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4 Connection to Draft-then-Refine
The draft-then-refine approach exploits a simple observation: if the initialization is already closer to the target distribution, fewer denoising steps may be required
from the top result below
https://arxiv.org/html/2608.02625v1
Draft–then–refine and progressive diffusion
https://arxiv.org/html/2607.22511v2
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https://arxiv.org/html/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
https://arxiv.org/list/cs.CL/new
After fine-tuning on OncoTriad-QA, OncoVLM exceeds MedGemma-4B by an average of 10
https://arxiv.org/list/cs.AI/new
Here, modes denote operational roles rather than modal or deontic logic
https://arxiv.org/html/2608.03452v1
And when a learner reproduces the pattern, does it thereby represent it, or only its visible alternations
https://arxiv.org/html/2407.13076v1
Transmission Parameter Allocation in Multi-Gateway LoRa Networks
I INTRODUCTION
II RELATED WORK
III SYSTEM MODEL
III-A The Traffic Pattern of LoRa Networks
III-B Capture Effect and Quasi-Orthogonality between SFs
III-C Packet Delivery
https://arxiv.org/html/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction
https://arxiv.org/html/2608.03138v1
During training, LLMs generate draft–revision trajectories and drafts that are substantially improved by their revisions receive an additional penalty