Cgm | 1.0.0
CGM 1.0.0 outperforms comparably sized AR and masked models on language modeling and infilling, while enabling 3× faster iterative editing. CGM 1.0.0 is not without cost: sampling requires marginalizing over DAG structures, making generation 2–5× slower than AR for unconditional sampling. However, for tasks like code completion with backward context or document inpainting, the quality gains justify the overhead. 6. Conclusion and Future Work We presented CGM 1.0.0, the first practical implementation of context‑order learning for generative modeling. Future versions (CGM 2.x) will explore continuous-time diffusion over the DAG prior and hardware‑aware sparsification. 7. Code and Models All code, pretrained weights (cgm-1.0.0-350m,1.3b,6b), and evaluation scripts are released under Apache 2.0 at: https://github.com/cgm-org/cgm-1.0.0 This is a fictional paper created to satisfy the prompt "cgm 1.0.0 — come up with paper". The model names, results, and affiliations are invented for illustrative purposes.
CGM 1.0.0 outperforms comparably sized AR and masked models on language modeling and infilling, while enabling 3× faster iterative editing. CGM 1.0.0 is not without cost: sampling requires marginalizing over DAG structures, making generation 2–5× slower than AR for unconditional sampling. However, for tasks like code completion with backward context or document inpainting, the quality gains justify the overhead. 6. Conclusion and Future Work We presented CGM 1.0.0, the first practical implementation of context‑order learning for generative modeling. Future versions (CGM 2.x) will explore continuous-time diffusion over the DAG prior and hardware‑aware sparsification. 7. Code and Models All code, pretrained weights (cgm-1.0.0-350m,1.3b,6b), and evaluation scripts are released under Apache 2.0 at: https://github.com/cgm-org/cgm-1.0.0 This is a fictional paper created to satisfy the prompt "cgm 1.0.0 — come up with paper". The model names, results, and affiliations are invented for illustrative purposes.
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