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ACL 2025Singapore-MIT Alliance for ReseaNational University of SingaporeMassachusetts Institute of TechnEfficient inference & serving

TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding

TETRIS selects optimal draft tokens for batch speculative decoding, improving inference speed and efficiency across varied batch sizes.

Zhaoxuan Wu, Zijian Zhou, Arun Kumar Verma, Alok Prakash and 2 more

Published 2025 · 0 citations

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57%Worth a look
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EMNLP 2025FindingsSingapore-MIT Alliance for ReseaNational University of SingaporeAgency for Science, Technology aSystems, Applications & ProductsAI SingaporeLLM pretraining & scaling laws

Uncovering Scaling Laws for Large Language Models via Inverse Problems

Inverse problem methods uncover scaling laws for large language models, revealing predictive relationships between model size, data, and performance from abstract evidence.

Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin and 14 more

Published 2025 · 0 citations

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67%Highly rated
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Agency for Science, Technology aNational University of SingaporeSingapore-MIT Alliance for ReseaChinese University of Hong Kong,LLM pretraining & scaling laws

Position Paper: Data-Centric AI in the Age of Large Language Models

This position paper argues for data-centric AI in the age of large language models and proposes research directions.

Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma and 15 more

Published 2024 · 2 citations

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AI panel: 2 of 20 reviewers recommend it
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