9 citations · 12 across the 18 of their papers we have counts for
7 papers · 1 filter
Arbitrage: Efficient Reasoning via Advantage-Aware Speculation
Monishwaran Maheswaran, Rishabh Tiwari, Yuezhou Hu +8
Modern Large Language Models achieve impressive reasoning capabilities with long Chain of Thoughts, but they incur substantial computational cost during inference, and this motivat…
Beyond Next-Token Prediction: A Performance Characterization of Diffusion versus Autoregressive Language Models
Minseo Kim, Coleman Hooper, Aditya Tomar +5
Large Language Models (LLMs) have achieved state-of-the-art performance on a broad range of Natural Language Processing (NLP) tasks, including document processing and code generati…
SciML Agents: Write the Solver, Not the Solution
Saarth Gaonkar, Xiang Zheng, Haocheng Xi +5
Recent work in scientific machine learning aims to tackle scientific tasks directly by predicting target values with neural networks (e.g., physics-informed neural networks, neural…
XQuant: Breaking the Memory Wall for LLM Inference with KV Cache Rematerialization
Aditya Tomar, Coleman Hooper, Minjae Lee +7
Although LLM inference has emerged as a critical workload for many downstream applications, efficiently inferring LLMs is challenging due to the substantial memory footprint and ba…
Multipole Attention for Efficient Long Context Reasoning
Coleman Hooper, Sebastian Zhao, Luca Manolache +5
Large Reasoning Models (LRMs) have shown promising accuracy improvements on complex problem-solving tasks. While these models have attained high accuracy by leveraging additional c…
QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache
Rishabh Tiwari, Haocheng Xi, Aditya Tomar +7
Large Language Models (LLMs) are increasingly being deployed on edge devices for long-context settings, creating a growing need for fast and efficient long-context inference. In th…
ETS: Efficient Tree Search for Inference-Time Scaling
Coleman Hooper, Sehoon Kim, Suhong Moon +7
Test-time compute scaling has emerged as a new axis along which to improve model accuracy, where additional computation is used at inference time to allow the model to think longer…