Author

Chumeng Liang

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#machine learning Preprint Oct 2026

How Much Can Language Models Gain from Test-Time Computation?

How much can test-time computation improve a language model, and at what cost? Test-time scaling is widely proposed as a substitute for larger models, but existing comparisons mostly evaluate one domain at a time and rarely charge selection to the budget. We introduce SELF-POT, a benchmark and evaluation framework that...

Bang Yang, Jing-Yuan Li, Jia-Jun Fan et al. · 0 citations

#natural language process... Preprint Sep 2026

UBTree: Parallel Tree Drafting via Unigram and Bigram Models for Speculative Decoding

Speculative decoding accelerates language model inference by verifying multiple draft tokens in a single target-model pass. Recent parallel drafters have achieved breakthrough performance in frontier production models, but their effectiveness deteriorates as the entropy of target distributions increases due to insuffic...

Chumeng Liang, Lin-Xuan Wang, Xin-Yu Peng et al. · 0 citations