Call for Papers (Non-archival)

We welcome up to 5-page workshop papers on topics connecting Information Theory and Generative AI.

Submission & Important Dates


Topics of Interest

We invite submissions on (but not limited to) the following topics, organized under two complementary themes.

I. Information Theory for Generative AI

Using information-theoretic principles to understand, analyze, and improve generative AI systems.

  1. Generalization, Representation, and Reasoning in GenAI
    Information-theoretic analyses of generalization, representation learning, in-context learning, and reasoning in large language and multimodal models.
  2. Compression, Rate–Distortion, and Efficiency in Generative Models
    Rate–distortion–perception tradeoffs, model compression, quantization, and efficient training or inference of generative models.
  3. Trustworthiness, Robustness, and Security of GenAI
    Information-theoretic foundations of watermarking, privacy, robustness to distribution shift or attacks, reliability, and controllability.

II. Generative AI for Information Theory

Leveraging GenAI as a tool or catalyst for advancing information-theoretic research.

  1. GenAI-Assisted Theoretical Discovery and Analysis
    Conjecture generation, proof assistance, symbolic reasoning, exploration of open problems in information theory, and AI agents for research that support iterative hypothesis testing, literature synthesis, and theory-driven experimentation.
  2. Learning-Based Methods for Classical IT Problems
    Applications of GenAI to coding, compression, inference, estimation, and communication system design.
  3. Simulation, Modeling, and Algorithmic Design via GenAI
    Using generative models to simulate channels, sources, or learning dynamics and to inspire new algorithmic frameworks.

Submission Guidelines

📄 Paper Format

📎 Supplementary Material

👤 Author Information


Presentation Format

(When you’re ready, send me the exact wording + deadlines you want on the site, and I’ll lock it in.)