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.
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: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.
CoinVE-200K is a large, high‑quality dataset for compositional instruction‑guided video editing, featuring 1080p video‑editing pairs up to 201 frames long and containing 2–5 atomic editing operations per sample. The dataset covers diverse editing intents—targeting humans, objects, and backgrounds with addition, removal, modification, and stylization—while ensuring instruction faithfulness, visual quality, temporal consistency, and compositional diversity through a careful generation and filtering pipeline. CoinVE-Bench benchmarks these capabilities, and CoinVE-Edit, a 22B model built on Wan2.1‑T2V‑14B and Qwen3‑VL‑8B‑Instruct, demonstrates strong performance in instruction following, compositional editing accuracy, visual quality, and temporal consistency.
arXiv:2606. 31154v1 Announce Type: cross Abstract: Creating and editing slides is a rich, multimodal activity that is ubiquitous in professional and educational settings, making it an ideal testbed for real-world computer-use agents.
Commercial design platforms increasingly edit documents through large language model (LLM) agents, but two practical problems block reliable deployment: legacy document formats expose only \emph{flat}...
ACE is a self‑correcting agentic canvas editor that operates on a hierarchical scene‑graph rather than flat document formats, enabling reliable multi‑slide presentation automation. It pairs a presentation‑specialized action space of 98 tools with CARE, a content‑aware router that reduces input tokens by about 89%, and a ground‑truth‑free instruction‑following judge that feeds natural‑language critiques back into the agent for self‑correction. In benchmarks, ACE outperforms a comparable agentic HTML pipeline on instruction following (4.23 vs. 3.81), runs 1.75× faster, costs 44% less, and is preferred by 58.7% of blind raters, with 81% favoring the self‑corrected output.
arXiv:2606. 08016v1 Announce Type: cross Abstract: Current image editing software often hinges on fixed filters or expert tuning, leaving a gap between amateur users' intent and outcomes.
arXiv:2602. 18548v2 Announce Type: replace-cross Abstract: Design-to-code translates high-fidelity UI designs into executable front-end implementations, but progress remains hard to compare due to inconsistent datasets, toolchains, and evaluation protocols.
SlideGen is a collaborative vision‑language multi‑agent framework designed to generate scientific presentation slides from research papers. It assigns specialized agents to outline the presentation structure, align figures and tables with key claims, generate speaker notes, and compose editable PPTX slides using a diverse layout library. The system introduces a geometry‑aware density metric to evaluate visual clutter and demonstrates significant improvements in layout balance, content coverage, and text coherence over existing baselines on a 200‑paper benchmark.
arXiv:2608. 03298v1 Announce Type: new Abstract: Agentic presentation generation must preserve source content, maintain coherent visual design, render specialized objects, and produce usable artifacts.