset and implementation code in the near future, subject to proper documentation and licensing considerations.
8 REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our work. The implementation details of the LogiStory framework, including model configurations and pipeline design, are provided in Appendix(D) [D] The construction process of the Logic Tale benchmark, along with annotation protocols and evaluation procedures, is described in Appendix [ We provide a subset of Logic'Tale in the supplementary material. These materials are intended to allow researchers to replicate both our framework and evaluation methodology.
ACKNOWLEDGMENTS
This work is supported by the National Natural Science Foundation of China (62436007).
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A
MORE QUALITATIVE RESULTS
SDXL
Flux
ConsiStory
StoryDiffusion
MM-StoryAgent
Gemini 2.0 flash
Nano Banana
GPT
Ours
Figure 6: Quality analysis of generated stories across different methods. The Crow and the Pitcher: Under the scorching sun, across vast, parched land, a thirsty crow flies in search of water. eventually spots a tall glass bottle with a small amount of water at the bottom. Unable to reach with its beak, the crow notices small pebbles scattered the ground. One by one, it picks up the pebbles and drops them into the bottle. As the stones pile up, the water level slowly rises. At last, the water reaches the top, and the crow happily quenches its thirst.
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SDXL
Flux
ConsiStory
StoryDiffusion
MM-SStoyAAgen
Gemini 2.0 flash
Nano Banana
GPT
LogiStory
Figure 7: Quality analysis of generated stories across different methods. Three Little Pigs: Three little pigs live their mother's house and each build a house: one of straw, one of sticks, and one of bricks. hungry wolf comes and blows down the straw and stick houses, causing the first two pigs to flee to the brick house. The wolf tries to blow it down but fails. He then climbs the chimney, but the pigs boil a pot of water inside, and the wolf falls in and runs away burned. All three pigs live safely together in the brick house.
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SDXL
Flux
ConsiStory
StoryDiffusion
MM-StoryAgent
Gemini 2.0 flash
Nano Banana
GPT
Ours
Figure 8: Quality analysis of generated stories across different methods. The Pebble Bridge: group of ants needs to cross small stream. They drop pebbles into the water to form bridge and successfully cross together.
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SDXL
Flux
ConsiStory
StoryDiffusion
MM-StoryAgent
Gemini 2.0 flash
Nano Banana
GPT
Ours
Figure 9: Quality analysis of generated stories across different methods. Borrowing Arrows with Straw Boats: During a critical shortage of arrows, Zhuge Liang boldly promised to deliver 100,000 arrows within three days. To fulfill this promise, he ordered countless straw men to be placed boats and, under the cover of thick fog, sailed toward Cao Cao' 's camp. Mistaking the figures for soldiers, the enemy forces unleashed volleys of arrows the boats. When the boats returned, they were laden with arrows, allowing Zhuge Liang to fulfill his promise cleverly and without shedding drop blood.
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SDXL
Flux
ConsiStory
StoryDiffusion
MM-StoryAgent
Gemini 2.0 flash
Nano Banana
GPT
Ours
Figure 10: Quality analysis of generated stories across different methods. The Dog and His Reflection: dog crosses a bridge with a bone in its mouth. Looking into the water, he sees his own reflection and mistakes it for another dog with bigger bone. He snaps the reflection, dropping his bone into the river and losing it.
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Coutfay
Contley
13
Score Marrrrl R0e0
0.2
DAay
20
HYdl
0968
1811
(a) Instance Consistency
(b) Narrative Causality
(c) Story Readability
Figure 11: Comparative evaluation of different methods across difficulty levels.
B MORE QUANTITATIVE RESULTS
B. MORE METHODS PERFORMANCE ON LOGICTALE
To further demonstrate the robustness of our framework, we include results additional baselines on the LogicTale benchmark. Table 4 reports the performance across six dimensions: Instance
Consistency (ICons.), Narrative Causality (NCausal.), Story Readability (SRead.), Aesthetic Quality (AesthQ.), Style Consistency (SCons.), and Character Expressiveness (CExpr.).
Table 4: Performance comparison of additional baselines on the Logic Tale benchmark.
Method
ICons.
NCausal.
SRead.
AesthQ. f
SCons. f
CExpr:
StoryGen
3.24
1.72
0,5366
0.2873
0.8162
1.92
StoryDiffusion
3.07
2.06
0.5409
0.2909
0.8276
2.07
Story-Adapter
3.65
2.23
0.5733
0,2952
0.8195
3.16
Ours
4.23
4.45
0.8267
0.3088
0.8572
4.32
As shown in Table 4. our method consistently outperforms all baselines across both logical dimen- sions (Instance Consistency, Narrative Causality, Story Readability) and visual dimensions (Aesthetic Quality, Style Consistency, Character Expressiveness). These results confirm that LogiStory achieves superior performance in balancing narrative logic with visual presentation, reinforcing its role as the first framework explicitly designed for logic-aware visual storytelling.
B.2 ANALYSIS ON DIFFERENT DIFFICULTY LEVELS
A further analysis based on the difficulty levels shown in Figure reveals that our method's 's advantage becomes more pronounced as the story complexity increases. This highlights the robustness of our approach in handling more intricate story structures.
B.3 LOGISTORY WITH DIFFERENT BASE MODELS
To evaluate the adaptability of LogiStory different backbone models, we tested the framework with smaller vision-language models: Qwen2.5-Vi -L--BB Intern VL. 2.5-8B, and (ivn2.5-VL-3222 For reference, we also include results with the larger DeepSeek- backbone. Results are summarized in Table
Table 5: Performance of LogiStory with different backbone models.
Backbone
ICons.
NCausal.
SRead.
Qwen2.5-ViL 5--7BB
3.04
3.32
0.6132
Intern VL 2.5-8B
3.21
3.24
0.6328
Qwen2.5-VV B-W-225-VL-32BB
3.85
3.78
0.7244
DeepSeek- V3
4.26
4.45
0.8267
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As shown in Table smaller backbones yield lower scores logic-related metrics, but performance still surpasses baseline methods, confirming the framework's ' robustness.
A closer analysis identifies three factors behind the performance gap:
Shorter panel scripts: Smaller models generate less detailed scene breakdowns, limiting narrative depth.
Weaker multi-agent communication: Semantic information is more likely be lost across stages, reducing coherence.
