EditPPT is a multi‑agent framework that turns slide editing into a constrained tool‑selection task, using the native PowerPoint COM interface to perform localized shape‑level operations. By separating validation across modalities, its dual‑modal validators assess both instruction fidelity and visual quality, achieving high execution and accuracy rates even on long decks. The authors also introduce DeckEdit‑Bench, a benchmark of 28 human‑authored decks with 582 slides and 183 editing prompts across varying deck lengths.
arXiv:2512. 03042v3 Announce Type: replace-cross Abstract: We introduce PPTArena, a benchmark for PowerPoint editing that evaluates how agents modify real slides from natural-language instructions.
SLIDEFORGE is a new AI agent designed for controllable editing of presentation slides. It constructs a Deck State Graph that links visual decomposition, native PowerPoint object structure, and perceptual organization, enabling theme‑preserving reconstruction through slide‑native operations and rendered‑state verification. The authors also propose an evaluation framework that jointly measures component recovery, preservation, restyling consistency, visual quality, and native editability, and demonstrate that SLIDEFORGE outperforms existing prompting, screenshot‑based, and generic code‑agent baselines.
SLIDEFORGE is an LLM‑driven agent designed for controllable editing of slide decks while preserving layout, style, component structure, and native editability. It constructs a Deck State Graph that links visual decomposition, PowerPoint object structure, and perceptual organization, enabling theme‑preserving reconstruction through slide‑native operations and rendered‑state verification. The authors also propose a comprehensive evaluation framework measuring component recovery, preservation, restyling consistency, visual quality, and native editability, and demonstrate that SLIDEFORGE outperforms direct prompting, screenshot‑based agents, and generic code‑agent baselines.
arXiv:2610.14253v2 Announce Type: replace Abstract: Slider-based control aims to give users continuous, fine-grained control over the intensity of individual edits in instruction-based image editing....
PPTBench is a new benchmark that tests coding agents’ ability to reconstruct scientific flow diagrams from arXiv papers into editable PowerPoint slides. The dataset contains 500 tasks, each requiring agents to produce a single PPTX page with native, editable objects, and a four‑stage Agentic Judge evaluates validity, semantic correctness, rendering quality, and fine‑grained visual quality. Across 31 model configurations, the best score is 67.80, with a median of 19.47, showing that while agents can generate valid PPTX files, they still struggle with semantic and visual accuracy, especially text details.
ReDeck introduces a step‑level render‑grounded refinement framework for document‑to‑slide generation, breaking slide revision into atomic edit actions with immediate renderer‑derived observations. It employs multi‑granular feedback—step‑level spatial checks, turn‑level adaptive critique, and a submission‑level layout gate—to balance local repair with overall quality. The authors also present DeckQuiz, a benchmark that separates content fidelity, spatial correctness, and design quality, and demonstrate ReDeck’s superior performance across GPT‑5.4, Claude‑4.6, and Gemini‑3.1.
arXiv:2601.09487v2 Announce Type: replace Abstract: The rapid evolution of Large Language Models (LLMs) has fostered diverse paradigms for automated slide generation, ranging from code-driven layouts...
arXiv:2610.16784v1 Announce Type: cross Abstract: Generating and editing presentation slides requires models to combine accurate text rendering, instruction following, visual grounding, aesthetic des...
arXiv:2608.17566v2 Announce Type: replace Abstract: The quality and diversity of instruction-based video editing datasets are steadily improving, yet existing datasets mainly focus on single editing...
arXiv:2606. 08415v1 Announce Type: cross Abstract: While recent text-guided video editing models excel at elementary tasks (e.
WeVisCut is a long‑form script‑to‑video system that unifies narration, editing, and tool execution into a single agent. It uses a four‑stage data pipeline that reconstructs editing intent from finished videos, generating over 130K training examples to train a 9B policy capable of coordinating production and recovering from failures. The system introduces a 100‑task benchmark with five metrics, outperforming its base model, matching Claude Opus 5 in performance while using far fewer tokens, and surpassing task‑matched native editors on the proposed metrics.
OmniEdit-Bench introduces a comprehensive benchmark for instruction-based video editing (IVE), addressing limitations of existing datasets by covering spatial, temporal, audio, and reference-based editing tasks and distinguishing explicit from implicit instructions. The evaluation framework assesses editing quality across accuracy, preservation, realism, and consistency, using human judgments and vision-language models, and incorporates an accuracy-aware penalty to ensure instruction fidelity. Experiments reveal that current IVE models perform poorly, highlighting the need for improved methods.