---
title: 'DeepPresenter: Adaptive Slide Generation'
url: https://www.emergentmind.com/topics/deeppresenter
type: topic
---

# DeepPresenter: Adaptive Slide Generation

DeepPresenter is an agentic framework for presentation generation that separates long-horizon content research from slide design and revision through a dual-agent architecture composed of a Researcher and a Presenter. Its defining mechanism is **environment-grounded reflection**: rather than reflecting only on internal reasoning traces, code, or intermediate text, it conditions revision on perceptual artifact states such as rendered slides and manuscript diagnostics. This positions DeepPresenter as a system for adaptive presentation construction under user constraints, not merely document summarization. In evaluation, the Gemini-3-Pro-based framework reaches a **4.44 average** score and a **0.79** diversity score, while the compact **DeepPresenter-9B** reaches **4.19**; in the audience-conditioned **X+Slides** benchmark, DeepPresenter is the strongest PPTX-generating baseline by **Audience Coverage** but still recovers only a substantial, incomplete portion of audience-essential information, with a best score of **0.714** at $\tau_A=0.7$ [2602.22839] [2606.19256].

## 1. Problem formulation and design rationale

DeepPresenter is motivated by a tension specific to automated presentation generation: a useful deck must do more than summarize source material. It must perform deep content research, satisfy user constraints such as language, slide count, and aspect ratio, and remain visually coherent after rendering. The framework is explicitly presented as an alternative to systems that rely on predefined workflows and fixed templates, which are described as brittle under diverse user intents and prone to text-heavy slides with weak topical alignment.

The same general problem is formalized in X+Slides as **audience-conditioned slide generation**, where a slide deck is treated as a **selective, audience-tailored information artifact** rather than a compressed version of its source. Specialists demand technical depth, assumptions, proofs, and limitations; learners prioritize motivation, intuition, and core ideas; decision makers prioritize implications, risks, and actionable conclusions. This framing is important for DeepPresenter because it shifts evaluation away from generic completeness or visual polish toward whether the deck selected the right source-grounded information for the intended audience [2602.22839] [2606.19256].

## 2. Dual-agent architecture and artifact production

DeepPresenter uses a dual-agent, environment-aware architecture in which the **Researcher** and **Presenter** share a common tool environment and file system. The Researcher is responsible for autonomous information gathering and manuscript construction. Given a user instruction, it plans its own exploration, retrieves web pages, papers, images, and other resources as needed, and synthesizes them into a structured Markdown manuscript. That manuscript includes slide text and associated assets organized by narrative flow and is written to disk for downstream consumption.

The Presenter converts the manuscript into the visual deck. Rather than populating a fixed template, it creates slides from scratch as HTML files. The process begins with a global **slide master** style plan specifying color palette, typography, layout grid, and overall visual language, after which the slides are rendered page by page in a design intended to remain consistent with the topic. This yields a long-horizon iterative workflow in which intermediate artifacts can be inspected, revised, and regenerated until the presentation is finalized.

The Researcher–Presenter split is not treated as a convenience layer but as a core quality mechanism. In ablation, removing the dual-agent structure drops the Gemini-3-Pro configuration from **4.44** to **4.04** and the 9B model from **4.19** to **3.23**, indicating that separating long-horizon research from visual deck construction materially improves performance [2602.22839].

## 3. Environment-grounded reflection and execution model

The technical core of DeepPresenter is **environment-grounded reflection**, which differs from self-reflection over internal reasoning traces. The premise is that the true presentation artifact is the rendered slide, not the HTML or Markdown source. Many defects are post-render phenomena: overlapping elements, broken images, text truncation, overflow, low contrast, misalignment, and font or image rendering issues. DeepPresenter therefore adds explicit inspection interfaces that expose artifact state in the environment.

For the Presenter, `inspect_slide` renders an HTML slide into pixels using a headless browser, allowing the agent to observe real rendering defects. For the Researcher, `inspect_manuscript` parses the Markdown manuscript and returns structured diagnostics such as slide count, detected language, asset validity, missing local image paths, missing alt text, and duplicate image usage. The reflective loop is then implemented as observe–think–revise: the agent inspects the actual artifact state, uses `think` to reason about that observed state, and applies targeted edits.