Homogeneous causal scores in Local Causal Monitor: Smaller LLMs assign uniformly high scores, failing to trigger necessary refinements.
These findings highlight that while stronger backbones enhance story-level logic modeling. LogiStory remains effective and competitive even with lightweight models.
B.4 EVALUATION OTHER DATASETS
We conducted additional evaluations on ViStoryBench-Lite to further assess the generalizability of our framework. Results are summarized in T
Table 6: Comparison on ViStoryBench-Lite.
Method
CSD Self
CIDS Self
Alignment
OCCM
Inception
Aesthetics f
GPT-4o
68.5
73.1
89.3
93.4
9.02
5.52
Gemini 58.6
53.7
76.1
86.9
10.12
4.91
Story-Adapter
70.0
62.6
38.8
82.0
10.36
5.81
Ours
64.6
69.4
92.2
93.4
10.16
5.88
As shown in Table 6 method performs strongly across dimensions, particularly in Align- ment, OCCM, and Aesthetics. While some baselines achieve slightly higher scores on low-level quality metrics, LogiStory excels in narrative coherence and visual storytelling, which aligns with the central goals of our framework. These results further demonstrate its generalizability beyond LogicTale.
B.5 USER STUDY ANALYSIS
We evaluate inter-rater agreement using Krippendorff's Alpha t, a robust statistical measure for ordinal-scale annotations. Across 30 annotators and 8 methods, the agreement scores are: 0.78 for Instance Consistency, 0.82 for Narrative Causality, 0.70 for Story Readability, 0.60 for Aesthetic Appeal.
The relatively lower agreement on Aesthetic Appeal is likely due to the visual similarity of outputs from methods such as ConsiStory and StoryDiffusion. To further confirm annotation reliability, we conducted subset analysis on four representative methods: SDXL, StoryDiffusion, Gemini 2.0 Flash, and LogiStory. In this focused comparison, Krippendorff's Alpha improves to: 0.90 for Instance Consistency, 0.94 for Narrative Causality, 0.84 for Story Readability, 0.86 for Aesthetic Appeal.
These results demonstrate high consistency and reliability of the human evaluation process, particu- larly when method diversity increases.
C LOGICTALE BENCHMARK DETAILS AND ANALYSIS
C.1 LOGICTALE DATASET CONSTRUCTION
To facilitate the development and evaluation of logic-aware story visualization models, introduce the LogicTale dataset. This dataset specifically designed to assess narrative coherence, causal interpretability, and visual quality. The construction process is follows:
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C.1.1 STORY COLLECTION AND COMPOSITION
We curated a total of 60 stories, balancing between classical well-known narratives and newly created original stories in ratio of 3:2. This ensures both diversity and challenge:
Classical stories: Selected from widely known fables, fairy tales, and folklore to provide recognizable plot structures.
Original stories: Authored by professional writers and illustrators, designed to introduce novel situations, abstract concepts, and creative settings that challenge visual logic modeling.
C.1.2 ANNOTATION PROCESS
Each story in Lale is meticulously annotated with the following elements:
Story Title and Souree: Including attribution for classical or original content.
Full Narrative Text: Cleaned and standardized for consistency.
Character List:
Detailed descriptions of key characters, their appearances, and roles.
Visual Logic Chains: Structured annotation of critical events using triplets in the format (action, result, importance weight), capturing causal dependencies and expected state changes.
Difficulty Tag: Each story is labeled as Easy, Medium, or Hard, based on:
Easy: 1-2 characters, straightforward interactions, simple causal links.
Medium: More than characters, plot twists, moderately complex interactions.
Hard: Multiple characters, abstract events, non-liness storytelling, or temporal jumps.
"id":
3
"level": "easy",
"title": "The Crow and the Pitcher", "source"; "Acsop's s Fables",
5
"story Eitorryoutline". "Under the sccrching sun, across a vast, parched land...",
"character_listt list"; ["crow",, "causal_evee Caausal_even__chain":
"action": "Crow tries to drink water fails", "result"; "Crow looks for a solution", "weight"; 0.3
12
13
"action": "Crow picks up pebbles and drops them into the bottle", " "result"; "Water level rises", "weight": 0.5
19
20
"action": "Water level reaches the top", "result"; "Crow drinks the water", "weight": 0.2
C.1.3 QUALITY CONTROL
All annotations were performed by experienced annotators with backgrounds in storytelling, visual arts, and education. A multi-phase validation process was conducted to ensure:
Annotation Accuracy:
Cross-checking by multiple annotators.
Logical Soundness: Ensuring causal chains are coherent and interpretable.
Visual Plausibility: Verifying that the story could be visually realized multi-image
sequence.
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C.1.4 DATASET PURPOSE
LogicTale is intended both training resource for enhancing logic-aware story visualization models and a benchmark for evaluating models in terms of visual logic, consistency, and storytelling quality.
C.2 EVALUATION DETAILS
C.2.1 AUTOMATIC EVALUATION
We design a comprehensive automatic evaluation framework that covers both visual logic consistency and visual aesthetics. Specifically, we adopt the following six metrics:
Instance Consistency: We employ a Large Language Model (LLM) to assess the consistency of key elements across the image sequence. We designed the following prompt to guide the LLM in evaluating the consistency of key elements (characters, objects, scenes) across the image sequence:
You are given a story and a sequence of images representing different moments from the story. Your task evaluate whether the same characters, key objects, and environments appear consistently and coherently throughout the images. Please assess the overall instance consistency using the following scale:
1 Poor: Severe inconsistencies; characters, objects, environments change drastically without narrative justification.
2 Fair: Multiple inconsistencies present; noticeable attribute appearance
shifts that harm understanding.
3 Good: Minor inconsistencies; small differences appearance or object
details, but the overall coherence mostly maintained.
4 Very Good: Mostly consistent with only subtle or hard-to-notice differ- ences.
5 Excellent: Fully consistent; characters, objects, and environments visually stable and coherent across all frames.
Please provide the rating and a brief justification.
Narrative Causality: To evaluate key event causality, we adopt a Visual Question Answering (VQA) ((QA)-basee based strategy. For each annotated key event in the dataset, we formulate specific questions that probe the causality (e.g.. "Does the wolf fall into the pot after climbing the chimney?") )"). The answers generated by the model are compared to the ground truth, and the score is aggregated as the average accuracy over all key causal questions.
