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HFEPX · Eval paper review

EditPPT: Structured Execution and Evaluation for Reliable Slide Editing

Jiheon Kim, Kyudan Jung, Jaegul Choo

Published

Jun 29, 2026

Citations

0

Trust level

Moderate

Usefulness score

25/100 (Low)

Extraction confidence

55% (Moderate)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 18, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

Automatic Metrics Coding Multi Agent

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this for comparison and orientation, not as your only source.

Best use

Background context only

Use if you need

A benchmark-and-metrics comparison anchor.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

No major weakness surfaced.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
25/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

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.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

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

Evaluation Modes

strong

Automatic Metrics

Includes extracted eval setup.

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

Quality Controls

missing

Not reported

No explicit QC controls found.

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

Benchmarks / Datasets

strong

Deckedit Bench

Useful for quick benchmark comparison.

"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."

Reported Metrics

strong

Task success

Useful for evaluation criteria comparison.

"Across five backbone models, EditPPT reaches 66.7-74.9% Task Success while keeping Preservation above 97%."

Benchmarks and datasets

Deckedit-Bench

Reported metrics

task success
Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
Coding
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
Multi Agent
Quality controls
Not reported
Evidence quality
Moderate
Use this page as
Background context only

Research brief

Metadata summary

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

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • 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.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • 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…

Why it matters for eval

  • 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…

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    Detected: Deckedit-Bench

  • Metric reporting is present

    Detected: task success