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ARXIV:2603.16839 · PRESENTATION AUTOMATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.16839PRESENTATION AUTOMATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEKarthik Ragunath Ananda Kumar · Subrahmanyam Arunachalam · arXiv
Automate professional slide deck creation using LLMs and a novel inverse specification reward system.
Opportunity summary
Pain Automate professional slide deck creation using LLMs and a novel inverse specification reward system.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Automate professional slide deck creation using LLMs and a novel inverse specification reward system. This work proposes an OpenEnv-compatible reinforcement learning environment where LLM agents learn to research topics, plan content, and generate professional…
Automated presentation generation remains a challenging task requiring coherent content creation, visual design, and audience-aware communication. This work proposes an OpenEnv-compatible reinforcement learning environment where LLM agents learn to research topics, plan content, and…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Experiments on 48 diverse business briefs across six models demonstrate that our fine-tuned 7B model achieves 91.2% of Claude Opus 4.6's quality while improving…
Presentation Automation moved forward this cycle; last verified April 2026. Public score 8.0/10.
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Automate professional slide deck creation using LLMs and a novel inverse specification reward system.
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10.48550/arXiv.2603.16839Automate professional slide deck creation using LLMs and a novel inverse specification reward system.
Abstract
Automated presentation generation remains a challenging task requiring coherent content creation, visual design, and audience-aware communication. This work proposes an OpenEnv-compatible reinforcement learning environment where LLM agents learn to research topics, plan content, and generate professional HTML slide presentations through tool use. We introduce a multi-component reward system combining structural validation, render quality assessment, LLM-based aesthetic scoring, content quality metrics, and an inverse specification reward that measures how faithfully generated slides convey their intended purpose. The inverse specification reward, an "inverse task" where an LLM attempts to recover the original specification from generated slides, provides a holistic quality signal. Our approach fine-tunes Qwen2.5-Coder-7B via GRPO, training only 0.5% of parameters on prompts derived from expert demonstrations collected using Claude Opus 4.6. Experiments on 48 diverse business briefs across six models demonstrate that our fine-tuned 7B model achieves 91.2% of Claude Opus 4.6's quality while improving 33.1% over the base model. The six-model comparison reveals that instruction adherence and tool-use compliance, rather than raw parameter count, determine agentic task performance. We contribute SlideRL, an open-source dataset of 288 multi-turn rollout trajectories across all six models: https://huggingface.co/datasets/KarthikRagunathAnandaKumar/sliderl-multi-turn-rollouts Code: https://github.com/pushing-the-frontier/slide-forge-llm
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Viability
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Dimensions overall score 8.0
PROBLEM
Automate professional slide deck creation using LLMs and a novel inverse specification reward system. This work proposes an OpenEnv-compatible reinforcement learning environment where LLM agents learn to research topics, plan content, and generate professional HTML slide present...
METHOD
Automated presentation generation remains a challenging task requiring coherent content creation, visual design, and audience-aware communication. This work proposes an OpenEnv-compatible reinforcement learning environment where LLM agents learn to research topics, plan content,...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Experiments on 48 diverse business briefs across six models demonstrate that our fine-tuned 7B model achieves 91.2% of Claude Opus 4.6's quality while improving 33.1% over the base model.
WHY NOW
Presentation Automation moved forward this cycle; last verified April 2026. Public score 8.0/10.
Experiments on 48 diverse business briefs across six models demonstrate that our fine-tuned 7B model achieves 91.2% of Claude Opus 4.6's quality
Explicitly stated numeric result in abstract with specific percentage comparison
partial
improving 33.1% over the base model
Explicitly stated numeric improvement in abstract with specific percentage
partial
The six-model comparison reveals that instruction adherence and tool-use compliance, rather than raw parameter count, determine agentic task performance
Directly stated conclusion from six-model comparison in abstract
partial
an inverse specification reward that measures how faithfully generated slides convey their intended purpose
Directly described in abstract as a key methodological innovation
partial
Our approach fine-tunes Qwen2.5-Coder-7B via GRPO, training only 0.5% of parameters on prompts derived from expert demonstrations
Specific technical details provided in abstract with exact percentage
partial
The system's effectiveness depends heavily on the quality of the initial brief and the specificity of user input
Explicitly stated limitation in analysis section with clear causal relationship
partial
while it automates slide creation, it may miss nuanced design elements human designers might catch
Directly stated limitation in analysis section, though somewhat qualitative
partial
We introduce a multi-component reward system combining structural validation, render quality assessment, LLM-based aesthetic scoring, content quality metrics, and an inverse specification reward
Comprehensive description of reward components directly stated in abstract
partial
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Automate professional slide deck creation using LLMs and a novel inverse specification reward system.
Segment
Presentation Automation
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Commercial read
8.0/10 public viability
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proof status
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stale
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Technical feasibility
partial
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Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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