3. Story Readability: We adopt two-step approach. First, image captioning model (e.g., BLIP-2) generates a textual description for the entire image sequence. Then, the L.M is provided with the story's character list and the generated captions and is tasked infer the overall story plot. The inferred story is compared to the original story text using text similarity scores computed via CLiP-based embedding similarity.
4. Aesthetic Score: We utilize HPSv2 to automatically evaluate the aesthetic quality of each generated image. The overall score is obtained by averaging the per-image scores across the sequence.
5. Style Consistency: We adopt DINOv2 compute the visual embeddings of all images in sequence and measure the pairwise cosine similarity. The higher the average similarity, the more stylistically consistent the image sequence is considered.
6. Character Expressiveness: An LLM is tasked to rate the appropriateness of character ex- pressions and actions with respect to the narrative. The model observes the image sequence and is instructed to assign score from I (poor) to 5 (excellent) based how well the characters' emotions, gestures, and poses align with the story's development. We designed the following prompt to guide the L.L in evaluating the expressiveness of characters across the image sequence:
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You are given story and sequence of images representing different moments from the story. Your task is to evaluate whether the characters' emotions, gestures, and body language are clearly conveyed and appropriate the narrative context. Please assess the overall character expressiveness using the following scale:
Poor: Characters appear expressionless or with irrelvant/unintelliibbbe
expressions; emotional intent is entirely unclear.
2 Fair: Characters show limited or inconsistent expressions; emotions are
weakly conveyed and often mismatched with the story context.
3 Good: Characters display some relevant expressions or gestures, but
emotional clarity is only partially achieved.
4 Very Good: Characters are generally expressive and aligned with the narrative; only minor ambiguities remain.
Characters are highly expressive; emotions, gestures, and body language are vivid, coherent, and fully aligned with the story context.
5 Excellent:
Please provide the rating and a brief justification.
C.2.2 HUMAN EVALUATION PROTOCOL
To complement automatic evaluation, we conducted structured human study across four evaluation dimensions. Thirty annotators participated, each being shown the full story text and the corresponding generated image sequences from eight different methods. All tasks were randomized to avoid ordering bias. Below, we detail the protocol for each dimension.
Instance Consistency (1-5). Measures whether the same character entity maintains consistent appearance across the story sequence. Annotators were instructed with the following prompt:
Across the entire image sequence, the main characters entities visually consistent in terms of identity, clothing, and major attributes? Please provide a score from following the scale below, and brief justification:
1 Poor: Severe inconsistencies; characters, objects, environments change drastically without narrative justification.
2 Fair: Multiple inconsistencies present; noticeable attribute or appearance
shifts that harm understanding.
Good: Minor inconsistencies: small differences in appearance or object
details, but overall coherence is mostly maintained.
4- Very Good:
Mostly consistent with only subtle or hard-to-notice differ-
ences.
5 Excellent: Fully consistent; characters, objects, and environments remain visually stable and coherent across all frames.
Narrative Causality (binary judgment). Since Logic Tale provides ground-truth causal chains, we designed VQA-style evaluation where annotators were asked causal questions derived from dataset annotations. For each causal pair (action - resulr), we generated prompts such as:
this story, after [action], does [result] happen in the images? (Yes/No)
Scores were aggregated as the percentage of correctly satisfied causal relations.
Story Readability (1-5), Reflects how well the story can be understood solely through the image sequence. Annotators were instructed with the following prompt:
If you only look at the images without re-reading the text, how clearly can you understand the intended narrative progression? Please provide score from I to 5, following the seale below, and a brief justification:
Poor: Completely confusing; the story impossible to follow.
Fair: Very unclear; only fragments of the story are understandable.
3 Good: Partially clear; the overall idea is somewhat recognizable but many gaps remain.
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4 Very Good: Mostly clear; only minor ambiguities remain.
5 Excellent: Very clear; the story is easy to follow without text.
Aesthetic Appeal (1-5). Captures perceptual quality, including realism, visual attractiveness, and stylistic coherence. Annotators were instructed with the following prompt:
Considering the image sequence whole, how visually appealing and stylistically coherent are the generated images? Please provide a score from to 5, following the scale below, and brief justification:
1 Poor: Very low quality; unrealistic and inconsistent images.
2 - Fair: Low quality; distracting artifacts or mismatched styles.
3 Good: Moderate quality; acceptable but with noticeable flaws.
4 Very Good: High quality; visually appealing with only minor issues.
5 Excellent: Excellent quality; highly realistic and aesthetically pleasing.
Each image sequence was independently evaluated by least 5 annotators, and the final score was computed by averaging the ratings.
C.3 ABLATION STUDY ON LOGICTALE: DATASET SCALE AND EVALUATION STABILITY
To evaluate whether a relatively small but high-quality dataset like LogicTaae can yield stable and discriminative evaluation, we conduct a saturation analysis across subsets of increasing size. Specifically, we construct incremental subsets D12 C D24 C ... C Doo,, maintaining a balanced distribution of story difficulty and topic. For each subset, we evaluate all competing models using key automated metrics such as visual logic coherence and story understanding similarity.
We analyze the consistency of model rankings, measured using Kendall's Tau correlation against the full 60-story set. Figure 12 shows that the model rankings stabilize as the subset size increases. This demonstrates that even with 60 carefully curated stories, Logic'Tale provides a reliable and discriminative benchmark for visual logic assessment.
Instance Corsistency
Norronive Cousality
Readability
1.0
1.0
31
Supwer 3bre
Subbet Stow
Sabeer 2be
(a) Instance Consistency
(b) Narrative Causality
(c) Story Readability
Anvilbetic
Corsisarry
Churecter Exprass
1.0
0.5
3.8
0.0
SystAC 546
Su.bart Stoe
Sa0590 54x
(d) Aesthetic Quality
(c) Style Consisteney
(f) Character Expressiveness
Figure 12: Kendall's Tau correlation between model rankings on full dataset (60 stories) and various dataset subsets across six evaluation metries. Results indicate high ranking consistency with subsets as small as 36 stories, validating the reliability of LogicTale for evaluation purposes.
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Dataset
#Samples
Logic Annotation
Evaluation Mode
StoryDiffusion
20
X
Single-image
ConsiStory
20
X
Single-image
Agentoryygget
100
X
Single-image
MovieAgent
12
X
Single-image
ViStoryBench
80
X
Single-image
LogicTale (Ours)
60
Multi-image joint
Table 7: Comparison of LogicTale with existing story visualization benchmarks. Unlike prior datasets, Logic'Tale provides explicit causal logic annotations and supports multi-image joint evaluation, enabling systematic assessment of narrative coherence.
