Kede Ma

RUTA: Principled Visual Token Allocation via Rate-Utility Optimization

This work introduces RUTA, a principled Rate-Utility Token Allocation method that performs pre-LLM reduction by jointly learning which tokens to retain and how many to allocate to each image-query pair, and is optimized with a penalized rate-utility objective that balances downstream task loss against expected token usage.