5 papers
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…
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…
Squeezed Attention: Accelerating Long Context Length LLM Inference
Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh +6
Emerging Large Language Model (LLM) applications require long input context in order to perform complex tasks like document analysis and code generation. For these long context len…
Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim +5
Large language models (LLMs) have shown remarkable advancements in enabling language agents to tackle simple tasks. However, applying them for complex, multi-step, long-horizon tas…
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…