1 citations · 1 across the 11 of their papers we have counts for
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DisCo Algebra: Why Disintegration and Combination Are Not Inverses
Xuexing Lu
We present DisCo algebra---a compositional framework for joint probability built from a symmetric cooperad (disintegration) and a magma-operad (non-associative partial combination,…
Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models
Martin Q. Ma, Willis Guo, Aditya Agrawal +4
Large vision-language models (VLMs) have advanced multimodal tasks such as video question answering (QA). However, VLMs face the challenge of selecting frames effectively and effic…
Act2See: Emergent Active Visual Perception for Video Reasoning
Martin Q. Ma, Yuxiao Qu, Aditya Agrawal +4
Vision-Language Models (VLMs) typically rely on static initial frames for video reasoning, restricting their ability to incorporate essential dynamic information as the reasoning p…
Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aakshita Chandiramani +544
We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemo…
Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions
Aditya Agrawal, Alwarappan Nakkiran, Darshan Fofadiya +3
Retrieval-Augmented Generation (RAG) systems are built on an unexamined assumption - that queries have correct answers and retrieval should converge toward them. This position pape…
Quad Length Codes for Lossless Compression of e4m3
Aditya Agrawal, Albert Magyar, Hiteshwar Eswaraiah +5
Training and serving Large Language Models (LLMs) relies heavily on parallelization and collective operations, which are frequently bottlenecked by network bandwidth. Lossless comp…
Single-Stage Huffman Encoder for ML Compression
Aditya Agrawal, Albert Magyar, Hiteshwar Eswaraiah +5
Training and serving Large Language Models (LLMs) require partitioning data across multiple accelerators, where collective operations are frequently bottlenecked by network bandwid…