PPTBench: Can Coding Agents Reconstruct the Visual World through Structured, Editable Slides

arXiv:2609.29718 · cs.CL, cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-08-31

Updated: 2026-09-28

Project page: https://lab.einsia.ai/pptbench

Terminology

• Benchmark — 49× in this paper · explained in 2 episodes
• Alignment — 10× in this paper · explained in 1 episode
• Benchmarking — 7× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 7× in this paper
• Consistency — 6× in this paper · explained in 3 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Self-consistency — 5× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 4× in this paper
• In International Conference on Learning Representations — 4× in this paper
• Provenance — 4× in this paper · explained in 3 episodes
• Association for Computational Linguistics — 3× in this paper
• Fidelity — 3× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 3× in this paper
• Winter Conference on Applications of Computer Vision — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Chain-of-Thought Prompting — 2× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Agent Harness — 1× in this paper · explained in 2 episodes
• Agentic Workflows — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Self-Evolving Agents — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Thirty-First International Joint Conference on Artificial Intelligence — 1× in this paper

Sources

• AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ
• DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZ
• MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark
• PresentBench: A Fine-Grained Rubric-Based Benchmark for Slide Generation
• MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
• Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization
• Design First, Code Later: Aesthetically Pleasing Template-Free Slides Generation
• AutoPresent: Designing Structured Visuals from Scratch
• PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion
• Paper2SysArch: Structure-Constrained System Architecture Generation from Scientific Papers
• SciFig: Towards Automating Editable Figure Generation for Scientific Papers
• COLE: A Hierarchical Generation Framework for Multi-Layered and Editable Graphic Design
• FigureQA: An Annotated Figure Dataset for Visual Reasoning
• Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
• Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset
• VL-RewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models
• DiagramEval: Evaluating LLM-Generated Diagrams via Graphs
• Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers

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