Learning to Present: Inverse Specification Rewards for Agentic Slide Generation explores Automate professional slide deck creation using LLMs and a novel inverse specification reward system.. Commercial viability score: 8/10 in Presentation Automation.
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Karthik Ragunath Ananda Kumar
Tavus Inc., University of Texas at Dallas
Subrahmanyam Arunachalam
Texas A&M University
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Analysis model: GPT-4o · Last scored: 4/2/2026
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Creating professional presentations is a time-consuming task across various industries. An automated solution that produces high-quality presentations can save significant time and effort for professionals who need to communicate ideas effectively.
The system can be turned into a cloud-based application where users input their presentation needs and receive professionally generated slides in various formats, significantly reducing time spent on manual slide creation.
This system could replace existing manual tools and processes for slide creation in organizations, potentially reducing or replacing the need for design consultants or PowerPoint specialists.
The market includes businesses, educational institutions, and conference organizers, all of whom require presentations regularly. This solution addresses the pain point of time-consuming slide creation, offering a potentially large user base willing to pay for efficiency gains.
A SaaS tool for businesses and educational institutions that automates the generation of high-quality slide presentations from text briefs or documents, relieving employees and educators from manual slide creation tasks.
The approach uses a reinforcement learning framework where a language model is fine-tuned to generate slides by selecting and using different tools within a structured environment. It employs a multi-component reward system to assess and refine slide quality, including a novel inverse specification reward for coherence and faithfulness.
Tested across 48 business presentation briefs. The fine-tuned model achieved 91.2% of Claude Opus 4.6's quality while improving 33.1% over the base model, demonstrating strong adherence to presentation quality metrics.
The system's effectiveness depends heavily on the quality of the initial brief and the specificity of user input. Misaligned briefs could lead to suboptimal presentations. Additionally, while it automates slide creation, it may miss nuanced design elements human designers might catch.
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