activity
20232025
most citedThought Graph: Generating Thought Process for Biological Reasoning

5 citations · 10 across the 8 of their papers we have counts for

collaborators

6 papers

cs.LG2024

Cross-Modality Program Representation Learning for Electronic Design Automation with High-Level Synthesis

Zongyue Qin, Yunsheng Bai, Atefeh Sohrabizadeh +4

In recent years, domain-specific accelerators (DSAs) have gained popularity for applications such as deep learning and autonomous driving. To facilitate DSA designs, programmers us…

cs.CL20241 cited

MIRAI: Evaluating LLM Agents for Event Forecasting

Chenchen Ye, Ziniu Hu, Yihe Deng +4

Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems.…

cs.CL20245 cited

Thought Graph: Generating Thought Process for Biological Reasoning

Chi-Yang Hsu, Kyle Cox, Jiawei Xu +7

We present the Thought Graph as a novel framework to support complex reasoning and use gene set analysis as an example to uncover semantic relationships between biological processe…

cs.CV20242 cited

SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code

Ziniu Hu, Ahmet Iscen, Aashi Jain +5

This paper introduces SceneCraft, a Large Language Model (LLM) Agent converting text descriptions into Blender-executable Python scripts which render complex scenes with up to a hu…

cs.AI2023

AvalonBench: Evaluating LLMs Playing the Game of Avalon

Jonathan Light, Min Cai, Sheng Shen +1

In this paper, we explore the potential of Large Language Models (LLMs) Agents in playing the strategic social deduction game, Resistance Avalon. Players in Avalon are challenged n…

cs.LG20231 cited

ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation

Yunsheng Bai, Atefeh Sohrabizadeh, Zongyue Qin +3

Recent years have witnessed the growing popularity of domain-specific accelerators (DSAs), such as Google's TPUs, for accelerating various applications such as deep learning, searc…