Publications (14)
Streaming Detection of Queried Event Start
Cristobal Eyzaguirre, Eric Tang, Shyamal Buch +3
Robotics, autonomous driving, augmented reality, and many embodied computer vision applications must quickly react to user-defined events unfolding in real time. We address this se…
Measuring Mathematical Problem Solving With the MATH Dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath +5
Many intellectual endeavors require mathematical problem solving, but this skill remains beyond the capabilities of computers. To measure this ability in machine learning models, w…
Filling Positions Without Transfers: Screening on Outside Options
Morteza Honarvar, Joanna Krysta, Eric Tang
A designer offers vertically-differentiated positions to agents in the absence of transfers. Agents have private outside options and may reject their offers ex-post. The designer h…
Cued to Queue: Information in Waiting-Line Auctions
Jack Hirsch, Eric Tang
We study the effect of providing information to agents who queue before a scarce good is distributed at a fixed time. Many information policies reveal "sudden bad news," when agent…
SkyRL-Agent: Efficient RL Training for Multi-turn LLM Agent
Shiyi Cao, Dacheng Li, Fangzhou Zhao +12
We introduce SkyRL-Agent, a framework for efficient, multi-turn, long-horizon agent training and evaluation. It provides efficient asynchronous dispatching, lightweight tool integr…
Understanding LLM Embeddings for Regression
Eric Tang, Bangding Yang, Xingyou Song
With the rise of large language models (LLMs) for flexibly processing information as strings, a natural application is regression, specifically by preprocessing string representati…
Imaging reconfigurable molecular concentration on a graphene field-effect transistor
Franklin Liou, Hsin-Zon Tsai, Andrew S. Aikawa +9
The spatial arrangement of adsorbates deposited onto a clean surface in vacuum typically cannot be reversibly tuned. Here we use scanning tunneling microscopy to demonstrate that m…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Private From Whom? Minimal Information Leakage in Auctions
Eric Gao, Eric Tang
In many auctions, bidders may value keeping their private information hidden from the auctioneer or other bidders. Yet information must be conveyed to conduct an auction. Among det…
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios
Mantas Mazeika, Eric Tang, Andy Zou +6
In recent years, deep neural networks have demonstrated increasingly strong abilities to recognize objects and activities in videos. However, as video understanding becomes widely…
Empirical Computation: Prompting versus Programming
Eric Tang, Jing Liu, Marcel Böhme
Large Language Models (LLM) can solve *any* computational problem *without* an algorithm in a runtime *independent* of the computational complexity of that problem. Instead of spec…
Validation of image systems simulation technology using a Cornell Box
Zheng Lyu, Krithin Kripakaran, Max Furth +3
We describe and experimentally validate an end-to-end simulation of a digital camera. The simulation models the spectral radiance of 3D-scenes, formation of the spectral irradiance…
On the Existence of Balanced Generalized de Bruijn Sequences
Matthew Baker, Bhumika Mittal, Haran Mouli +1
A balanced generalized de Bruijn sequence with parameters is a cyclic sequence of bits such that (a) the number of 0's equals the number of 1's, and (b) each substrin…
LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!
Dacheng Li, Shiyi Cao, Tyler Griggs +9
Large reasoning models (LRMs) tackle complex reasoning problems by following long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking, and self-validation. Howev…