The execution process is written as an interactive agentic trajectory
$$
\tau = \{(r_1, a_1, o_1), \dots, (r_T, a_T, o_T)\},
$$
where each step consists of a reasoning trace $r_t$, an action $a_t$ from a tool library $\mathcal{T}$, and an observation $o_t$ from the environment $\mathcal{E}$. The overall trajectory is decomposed as
$$
\tau = \tau^R \circ \tau^P,
$$
with $\tau^R$ the Researcher trajectory and $\tau^P$ the Presenter trajectory; communication occurs through the file system $\mathcal{F}$, which stores the manuscript $\mathcal{M}$ and associated assets.

The tool ecosystem is organized into five categories: **Retrieve**, **File**, **Reason**, **Control**, and **Create**. Retrieve includes web and document search, URL fetching, paper metadata, and image captions. File includes reading, writing, moving, editing, downloading, executing commands, and directory management. Reason contains `inspect_manuscript`, `inspect_slide`, and generic thinking. Control includes todo management and finalize. Create includes image generation. During execution, the system also emits context-length warnings at **50%** and **80%** of the **50K-token** maximum so that the agent can adapt strategy and avoid overflow [2602.22839].

## 4. Training pipeline, extrinsic verification, and DeepPresenter-9B

The compact model, **DeepPresenter-9B**, is trained from synthesized trajectories rather than from a manually authored fixed workflow. Query construction draws from **PersonaHub**, **arXiv**, and **FinePDFs-Edu**, and these prompts are augmented with verifiable constraints such as slide count, language, and aspect ratio. The task pool contains **1,152 tasks**, with **1,024** used for trajectory sampling and **128** held out for evaluation.

A central training claim is that self-verification creates bias: if the agent evaluates its own output in the same trajectory state that produced it, it may rationalize defects instead of correcting them. DeepPresenter addresses this with **extrinsic verification**. After the agent calls `inspect`, an independent critic evaluates the observed artifact in isolation and outputs a short reasoning trace with a severity score and actionable advice. That trace is then inserted back into the agent context as a `think` signal. The goal is to produce targeted revisions guided by artifact-level critique rather than self-justifying introspection.

Before supervised fine-tuning, trajectories pass through a three-stage filtering pipeline. First, a rule-based system checks whether specified constraints are satisfied. Second, **GLM-4.6** evaluates whether the trajectory actually follows the extrinsic-verification advice; trajectories with reflection-action inconsistency are removed. Third, **GLM-4.6V** evaluates output quality and removes trajectories with critical defects such as overlap or broken images. Of the **1,024** sampled trajectories, **802** survive filtering and are used for fine-tuning.

DeepPresenter-9B is trained with supervised fine-tuning using **MS-SWIFT**, **batch size 32**, **learning rate $10^{-5}$**, and **5 epochs**, requiring about **80 GPU hours on 8 A800 GPUs**. In ablation, removing trajectory filtering reduces the 9B model from **4.19** to **4.03**, while training with extrinsic verification yields a larger gain than fine-tuning alone on a **300-trajectory** comparison. The analysis also reports that extrinsic verification detects more issues than self-verification across manuscript and slide categories, especially layout and render defects [2602.22839].

## 5. Empirical performance and audience-conditioned evaluation

DeepPresenter is evaluated on **128** held-out tasks spanning English and Chinese, persona-driven prompts, arXiv papers, and educational PDFs, with varied slide-count and aspect-ratio constraints. The primary evaluation dimensions are **Constraint**, **Content and Style**, and **Diversity**. Constraint is the fraction of user-specified constraints satisfied, verified by rules that parse generated PDFs. Content and Style are assessed by an MLLM-based framework from prior work, using **GPT-5** as judge. Diversity is measured by the **Vendi Score**, computed from eigenvalue entropy over **DINOv2** feature similarity matrices. The reported average score is the mean of Constraint, Content, and Style, each scaled to **0–5**, while Diversity is separately scaled to **0–1**.

Under this protocol, the full DeepPresenter framework with **Gemini-3-Pro** as backbone scores **4.44 average**, surpassing the best open-source baseline and also beating **Gamma**, which scores **4.36**. It also achieves a **0.79** diversity score. The compact **DeepPresenter-9B** scores **4.19 average**, outperforming all open-source baselines and approaching **GPT-5’s 4.22**. Removing environment-grounded reflection lowers performance from **4.44** to **4.32** on Gemini-3-Pro and from **4.19** to **3.82** on the 9B model, which supports the claim that post-render, artifact-grounded revision is a substantive contributor to quality [2602.22839].