C.4 DATASET COMPARISON
As shown Table 7 existing datasets for story visualization are limited in both scale and scope: most consist of short, single-character handerafted narratives, lack logical annotations, and restrict evaluation to single-image fidelity or alignment. In contrast, Logic'Tale the first dataset that explicitly incorporates causal logic annotations, enabling systematic evaluation of story-level reasoning, Furthermore, we design a suite of automatic multi-image evaluation metrics that go beyond local image quality to assess cross-image semantic consistency and narrative eausality, providing more rigorous foundation for advancing logic-aware story visualization.
D LOGICSTORY FRAMEWORK DETAILS
D.1 LOGIC-AWARE MULTI-AGENT SYSTEM
To generate structured and logic-aware visual narratives, LogiStory employs a multi-agent planning system composed of three agents: SceneCrafter, LogicMiner, and ShotPlanner. These agents work in sequence to analyze the story text and construct an intermediate story plan, consisting of characters, key events, and frame-level scene descriptions.
D.1.1 SCENECRAFTER
The SceneCrafter agent is responsible for extracting the visually relevant components of the story, including characters, key objects, and environmental settings. Unlike conventional entity extraction, we ask the model to infer and provide visual attributes such as appearance, size, texture, and emotional tone. This provides strong guidance for subsequent visual generation.
Prompt Example:
You visual designer tasked with preparing for story illustration. Given short story, identify and describe the following elements with visual details:
Characters: List each character. For each, provide name and short description of their physical appearance, clothing, emotional traits, and any distinguishing features.
Key Objects: Identify important objects mentioned or implied in the story. Describe their size, material, and visual appearance (e.g., color, texture).
Scene Locations: Identify each main scene or background. Describe the environment, including style (c.g., cartoon, watercolor), atmosphere (c.g., cozy, ominous), and key visual elements (c.g., forest path, house made of straw).
Your output should be structured in three sections: Characters / Key Objects Scene Locations. Be as visual as possible.
Story: "The three little pigs leave their mother to build their own houses. The first pig buys straw,
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the second buys wood, and the third buys bricks. Each builds house from their chosen material."
Expected Output Format:
Characters:
Pigl: small, cheerful piglet wearing a blue shirt. Has round eyes and a playful smile.
Carries bundle of straw.
Pig2: Medium-sized pig with green cap and suspenders. Appears cautious and focused. Often seen holding wooden planks.
Piig: Slightly larger pig with glasses and red scarf. Looks serious and thoughtful. Holds bricks in neat stack.
Mother Pig: A kind, elderly pig wearing an apron and bonnet. Stands at the cottage door waving goodbye.
Wolf: A tall, menacing wolf with gray fur, sharp eyes, and a mischievous grin. Often lurking in the background.
Key Objects:
Straw bundle:
Light yellow, loosely tied, lightweight with uneven edges.
Wood planks: Brown, sturdy, rectangular with visible grain texture.
Brick stack: Dark red, neatly aligned bricks with mortar stains.
Boiling pot: Large iron cauldron with steam rising from it, placed over firewood.
Scene Locations:
Forest path: Gentle trail surrounded by tall green trees, soft lighting, storybook cartoon style.
Pigl's straw house: Small, straw-covered hut with slanted roof. Cozy but fragile appearance.
Pig2's wood house: Medium-sized cabin with wooden logs and chimney, Structured and rustic.
Pig3's brick house: Solid and square, with tiled roof and brick walls. Brightly lit and secure.
Mother's home: warm cottage with a garden, classic watercolor storybook feel.
This visually grounded output equips the system with rich contextual signals, serving as scaffold for logic mining and shot planning in downstream modules.
D.1.2 LOGICMINER
The LogicMiner agent extracts sequence of causally and temporally significant events from the story text. Each event is represented using a structured 4-tuple format (actor, action, target, result), capturing both physical interactions and narrative consequences. This formalization ensures fine- grained reasoning and facilitates downstream consistency checking and scene planning.
Prompt Example:
You story logic analyst. Given short narrative, your task is extract the key events that define the causal and temporal structure of the story. For each event, output structured record in the format: (actor, action, target, result) Where:
actor the entity initiating the event.
action is the main verb activity.
target is the object or entity affected by the action.
result describes what changed in the world due this event.
Your output should be chronological list of events. After each event, also specify:
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Preconditions: What must be true before the event occurs.
Effects:
What changes after the event (state change).
"The first little pig buys straw and builds house. The wolf comes and blows it down. The pig runs to the second pig's s wooden house. The wolf follows and destroys that house too. Both pigs escape to their brother's s brick house. The wolf tries to enter through the chimney but falls into a pot of boiling water."
Story:
Expected Output Format:
1. (Pigl, buys, straw, Pigl owns building material) Preconditions: Straw is available for purchase. Effects: Pig/ has straw.
2. (Pigl, builds, house, Straw house exists)
Preconditions: Pigl owns straw.
Effects: Straw house appears in the scene.
3. (Wolf, blows down, straw house, House destroyed, Pigl is homeless)
Preconditions: Straw house exists. Wolf is nearby.
Effects: Straw house is destroyed. Pigl must flee.
4. (Pigl, runs Pig2's house, Pig1 and Pig2 are together) Preconditions: Pigl is outside, Pig2's 's house is standing. Effects: Pigl is inside Pig2 house.
5. (Wolf, blows down, wood house, House destroyed, Pigl and Pig2 flee) Wood house exists. Wolf is nearby.
Preconditions:
Effects: House destroyed. Two pigs away.
6. (Pigl and Pig2, escape to, Pig3's house, All pigs are in brick house) Preconditions: Pig3's 's house intact.
Effects: All three pigs are together in brick house.
7. (Wolf, climbs, chimney. Wolf attempts to enter house)
Preconditions: All doors and windows are shut. Effects: Wolf is inside the chimney.
8. (Pigs, boil, water, Boiling water is prepared) Preconditions: Pot and fire are present.
Effects:
Trap for the wolf is ready.
9. (Wolf, falls into, boiling pot, Wolf is defeated) Preconditions: Wolf is in chimney. Pot is boiling.
