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STEPQuant is proposed, a spatial-temporal post-training quantization framework for Delta-rule recurrent states that allocates precision according to error magnitude and memory lifetime, and jointly fits key-row and value-column scales based on state distributions and key-row impact on output error.
First, it is demonstrated that quantization is significantly more effective in preserving trustworthiness compared to pruning, and more importantly, it is demonstrated that compressing a reliable large model via quantization can produce SLMs with superior trustworthiness and adaptability compared to using small models...
PILL (Probing-based InfiLling with preset-Length-free decoding), an efficient infilling method for DLMs that requires no preset initial length and adds far fewer extra forward passes than baselines, substantially reducing inference time is proposed.
This work proposes method that unifies textual reasoning and graph message passing within a masked diffusion language model, a language model with bidirectional attention and generative decoding that outperforms graph neural networks, graph transformers, and LLM-based baselines on all three TAG benchmarks across two ta...
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