1.3k citations · 1.8k across the 110 of their papers we have counts for
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Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning
Nadine Chang, Maying Shen, Shizhe Diao +6
Deployed AI systems are often trained from broad candidate data pools, necessitating data curation towards the deployment test distribution. However, standard data curation methods…
From Modalities to Propositions: A Language-Centric Framework for Multimodal Intelligence
Nadine Chang, Maying Shen, Shizhe Diao +6
We propose a language representation for multimodal data in which any observation, whether image, video, or text, is expressed as a bag of atomic propositions, simple statements ab…
Nemotron-Labs-3-Puzzle-75B-A9B: Compressing Hybrid MoE LLMs
Akhiad Bercovich, Talor Abramovich, Daniel Afrimi +67
We present Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super optimized for interactive deployment. We designed the model to maximize server throughput under…
PRESTO: Prefix-Aligned Tree Drafting for Diffusion Speculative Decoding
Zheng Wang, Zhifan Ye, Qi Cheng +8
Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs, generating tokens in parallel. This makes them effective draft models f…
Universal Deep Research: Bring Your Own Model and Strategy
Peter Belcak, Pavlo Molchanov
Deep research tools are among the most impactful and most commonly encountered agentic systems today. We observe, however, that each deep research agent introduced so far is hard-c…
Small Language Models are the Future of Agentic AI
Peter Belcak, Greg Heinrich, Shizhe Diao +5
Large language models (LLMs) are often praised for exhibiting near-human performance on a wide range of tasks and valued for their ability to hold a general conversation. The rise…
MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models
Gongfan Fang, Hongxu Yin, Saurav Muralidharan +5
Large Language Models (LLMs) are distinguished by their massive parameter counts, which typically result in significant redundancy. This work introduces MaskLLM, a learnable prunin…