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CaLR: Causal Latent Revision for Robust Diffusion Reasoning
Not provided in the abstract
reasoningdiffusion modelscausal inference
2609.20981
Builder Relevance
1h ago80%
Abstract
CaLR proposes a framework that reformulates reasoning as constrained latent optimization to enhance logical consistency in diffusion language models.
Reality Card
Core Claim
CaLR achieves state-of-the-art performance in diffusion language models on complex benchmarks, surpassing strong autoregressive baselines.
Method / Result
CaLR demonstrates superior robustness in constrained tasks like Sudoku.
Limitations
The abstract does not specify limitations or reproducibility concerns.
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