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arxiv: 2604.17894 · v1 · submitted 2026-04-20 · 💻 cs.CL

Automatic Slide Updating with User-Defined Dynamic Templates and Natural Language Instructions

Kun Zhou , Jiakai He , Wenmian Yang , Zhensheng Wang , Yiquan Zhang , Weijia Jia This is my paper

Pith reviewed 2026-05-10 04:08 UTC · model grok-4.3

classification 💻 cs.CL
keywords dynamic slide updatenatural language instructionspresentation slidesagent frameworkmultimodal parsingDynaSlide benchmarktool-augmented reasoning
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The pith

The paper defines a task for updating user-designed slides from natural language instructions and provides a benchmark and agent system to do it.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The authors establish the problem of keeping diverse, user-created presentation slides current using only natural language commands on the original template. They release DynaSlide, a dataset of 20,036 instruction-slide pairs drawn from real business reporting decks that share an external data source. They introduce SlideAgent, which parses the slide visually and textually, grounds the instruction, and uses tools to update tables, charts, and conclusions while leaving layout and styling unchanged. This approach offers a workable baseline and highlights open difficulties in accurate data handling and style preservation. A reader would care because manual slide maintenance consumes significant time in data-driven work.

Core claim

Dynamic Slide Update via Natural Language Instructions on User-provided Templates is a new task that requires an agent to modify slide content according to instructions while strictly preserving the original layout and visual style. The DynaSlide benchmark supplies 20,036 triples of source slide, instruction, and target slide for training and evaluation. SlideAgent solves this by multimodal parsing of the slide, grounding the instruction in the slide elements, and employing tools for numerical and textual updates.

What carries the argument

SlideAgent, an agent-based system that merges multimodal slide parsing, natural language instruction grounding, and tool-augmented reasoning to perform updates on tables, charts, and text.

If this is right

  • Slides can be refreshed automatically from instructions without recreating layouts.
  • The shared database grounding allows consistent updates across related slides.
  • Evaluation protocols show specific failure modes in content accuracy and layout fidelity.
  • Future systems can build on this baseline for better performance on complex visuals.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This framework could extend to updating other visual documents like dashboards or infographics.
  • Connecting the agent directly to live databases might enable real-time slide maintenance.
  • Error patterns in the evaluations point to needs for better chart understanding modules.

Load-bearing premise

That existing multimodal and reasoning tools, when combined in an agent, can correctly interpret instructions and apply changes to arbitrary user slide designs without errors or style violations.

What would settle it

A test set of slides containing intricate charts and tables where SlideAgent produces outputs that deviate from the target slides in either data values or visual arrangement.

Figures

Figures reproduced from arXiv: 2604.17894 by Jiakai He, Kun Zhou, Weijia Jia, Wenmian Yang, Yiquan Zhang, Zhensheng Wang.

Figure 1
Figure 1. Figure 1: Illustration of the template filling. instantiated sub-template randomly selects one ti￾tle template to introduce lexical variation while preserving analytical intent. 3.2.2 Table template design Tables consist of a caption and a table body. In practical business presentation, the table caption typically specifies the analysis topic and data scope, while the table body aggregates and statistically summariz… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of SlideAgent. The framework operates in two stages: (1) [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Element-level accuracy across closed-domain [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of three canonical table structures: Field-constraint, Constraint-field, and Cross-constraint. [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The prompt used for multimodal layout parsing to predict semantic labels and bounding boxes. [PITH_FULL_IMAGE:figures/full_fig_p029_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The prompt for data source extraction, mapping slide content to database schema slots. [PITH_FULL_IMAGE:figures/full_fig_p030_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The prompt for closed-domain function logic extraction via predefined tool invocation. [PITH_FULL_IMAGE:figures/full_fig_p031_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The prompt for open-domain logic extraction, decomposing unknown logic into atomic parameters. [PITH_FULL_IMAGE:figures/full_fig_p032_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The prompt for user instruction parsing, modeled as a parameter state update task. [PITH_FULL_IMAGE:figures/full_fig_p033_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The prompt for SQL generation, converting updated parameters into executable database queries. [PITH_FULL_IMAGE:figures/full_fig_p034_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The prompt for fact-aware summary update based on data changes. [PITH_FULL_IMAGE:figures/full_fig_p035_11.png] view at source ↗
read the original abstract

Presentation slides are a primary medium for data-driven reporting, yet keeping complex, analytics-style decks up to date remains labor-intensive. Existing automation methods mostly follow fixed template filling and cannot support dynamic updates for diverse, user-authored slide decks. We therefore define "Dynamic Slide Update via Natural Language Instructions on User-provided Templates" and introduce DynaSlide, a large-scale benchmark with 20,036 real-world instruction-execution triples (source slide, user instruction, target slide) grounded in a shared external database and built from business reporting slides under bring-your-own-template (BYO-template) conditions. To tackle this task, we propose SlideAgent, an agent-based framework that combines multimodal slide parsing, natural language instruction grounding, and tool-augmented reasoning for tables, charts, and textual conclusions. SlideAgent updates content while preserving layout and style, providing a strong reference baseline on DynaSlide. We further design end-to-end and component-level evaluation protocols that reveal key challenges and opportunities for future research. The dataset and code are available at https://github.com/XiaoZhou2024/SlideAgent.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper defines the task of 'Dynamic Slide Update via Natural Language Instructions on User-provided Templates' and introduces DynaSlide, a benchmark of 20,036 real-world instruction-execution triples (source slide, instruction, target slide) constructed from business reporting slides under bring-your-own-template conditions and grounded in an external database. It proposes SlideAgent, an agent-based framework combining multimodal slide parsing, natural language instruction grounding, and tool-augmented reasoning for tables, charts, and text to update content while preserving layout and style, positions it as a strong baseline, and describes end-to-end and component-level evaluation protocols. The dataset and code are released.

