Author

Aditya Akella

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Preprint Jul 2026

Sangam: Efficiently Serving Diffusion LLMs with the AR Stack

Sangam, a serving system for cached dLLM inference that adopts a hybrid serving strategy, overflowing prefills onto decode workers to relieve prefill under-provisioning, and uses the same deficit-budget scheduler to protect those workers'decodes from the overflow.

Nitin Kedia, Saurabh Agarwal, Myungjin Lee et al. · 1 citation

Preprint Jul 2026

ACID: Adaptive Caching for vIDeo generation

ACID is a lightweight, training-free wrapper that monitors the rate of change of each method's existing drift signal to dynamically switch between a low and a high threshold and consistently expands the Pareto frontier of visual quality versus inference speed beyond what any fixed threshold achieves.

Om Agrawal, Saurabh Agarwal, Aditya Akella · 0 citations