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Trust but Verify: Designing Region-Inhomogeneous Forward Dynamics for Discrete Diffusion
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fine-tuningdiffusion modelscontext corruptiontrust dynamics
2610.22380
Builder Relevance
1h ago70%
Abstract
The paper explores a new approach to discrete language diffusion by combining absorbing and uniform-mixing kernels with region-dependent rates to improve context trust during generation.
Reality Card
Core Claim
The proposed method achieves a significant improvement in infill token-F1 scores by detecting corrupted context with high recall and precision, while maintaining clean conditioning.
Method / Result
Finetuning improves infill token-F1 by 3.8 points at 10% corruption and 6.4 points at 20% corruption.
Limitations
The results may vary with different model architectures or text domains, limiting generalizability.
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