8 papers
OpenThoughts-Agent: Data Recipes for Agentic Models
Negin Raoof, Richard Zhuang, Marianna Nezhurina +47
Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts…
VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation
Luca Collini, Andrew Hennesee, Patrick Yubeaton +2
Rapid advances in language models (LMs) have created new opportunities for automated code generation while complicating trade-offs between model characteristics and prompt design c…
Exploring the Agentic Frontier of Verilog Code Generation
Patrick Yubeaton, Siddharth Garg, Chinmay Hegde
Large language models (LLMs) have made rapid advancements in code generation for popular languages such as Python and C++. Many of these recent gains can be attributed to the use o…
Discovering Sparse Recovery Algorithms Using Neural Architecture Search
Patrick Yubeaton, Sarthak Gupta, M. Salman Asif +1
The design of novel algorithms for solving inverse problems in signal processing is an incredibly difficult, heuristic-driven, and time-consuming task. In this short paper, we the…
VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verification
Patrick Yubeaton, Andre Nakkab, Weihua Xiao +4
This paper introduces VeriThoughts, a novel dataset designed for reasoning-based Verilog code generation. We establish a new benchmark framework grounded in formal verification met…
Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference
Patrick Yubeaton, Tareq Mahmoud, Shehab Naga +8
As they become more capable, large language models (LLMs) have continued to rapidly increase in size. This has exacerbated the difficulty in running state of the art LLMs on small,…
TruncFormer: Private LLM Inference Using Only Truncations
Patrick Yubeaton, Jianqiao Cambridge Mo, Karthik Garimella +4
Private inference (PI) serves an important role in guaranteeing the privacy of user data when interfacing with proprietary machine learning models such as LLMs. However, PI remains…
SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification
Benjamin Feuer, Jiawei Xu, Niv Cohen +3
Data curation is the problem of how to collect and organize samples into a dataset that supports efficient learning. Despite the centrality of the task, little work has been devote…