Publications (7)
CodingGenie: A Proactive LLM-Powered Programming Assistant
Sebastian Zhao, Alan Zhu, Hussein Mozannar +3
While developers increasingly adopt tools powered by large language models (LLMs) in day-to-day workflows, these tools still require explicit user invocation. To seamlessly integra…
Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media
Smitha Milli, Micah Carroll, Yike Wang +3
In a pre-registered algorithmic audit, we found that, relative to a reverse-chronological baseline, Twitter's engagement-based ranking algorithm amplifies emotionally charged, out-…
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…
The Ingredients for Robotic Diffusion Transformers
Sudeep Dasari, Oier Mees, Sebastian Zhao +2
In recent years roboticists have achieved remarkable progress in solving increasingly general tasks on dexterous robotic hardware by leveraging high capacity Transformer network ar…
Need Help? Designing Proactive AI Assistants for Programming
Valerie Chen, Alan Zhu, Sebastian Zhao +3
While current chat-based AI assistants primarily operate reactively, responding only when prompted by users, there is significant potential for these systems to proactively assist…
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers
Hussein Mozannar, Valerie Chen, Mohammed Alsobay +7
Evaluation of large language models for code has primarily relied on static benchmarks, including HumanEval (Chen et al., 2021), or more recently using human preferences of LLM res…
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…