X+Slides evaluates DeepPresenter from a different angle: **source-grounded, audience-conditioned information selection**. The benchmark is constructed from **113 topics**, **50 academic papers**, **63 non-academic documents**, **seven presentation scenes**, **three audience profiles**, and **8,133 deduplicated probes**. Each probe is an evidence-backed information slice
$$
e_j=(q_j,a_j,Z_j,d_j,m_j,g_j),
$$
where $q_j$ is the question, $a_j$ the expected answer, $Z_j$ the exact source evidence span, $d_j$ the depth level, $m_j$ the evidence modality, and $g_j$ the information domain. Audience-specific utility is assigned on a four-point rubric, and the main threshold is $\tau_A=0.7$, retaining only probes judged **essential**. At that threshold, the benchmark retains on average **42 essential probes for specialists, 31 for learners, and 24 for decision makers per topic**. Audience Coverage is defined as
$$
\mathrm{AudCov} \leftarrow R_A/V_A,
$$
with $R_A=\sum w_j c_j$ and $V_A=\sum w_j$, so the metric measures how much audience-essential information is conveyed and grounded [2606.19256].

For DeepPresenter, the audience-conditioned gains are clear but incomplete:

| Audience | Agnostic AudCov | Conditioned AudCov |
|---|---:|---:|
| Specialist | 0.413 | 0.496 |
| Learner | 0.658 | 0.714 |
| Decision maker | 0.622 | 0.654 |

Its best reported **Audience Coverage** is **0.714** in the **conditioned learner** setting. The corresponding claim-level **Correctness** scores remain fairly stable: **0.842** in the agnostic setting, and **0.835** for conditioned specialist, **0.846** for conditioned learner, and **0.819** for conditioned decision maker. The time-based **Efficiency** values lie roughly between **1.663** and **2.603**, with the best conditioned specialist **SafeEfficiency** at **2.603** and conditioned learner **SafeEfficiency** at **2.448**. The domain-wise analysis reports that conditioning improves DeepPresenter across all six domains, with the conditioned learner row reaching **0.84 context**, **0.77 method**, **0.70 evidence**, **0.73 limitations**, **0.75 implementation**, and **0.72 implications**. These results support a specific interpretation: audience conditioning mainly improves **information selection** rather than correctness, and specialist-level depth remains a notable gap [2606.19256].

## 6. Position in the literature, misconceptions, limitations, and directions

DeepPresenter belongs to the broader family of agentic presentation-generation systems that rely on intermediate representations, rendering, and iterative repair rather than direct slide-image synthesis. A nearby system, **PreGenie**, uses **Slidev** and a five-agent decomposition consisting of **Text Summarizer**, **Image Captioner**, **Code Generator**, **Code Reviewer**, and **Page Reviewer**, which places it in the same general render-check-repair lineage while emphasizing multimodal document understanding and editable Markdown code [2505.21660].

Two recurrent misconceptions are directly challenged by work surrounding DeepPresenter. The first is that **visual quality implies evidential quality**. X+Slides explicitly argues that visual quality and broad topic coverage should not be treated as evidence support without source-grounded evaluation, and its NotebookLM contrast shows that strong coverage can be separated from claim-level correctness. The second is that **reflection alone is sufficient for layout quality**. In the controlled **prefix-conditioned next-slide generation** setting of **AeSlides**, reflection-heavy methods are presented as expensive and weak on subtle aesthetics; the reported **DeepPresenter baseline** receives a human score of **3.107**, while **AeSlides + verifiable rewards** reaches **3.435** and the **GDPO** variant reaches **3.561**. AeSlides therefore argues for explicit layout supervision through verifiable rewards rather than relying exclusively on VLM-based reflection for aspect ratio, whitespace, collision, and imbalance [2604.22840].

The limitations stated for DeepPresenter are operational rather than conceptual. Multi-step tool-using rollouts increase inference cost and create sensitivity to environment instability, including context overflow and infrastructure failures. Extrinsic verification is used during trajectory synthesis but not at inference time, because an external critic online can create reflection-action inconsistency and additional overhead. The analysis of synthesized trajectories also identifies quality errors as the most common problem before filtering, followed by environment failures.

The broader research direction suggested by DeepPresenter’s own results and by X+Slides is toward systems that combine agentic planning, explicit artifact inspection, and stricter source grounding with richer audience models. The benchmark literature specifically calls for finer-grained audience profiles, stronger visual grounding, better treatment of graphs, diagrams, and layout-dependent explanations, and multilingual or cross-cultural presentation settings. Taken together, these findings place DeepPresenter at an important transition point: from workflow- and template-based slide generation toward systems that jointly optimize content research, audience-utility-driven selection, rendered-form revision, and grounded presentation correctness [2606.19256].

Source: https://www.emergentmind.com/topics/deeppresenter