Author

Vaibhav Singh

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#machine learning Preprint Sep 2026

Fixed State, Long Reach: What a Constant-Size Cache Buys Block Diffusion at Scale

This work pretraining three 3B block-diffusion denoisers (attention, mamba, and hybrid) on 300B tokens under one single-frontier objective and decoding all three through a single cached interface finds the Mamba cache delivers 4.3x lower latency, 11x less memory, and 2.6x higher single-stream throughput.

Vaibhav Singh, Pierre-André Noël, Torsten Scholak et al. · 1 citation

Preprint Aug 2026

ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling

ThinkRetrieve is proposed, a test-time scaling framework that augments the reasoning traces of LRMs with dynamically retrieved solved examples at each reasoning step, providing the model with guidance on how to reason rather than merely what facts are relevant.

Vaibhav Singh, Soumya Suvra Ghosal, Sarvesh Gharat et al. · 0 citations