Research & Publications

Academic Research

Peer-reviewed research and publications validating our lossless token optimization approach.

PublishedPeer-Reviewed

Lossless Token Optimization for Large Language Model API Cost Reduction: A Novel Invertible Transformation Approach

This paper presents empirical validation of a lossless token optimization framework that guarantees 100% output preservation through invertible transformations. Through rigorous evaluation on 50,000 diverse prompts, we demonstrate an average token reduction of 25.96% while maintaining absolute fidelity.

TwoTrim Research
November 2025
Zenodo

Key Findings

  • • Average token reduction of 25.96% with perfect output fidelity
  • • 62.1% of test cases achieving 20-40% reduction
  • • Mathematical guarantee of invertibility across 50,000 diverse prompts
  • • Model-agnostic design compatible with all major LLM providers

Research Focus

Our research focuses on developing provably lossless optimization techniques that guarantee identical model outputs while significantly reducing token consumption.

Methodology

Empirical validation through large-scale testing on diverse prompt types, ensuring robustness across different use cases and domains.

Applications

Production-ready optimizations suitable for enterprise deployments requiring deterministic behavior and output consistency.

Experience the Research in Production

Our research-backed lossless optimization technology is available for production use. Start reducing your LLM costs while maintaining perfect output quality.