Significance. If the framework's performance claims hold under rigorous validation, the work would be significant for practical automation of data-driven slide maintenance and for advancing multimodal agent research in document editing. The large-scale, real-world benchmark construction and public release of data/code are clear strengths that enable reproducibility and follow-on work.

major comments (2)
  1. [Evaluation protocols] Evaluation protocols section: the manuscript describes end-to-end and component-level evaluation protocols but supplies no quantitative results, error analysis, or explicit factual-accuracy audit against ground-truth target slides. This is load-bearing for the central claim that SlideAgent constitutes a 'strong reference baseline,' because any mismatch between parsed slide state and database state or errors in the agent's reasoning trace would directly falsify reliable update performance on diverse BYO-template decks.
  2. [SlideAgent framework] SlideAgent framework description: the claim that combining existing multimodal parsing, instruction grounding, and tool use reliably preserves layout/style without content errors or hallucinations rests on an untested assumption. No ablation studies or concrete evidence are provided to show that off-the-shelf components suffice for the diverse, user-authored slides in DynaSlide.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'providing a strong reference baseline' is stated without reference to any specific metrics or results; adding a brief indication of key findings would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. The comments highlight important areas for strengthening the manuscript's claims about evaluation and the framework. We address each major comment below and commit to revisions that incorporate quantitative results, error analysis, and ablations.

read point-by-point responses

  1. Referee: [Evaluation protocols] Evaluation protocols section: the manuscript describes end-to-end and component-level evaluation protocols but supplies no quantitative results, error analysis, or explicit factual-accuracy audit against ground-truth target slides. This is load-bearing for the central claim that SlideAgent constitutes a 'strong reference baseline,' because any mismatch between parsed slide state and database state or errors in the agent's reasoning trace would directly falsify reliable update performance on diverse BYO-template decks.

    Authors: We agree that the absence of quantitative results, error analysis, and factual-accuracy audits weakens the support for claiming SlideAgent as a strong baseline. The current manuscript emphasizes the design of the protocols and the benchmark but does not present the actual performance numbers or audits. In the revised version, we will add the full quantitative results from our end-to-end and component-level evaluations (including accuracy metrics against ground-truth target slides), a detailed error analysis, and discussion of any parsing or reasoning mismatches. This will directly address the concern about potential falsification of performance claims. revision: yes

  2. Referee: [SlideAgent framework] SlideAgent framework description: the claim that combining existing multimodal parsing, instruction grounding, and tool use reliably preserves layout/style without content errors or hallucinations rests on an untested assumption. No ablation studies or concrete evidence are provided to show that off-the-shelf components suffice for the diverse, user-authored slides in DynaSlide.

    Authors: We acknowledge that the framework section relies on the integration of existing components without sufficient ablations or concrete evidence for the diverse BYO-template slides. While component-level evaluations are described, they do not include systematic ablations. In the revision, we will add ablation studies (e.g., removing individual modules like tool-augmented reasoning or multimodal parsing) and provide concrete examples and metrics demonstrating layout/style preservation, content accuracy, and handling of hallucinations on DynaSlide slides. This will replace the assumption with empirical support. revision: yes

Circularity Check

0 steps flagged

No circularity: task definition, external benchmark, and baseline framework are independent of self-referential inputs

full rationale

The paper defines the task of Dynamic Slide Update via Natural Language Instructions on User-provided Templates, constructs DynaSlide as 20,036 real-world triples (source slide, instruction, target slide) drawn from external business reporting slides under BYO-template conditions and grounded in a shared external database, and proposes SlideAgent as an agent framework that assembles existing multimodal parsing, instruction grounding, and tool-augmented reasoning components to serve as a reference baseline. Evaluation protocols are applied to this independently sourced benchmark. No equations, fitted parameters renamed as predictions, load-bearing self-citations, uniqueness theorems, or ansatzes appear in the derivation; the central claims rest on empirical results against external data rather than reducing to the paper's own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The work rests on standard assumptions about the capabilities of current multimodal language models and tool-use agents rather than introducing new mathematical axioms or fitted parameters.

axioms (2)
  • domain assumption Multimodal models can accurately parse layout, text, tables, and charts from user-authored slides
    Invoked as the foundation for the parsing step in SlideAgent.
  • domain assumption Tool-augmented reasoning can correctly interpret natural language instructions and apply changes to slide content
    Core premise of the agent framework description.
invented entities (2)
  • DynaSlide benchmark no independent evidence
    purpose: Large-scale dataset of instruction-execution triples for the defined task
    Newly constructed and released in this paper; no independent evidence provided beyond the authors' collection process.
  • SlideAgent framework no independent evidence
    purpose: Agent system that performs dynamic slide updates
    Proposed architecture combining parsing, grounding, and tools; no external validation cited.

pith-pipeline@v0.9.0 · 5505 in / 1559 out tokens · 41552 ms · 2026-05-10T04:08:52.174700+00:00 · methodology

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Reference graph

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