Effects: Wolf burned and flees. Pigs are safe.
This structured output enables later modules to verify logical alignment across frames and ensures causal coherence in the resulting visual sequence.
D.1.3 SHOTPLANNER
The ShotPlanner agent bridges structured event logic and visual storytelling. Given the original story text and the event list extracted by the LogicMiner. it generates sequence of shot plans. Each shot plan specifies the visual composition for an image, including characters, actions, objects, spatial relations, scene context, and camera parameters (e.g., angle, shot type, distance). The planner also produces rendering prompt Stable Diffussion format for each image.
To enhance narrative clarity and visual engagement, ShotPlanner incorporates principles of visual storytelling conventions, such as:
Pacing: Allocate longer visual emphasis high-impac events (e.g., conflict, climax).
Framing: Use wide, medium, close-up shots vary focus and emotional tone.
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Perspective:
Adjust camera angle and viewpoint to highlight relationships, danger,
tension.
Input:
Story text: A paragraph-length narrative.
LogicMiner event list: A chronological list of (actor, action, target, result) tuples.
Output (for each frame):
Shot Plan:
Scene Description: Natural language summary of what happens in the shot.
- Key Elements: {characters, actions, objects, spatial layout, background)
Camera Setup: (shot (e.g., wide, medium, close), angle (e.g., eye-level, low-angle), focal length)
Rendering Prompt (Stable Difffusion format): A concise visual prompt used for genera- tion.
Prompt Template:
You are visual story director. Given the following story and the structured list of events, your task is to design sequence of image shots that visually depict each event in narratively coherent and aesthetically pleasing way. For each event, provide:
1. Scene Description:
What is happening in the scene?
2. Characters and Actions: Who is doing what?
3. Objects and Scene Elements: What objects environment features involved?
4. Spatial Layout: Where is each character/object located in the scene?
5. Camera Parameters:
Shot Type: (c.g., wide shot, over-the-shoulder close-up)
Camera Angle: (e.g., eye-level, high-angle, low-angle)
Perspective: (e.g., character perspective, bird's eye)
Rendering Prompt (Stable Diffusion Style): Include:
scene setting and characters
emotional tone
composition and angle
style (e.g., children's book illustration, watercolor)
Story: "The first little pig builds straw house. The wolf blows it down. The pig runs away. The second pig builds wooden house..." "
Events:
(Pigl, builds, house, Straw house exists)
(Wolf, blows down, straw house, House destroyed)
Example Output:
Shot 1:
Scene Description: Pigl is happily finishing the construction of a straw house in sunny field.
Characters and Actions: Pigl is placing the final straw on the roof.
Objects and Scene Elements: Straw house, piles of straw, a hammer.
Spatial Layout: Pig1 stands in front of the straw house, facing the viewer.
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Camera Parameters:
Shot Type: Medium shot
Camera Angle: Eye-level
Perspective: Third-person perspective
Rendering Prompt (Stable Diffussion:: "A cheerful pig building a straw house in a sunny meadow, children's book illustration style, medium shot, eye-level angle. bright and warm color palette, watercolor texture, detailed background with green trees and blue sky"
Shot 2:
Scene Description: The wolf is blowing down the straw house while Pigl watches in fear.
Characters and Actions: Wolf is exhaling forcefully; Pig covering its face.
Objects and Scene Elements: Straw flying, partially collapsing house.
Spatial Layout: Wolf on left, Pig1 on right, house between them.
Camera Parameters:
Shhot Type: Wide shot
-amera Angle: Slight low-angle to emphasize action
Perspective: Dynamic third-person
Rendering Prompt (Stable Diffusion): "A fierce wolf blowing on a fragile straw house while a scared pig watches, straw flying everywhere, wide shot, low angle, dramatic lighting, children's 's book illustration, vivid cartoon style"
This module ensures that the resulting image sequence not only aligns with the logical events but also delivers a cinematic and emotionally engaging viewing experience, tightly coupling narrative rhythm with visual coherence.
D.2 VISUAL LOGIC ENHANCEMENT MODULE
D.2.1 LOCAL CAUSAL MONITOR
The Local Causal Monitor simulates a human-like reading experience by incrementally assessing the logical consistency of each image story sequence, conditioned on prior visual context and world knowledge. Unlike global verification, which evaluates the entire story retrospectively, this module performs step-by-step causal validation during sequence unfolding, modeling the linear comprehension process of human readers.
Core Design: At each timestep the monitor:
1. Maintains Memory Buffer M,-1 summarizing all previously observed visual content and inferred world states.
2. Parses the current image 1, into a structured caption or event description using an image captioning or event extraction model.
3. Evaluates whether I logically compatible with M,-1 using large language model (LLM).
4. Assigns causal consistency score E [0, 1] representing the degree of logical alignment.
5. Updates the buffer M, by incorporating the new information from I.,
This mechanism enables fine finennraieed monitoring of temporal consistency, causal progression, and state transitions across a story sequence.
Memory Buffer Example:
Initial Story: "Three little pigs each build a house using straw, wood, and bricks. A wolf tries to blow each house down."
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Memory after Image (Pigl builds straw house): "Pigl has constructed a straw house in an open field. No threats have appeared yet. Other pigs are not present."
Memory after Image 2 (Wolf blows down straw house): "Pigl's straw house has been destroyed by the wolf. Pigl frightened and escapes. The wolf is now active in the story.
Image 3 (Current); Pig2 is shown relaxing in completed wooden house, unawarc of any danger.
Question: Based the current memory and this image, is the event causally consistent with the story flow?
LLM Evaluation Prompt:
You are a causal reasoning expert.
Given: The memory of previously observed story events The current image description
Evaluate whether the current image is logically consistent with prior context, considering: 1. Whether the sequence of events makes causal sense 2. Whether character behavior is appropriate given past events 3. Whether any contradictions unexplained jumps occur
Return numerical consistency score between 0 (completely inconsistent) and (fully consistent), along with brief justification.
Memory Buffer: Pigl's straw house was destroyed by the wolf. Pig1 ran away. The wolf now active in the story.
Current Image Description: Pig2 is relaxing newly built wooden house, smiling. No signs of the wolf or alarm.
Output Format: Score: <float between 0 and 1> Justification:
sentences>
Example Answer: Score: 0.8 Justification: The scene is mostly logical. Pig2 may not yet be aware the wolf's actions, which explains the relaxed demeanor.
