EditPPT: Structured Execution and Evaluation for Reliable Slide Editing

arXiv:2608.20381 · cs.CL, cs.AI, cs.HC · Submitted 2026-06-29 · Read on arXiv

cs.CL, cs.AI, cs.HC

Submitted: 2026-06-29

Updated: 2026-10-18

License: http://creativecommons.org/licenses/by/4.0/

The gist: Reliable slide editing requires precise modifications to intended objects while preserving the rest of the presentation.

Abstract

Reliable slide editing requires precise modifications to intended objects while preserving the rest of the presentation. We argue that slide editing is a problem of structured, constrained modification rather than open-ended code generation. Despite the complexity of real presentations, editing requests can largely be decomposed into the objects to change, the operations to apply, and the state to preserve. This perspective guides both evaluation and execution. For evaluation, we introduce DeckEdit-Bench, 183 editing tasks on 28 human-authored decks whose target and preserved objects are annotated at the shape level, so that intended and unintended changes are scored mostly by deterministic rules. For execution, we present EditPPT, a multi-agent framework that resolves targets before editing, restricts each specialist to a subset of 37 predefined tools, and validates each edit for instruction fulfillment and preservation, correcting or rolling back failed ones. Across five backbone models, EditPPT reaches 66.7-74.9% Task Success while keeping Preservation above 97%. Even with an open 27B model and no task-specific fine-tuning, it approaches Claude Code with Sonnet 5 in Task Success while better preserving the original deck. These results suggest that constraining how edits are executed, not only model scale, makes slide editing more reliable.

Related papers