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  ---
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- license: apache-2.0
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  library_name: pytorch
 
 
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  tags:
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- - tracelock
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- - dream
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- - diffusion-language-model
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- - activation-autoencoder
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- - pytorch
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  ---
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  # TraceLock Dream Activation Autoencoder
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- This repository contains the projection autoencoder checkpoint used to reproduce TraceLock on Dream.
 
 
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  TraceLock is a token-level acceptance policy for Dream-style masked diffusion generation. Dream proposes candidate tokens during the denoising loop, and TraceLock decides which positions should be locked now versus kept masked for later refinement.
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  ## Citation
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- If you use this checkpoint, please cite the TraceLock paper/repository once available.
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-
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
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  library_name: pytorch
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+ license: apache-2.0
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+ pipeline_tag: feature-extraction
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  tags:
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+ - tracelock
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+ - dream
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+ - diffusion-language-model
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+ - activation-autoencoder
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+ - pytorch
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  ---
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  # TraceLock Dream Activation Autoencoder
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+ This repository contains the projection autoencoder checkpoint used to reproduce TraceLock on Dream, as presented in the paper [The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models](https://huggingface.co/papers/2605.24697).
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+
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+ **Code**: [https://github.com/BobSun98/TraceLock](https://github.com/BobSun98/TraceLock)
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  TraceLock is a token-level acceptance policy for Dream-style masked diffusion generation. Dream proposes candidate tokens during the denoising loop, and TraceLock decides which positions should be locked now versus kept masked for later refinement.
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  ## Citation
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+ If you use this checkpoint, please cite the following paper:
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+
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+ ```bibtex
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+ @misc{sun2026pathmatters,
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+ title={The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models},
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+ author={Bohang Sun and Max Zhu and Francesco Caso and Jindong Gu and Junchi Yu and Philip Torr and Pietro Liò and Jialin Yu},
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+ year={2026},
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+ eprint={2605.24697},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2605.24697}
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+ }
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+ ```