Scoring Interpretation:
Score 1.0: Full causal alignment with prior context
Score [0.7, 0.9]: Minor temporal gaps ambiguity, still plausible
Score [0.4, 0.6]: Noticeable logical inconsistencies, partially recoverable
Score < 0.4: Major contradiction or missing transitions
This local monitor provides frame-level causal validation and supports training or evaluation by detecting inconsistencies early during visual narrative generation.
Threshold Calibration for Image Refinement. To determine the appropriate response for each generated panel Pt during story visualization, we introduce two thresholds, тı and 72. which guide the image refinement process based on the normalized logic consistency score sp(,, MM-1)). This score integrates outputs from both the Local Causal Monitor and the Global Causal Verifier, representing the confidence that p. aligns with the intended narrative logic.
We define:
71: the upper bound below which the image is considered logically invalid and must be regenerated.
T2: the lower bound above which the image considered logically acceptable, requiring no refinement.
[T, T2]: range indicating partial alignment, where the image is passed through a refine- ment stage using inpainting M-bsed editing.
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To empirically determine these thresholds, we conducted calibration study held-out validation set:
1. A diverse set of generated panels was sampled from stories of varying complexity.
2. Human annotators rated the logical alignment of each panel with the narrative on a 0-5 Likert scale.
3. The corresponding logic scores v were collected from our verifier modules,
4. A distributional analysis was performed align human judgments with tb.
As result:
7i was to 0.4, covering the 90th percentile of panels rated below (logically flawed).
was set to 0.7, corresponding the 10th percentile of panels rated above 4 (logically sound).
This calibration ensures that the refinement mechanism is grounded in numan-perceinn narrative coherence, aligning automated validation with qualitative standards.
D.2.2 GL.OBAL CAUSAL VERIFIER
The Global Causal Verifier is designed ensure global narrative coherence throughout the visual story generation process. It constructs and leverages high-level Causal Graph that models the dependencies between key events, character/object states, and their temporal transitions, enabling comprehensive evaluation and refinement across the entire sequence.
Causal Graph Construction: We first construct directed Global Causal Graph G E) from the story text and key events extracted by the Logi cMi ner. Each node v; E V represents distinct story state event, and each edge eij E E denotes causal temporal dependency such
Event causality: "Pig builds house" "Wolf tries to blow it down"
State evolution: "Pot placed under chimney" - "Wolf falls into pot"
Implicit physics: "Stones added to pot" + "Water level rises"
Each node is annotated with involved characters, object states, spatial relations, and expected visual outcomes.
During generation, we maintain State Recorder S, that tracks which portion of the global story has been visually realized up to timestep t. It summarizes observed character locations, object configurations, known outcomes, and remaining events.
State Recorder:
For example:
At 3 (third image): "Pigl's house destroyed, Pigl escaped, Pig2 building house, wolf seen approaching"
This enables the verifier to identify mismatches missing transitions between the current visual state and expected causal flow.
Refinement Instruction Generation:
At each step, the current image I is parsed into structured scene description (e.g., via captioning or scene graph parsing). The verifier then checks for:
Causal Alignment: Does / align with the next expected node in G?
State Progression: Are object/character states consistent with prior evolution?
Implicit Logic: Does 1, reflect necessary physical/visual reasoning (c.g., gravity, contain- ment)?
If misalignment detected, refinement instruction is generated using a language model, aimed correcting the image in the next iteration.
Refinement Prompt (LLM))
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You visual reasoning assistant.
Given: The global causal graph for the story The current generation state (St) The current image description The next expected story event
Determine whether the current image faithfully represents the expected event. If not, generate concise instruction for image refinement, focusing on correcting logic or state alignment.
Example:
Expected Event: "Wolf climbs chimney, pigs prepare boiling pot"
Current Image Description: "Wolf near house, pigs inside, pot missing"
Instruction: "Add boiling pot beneath chimney. Show pigs anxiously watch- ing chimney."
This refinement instruction is passed to the visual generation module for image editing regeneration, ensuring both visual fidelity and narrative coherence. By integrating top-down symbolic planning with bottom-up visual verification, the Global Causal Verifier enforces long-range consistency and supports correction of subtle causal gaps that may not be captured at the local level.
E EXPERIMENT COMPUTE RESOURCES AND SETTINGS
All experiments conducted workstation equipped with two NVIDIA A6000 GPUs (48GB VRAM each). All baseline models, including StoryDiffusion and ConsiStory, were used with their officially released checkpoints and settings for fair comparison. No additional fine-tuning was performed on these models unless specified.
F DISCUSSION
F.1 ADVANTAGES
Our proposed framework LogiStory and the curated dataset LogicTTae offer multiple advantages to the field of visual storytelling and beyond:
Explicit Modeling of Visual Logic. Unlike prior approaches that implicitly rely on latent representations, LogiStory explicitly models visual deginedefine as the perceptual and causal coherence among characters, actions, and object states thhrough a structured multi-agent planning mechanism and logic-aware verification modules. This improves not only the factual correctness of individual images but also their temporal and narrative coherence.
2. Fine-Grained Story Understanding. Through the SceneCrafter, Logi cMiner, and ShotPlanned agents, our system decomposes stories into interpretable components (e.g.. char- acters, key events, spatial relations, and camera instructions), enabling transparent, controllable, and explainable generation. This opens the door for deeper interaction and analysis in multimodal generation.
3. Logic-Aware Image Refinement. By integrating both Local Causal Monitor and Global Causal Verifier, LogiStory simulates human-like reading and comprehension processes, identifying inconsistencies both at the fine-grained image level and the story-wide causal structure. This dual- level feedback loop guides generation toward visually and narratively coherent outcomes.
4. LogicTTae Dataset Structured Benchmark. LogicTale provides rich annotations including causal graphs, key events, object states, and visual prompts, serving as a high-quality benchmark for evaluating visual logic in story generation. It supports multiple evaluation paradigms automatic met- rics, human judgment, and logic tracing-aan and can foster the development of explainable multimodal models.
paradigms-automatiee
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5. Generalizability Beyond Story Visualization. While work focuses the multi story visualization task, the underlying principles visual logic modeling and logic-aware planning generalize naturally to related areas such
Video Generation: Ensuring temporal consistency and causal flow in generated frames.
