What is ControlText?
ControlText is a modular framework designed to simplify and enhance text-based workflows by providing reusable templates for automated reasoning and computational logic. With a set of well-defined patterns—such as ControlText-Markdown, ControlText-Regex, and ControlText-PyKE—it empowers developers to inject precise controls into natural language generation and processing pipelines.More
Integration with DLM
When paired with DLM (Diffusion Language Model), ControlText extends its capabilities by structuring the model's denoising steps into controllable reasoning stages. Each noise-to-signal transition can leverage ControlText templates to enforce constraints, incorporate external knowledge, or trigger subroutines. This seamless combination unlocks advanced use cases—such as template-guided code generation, conditional summarization, and multi-step question answering—while maintaining DLM's high-fidelity, long-context performance.
Key Advantages
Cost Efficiency
By leveraging intelligent caching of ControlText templates, GPU workload is significantly reduced. Frequent subroutine calls hit the cache instead of re-running expensive denoising operations, lowering GPU utilization and operational costs.
Enhanced Controllability
ControlText templates introduce explicit checkpoints that guide DLM's generation process, dramatically reducing the chance of hallucinations. Constraints and validation routines ensure outputs remain aligned with expected logic and factual consistency.
Blazing Speed
Parallel decoding combined with high cache hit rates accelerates overall throughput. Multiple denoising stages execute concurrently, and cached intermediate results further boost performance, delivering rapid, scalable responses.
New Paradigm Guidance
Application Directions
Explore practical use cases and in-depth examples of ControlText in action.
View Case StudiesAbout Rethink
Learn how the Deep Rethink mechanism enhances iterative self-correction in DLM workflows.
Learn About RethinkGet Started
Explore our documentation to learn how to integrate ControlText with your DLM-powered applications. Visit the repositories below and join our community for support and updates.