Interactive Narrative Systems: Providing feedback for story-based games or simulations.
Multimodal Planning and Robotics: Translating language instructions into logically consistent visual action sequences.
This makes LogiStory versatile foundation for broader multimodal reasoning and generation tasks.
F.2 LIMITATIONS
Despite its strengths, our approach presents several limitations that highlight future research direc- tions:
1. Scalability to High-Entity Scenarios. When the story involves large number of characters, objects, and complex interdependencies, maintaining consistent identity, state, and spatial logic across frames becomes increasingly difficult. This reflects a broader limitation in current generative models, even state-of-the-art models such as GPT--o struggle guarantee visual identity preservation and detailed multi-object reasoning during image editing or generation.
Framework Complexity. Our framework adopts a modular multi-agent design that allows flexibility, but it also introduces additional computational steps compared simpler pipelines. These trade-offs reflect our emphasis on interpretability and logical rigor, and we believe they open avenues for future refinement rather than fundamental limitations of the approach.
G USAGE OF LLMs
Large language models (LLMMs) were used solely aid to polish the language and improve the clarity of exposition. They were not involved research ideation, problem formulation, method- ology design, experimental implementation, analysis of results. All scientific contributions and claims are the work of the authors.
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Reviews:
[{"review_id": "hz3joNIzLr", "reviewer": "Reviewer_oUdB", "summary": "This paper introduces the concept of \"visual logic\" to address the challenge of generating causally and perceptually coherent visual story sequences. The authors define visual logic as the perceptual and causal coherence among characters, actions, and scenes over time. To tackle this, they propose `LogiStory`, a framework composed of two main parts:\n1.  **Logic-Aware Multi-agent System:** This system performs structured narrative planning. It uses three agents (`SceneCrafter`, `LogicMiner`, `ShotPlanner`) to decompose a story text $S$ into character/object definitions $E$, extract a causal chain of key events $K$, and plan a sequence of detailed visual shot specifications $P = \\\\{p_1, ..., p_T\\\\}$.\n2.  **Visual Logic Enhancement Module:** This module enforces coherence during the generation of the image sequence $I = \\\\{I_1, ..., I_T\\\\}$. It includes:\n    *   A **Local Causal Monitor** that checks step-by-step narrative plausibility by calculating a causal score $\\\\psi_t = C_p(I_t | M_{t-1})$ based on a memory buffer $M_{t-1}$.\n    *   A **Global Causal Verifier** that uses a story-level causal graph $G_{causal}$ to validate state transitions and guide an image refinement process based on predefined thresholds $\\\\tau_1$ and $\\\\tau_2$.\n\nTo facilitate development and evaluation, the authors construct `LogicTale`, a new benchmark of 60 stories with rich annotations for causal events. They also propose a comprehensive evaluation protocol that measures \"visual logic\" and perceptual quality. Experiments show that `LogiStory` significantly outperforms strong open-source and closed-source baselines, particularly in metrics related to narrative logic and readability.", "questions": "1.  **On Evaluation and Potential Bias:** Did you experiment with different families of MLLMs for evaluation (e.g., using an open-source model to evaluate proprietary model outputs) to test the robustness of the evaluation scores?\n2.  **On the `LogicMiner`'s Robustness:** The `LogicMiner` agent is crucial for constructing the causal graph. How robust is its capability to infer implicit events, especially for subtle logic? What are its common failure modes, and how does the framework handle an incorrect causal graph?\n3.  **On Verifier Interaction:** In Table 3, the `Instance Consistency` score for \"Ours (w/ local)\" is 4.24, while for \"Ours (w/ both)\" it is 4.23. This is a minor, perhaps insignificant, difference. Could you offer any insight into why adding the `Global Causal Verifier` might not strictly improve this specific metric?\n4.  **On the Refinement Process:** Regarding the image refinement step triggered when $\\\\psi_t \\\\in [\\\\tau_1, \\\\tau_2)$, could you provide statistics from your experiments on the frequency of this action (accept vs. refine vs. regenerate)? What is the computational overhead of this step compared to a simple regeneration?\n5.  **On Scalability:** How do you envision the framework scaling to more complex narratives with many characters, parallel plotlines, or non-linear temporal structures? Would the current causal graph representation be sufficient?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  **Novel and Important Problem Formulation:** The paper's primary strength is its formalization and focus on \"visual logic.\" This provides a clear, principled target for a key failure mode of current generative models—the lack of narrative and causal coherence in sequences.\n2.  **Well-Designed and Interpretable Framework:** `LogiStory` is a thoughtfully engineered system. The multi-agent planner provides an interpretable intermediate representation, and the dual-level verification (local and global) robustly enforces logical consistency.\n3.  **Comprehensive and Rigorous Evaluation:** The experimental evaluation is a major strength. The authors compare against a wide range of powerful baselines, including top-tier commercial models, and use a combination of automatic and human studies for robust validation.\n4.  **Creation of a Valuable Benchmark:** `LogicTale` is a significant contribution in its own right, enabling the community to move beyond simple frame-level metrics and systematically evaluate the storytelling capabilities of generative models.\n5.  **Exceptional Clarity and Presentation:** The paper is written with outstanding clarity. Complex ideas are explained intuitively, and the high-quality figures and detailed appendix make the work easy to understand and reproduce.", "weaknesses": "1.  **Scale of the LogicTale Benchmark:** The most apparent weakness is the scale of the `LogicTale` benchmark (60 stories). While the authors justify this with a saturation analysis, a larger dataset would provide stronger evidence for the generalizability of the findings.\n2.  **Reliance on MLLMs for Automatic Evaluation:** The automatic evaluation protocol for core visual logic metrics relies on judgments from MLLMs, which raises concerns about potential evaluation bias (an \"LLM-evaluating-LLM\" loop). While mitigated by high correlation with human ratings, this is a methodological limitation.\n3.  **Complexity and Latency:** `LogiStory` is a multi-stage pipeline involving several LLM/MLLM calls, which likely entails significant computational cost and latency. A discussion on the framework's efficiency and robustness to component failures would be beneficial.\n4.  **Insufficient Ablation of Refinement Mechanism:** The paper states that refinement is done via \"image editing tools\" for scores in the range $[\\\\tau_1, \\\\tau_2)$. A more detailed analysis or ablation of this step (e.g., frequency of use, comparison to simple regeneration) would strengthen the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3OvVZoF0pX", "reviewer": "Reviewer_GhPS", "summary": "This paper presents LogiStory, a framework for generating multi-image visual stories with a focus on logical coherence. The authors introduce the concept of \"visual logic\" and propose a multi-agent system to plan the narrative, combined with verification modules to refine the generated images. To evaluate their approach, they construct a new benchmark, LogicTale. The paper is well-written, and the problem it addresses is both interesting and relevant to the community.", "questions": "My main questions are related to the weaknesses detailed above. In addition, I would like to ask the following:\n\n1. **Robustness of the MLLM-based evaluation metrics:**  For instance, during the VQA-based `Narrative Causality` assessment, how can we distinguish between failures of the \"evaluator\" MLLM and genuine logical flaws in the generated images? Have you conducted any analysis on the reliability of the evaluator models themselves?\n\n2.  The Global Causal Verifier relies on a pre-constructed causal graph. How does the framework handle stories with very complex, non-linear, or ambiguous causal structures?\n\nI would be open to raising my score should the authors address the concerns noted above.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  **Interesting and Important Task:** The paper successfully highlights \"visual logic\" as a critical dimension in visual storytelling. This focus enriches the task by moving beyond per-image quality to sequence-level narrative coherence, which is an important and challenging problem.\n\n2.  **Effective Framework Design:** The proposed framework, with its planning and verification stages, appears to be an effective solution to the problem identified. The qualitative results demonstrate its capability to produce more logically consistent visual narratives compared to several strong baselines.\n\n3.  **Contributions of a New Benchmark:** The introduction of the LogicTale benchmark is a valuable contribution. It provides a dedicated resource for evaluating narrative logic, and the paper's experiments on this benchmark effectively showcase the performance of the proposed method.", "weaknesses": "1.  **Limited Novelty in Core Methodology:** While the overall system is well-engineered, the primary contribution appears to be the careful orchestration of existing, powerful models (LLMs, MLLMs, and diffusion models like Flux) into a sophisticated pipeline. The novelty in terms of fundamental model architecture or learning paradigms seems limited. \n\n2.  **System Complexity and Robustness Concerns:** The framework's effectiveness seems heavily dependent on the consistent and reliable performance of each component model (e.g., the agents). As a multi-stage pipeline, it is susceptible to error propagation, where an issue in an early stage could negatively impact the entire sequence. Additionally, this cascaded design naturally incurs significant computational overhead and latency, which could be a practical concern.\n\n3.  **Fairness of Experimental Comparison:** The experiments compare LogiStory, a highly customized pipeline for this specific task, against end-to-end models like GPT-4o and Gemini. This comparison, while informative, may not be entirely fair, as the proposed method leverages the combined strengths of multiple specialized models. It would be beneficial to discuss or explore fairer comparison settings, perhaps by comparing against other modular pipelines or by breaking down the performance gains attributable to each component.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Zi14n6Yx1h", "reviewer": "Reviewer_dd8Q", "summary": "This paper addresses a significant and well-known challenge in visual sequence generation: the lack of logical and causal coherence. The authors argue that while current models excel at generating high-fidelity images, the resulting sequences often fail as narratives, presenting disjointed actions and fragmented storylines.", "questions": "- I am curious about the computational cost required to generate a story. How many refinement steps (either full regeneration or editing) are typically required per story? Does this number vary significantly with the 'Hard' difficulty stories?\n\n- How robust is this LogicMiner agent to variations in narrative style? Does it perform equally well on highly implicit causality (e.g., \"The dog, remembering his master, felt sad\") versus the explicit physical causality shown in the examples (e.g., \"crow put pebbles into the cup,\" -> \"rising water level\" )?\n\n- The baseline naming in the results is slightly inconsistent. Section 5.1 lists GPT-4-image-1 as a closed-model baseline. However, Table 1 lists GPT4o-+GPT-image-1 , and Section 5.2.1 discusses the performance of GPT-4o. Could you clarify if these are the same model, or if GPT40-+GPT-image-1 implies a pipeline using two different models?\n\n- The qualitative results are very strong. To help the community understand the current boundaries of LogiStory, would it be possible to include a qualitative example of a failure case, perhaps from one of the 'Hard' stories? This would provide valuable context for the acknowledged limitation in \"high-entity scenarios\".", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The problem proposed in this paper is convincing. The motivation of the paper is very clear. It correctly identifies a key gap in generative models—moving beyond single-image fidelity to narrative coherence. The explicit modeling of \"visual logic\" is a significant conceptual contribution.\n\n- The creation of the LogicTale benchmark is a valuable contribution to the community.", "weaknesses": "- The proposed framework is complex, involving three distinct planner agents, a global causal graph, a state recorder, a local memory buffer, and a two-stage verification loop.\n\n- The scale of the benchmark is too small to fully evaluate the method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MbxaGmmqyf", "reviewer": "Reviewer_wuEE", "summary": "This paper proposes a logicaware multi-image story visualization framework, LogiStory. The framework is built around the central innovation of explicitly modeling visual logic in story visualization. This paper also introduces LogicTale, a benchmark comprising richly annotated stories, emphasizing causal reasoning, and visual logic interpretability.", "questions": "- It was stated that \"no existing benchmark systematically measures whether a visual sequence, either image-based or video-based, successfully communicates the intended story in a logically coherent manner,\" but VinaBench[1] is a benchmark dataset that considers commonsense links and visual consistency as visual narrative generation.\n\n[1] VinaBench: Benchmark for Faithful and Consistent Visual Narratives（CVPR2025）\n\n- Will the benchmark dataset, source code, and checkpoints be made publicly available?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- This paper introduces LogicTale, a new benchmark comprising richly annotated stories based on not only classical stories but also original stories, to validate on new stories.\n- This paper also proposes a logicaware multi-image story visualization framework, LogiStory to address the challenges of visual consistency and logical coherence in a series of story visualizations by constructing a new benchmark dataset. The results show high performance of LogiStroy on LogicTale.\n- LogiStroy was evaluated on ViStoryBench-Lite to assess the generalizability of the framework. Ablation studies are also included to evaluate each module.", "weaknesses": "- The framework is composed of a complex pipeline based on a multi-agent system. \n- The evaluation dimensions using LogiStory and the focus points of the agents are closely related, which raises the possibility that the framework may be somewhat dependent on the evaluation aspects.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)