activity
20192026
most citedGPT-4o System Card

164 citations · 307 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

TasteVal: Measuring the Experimental Research Taste of AI Systems Against Human Experts

Oliver Jaffe, Dane Sherburn

We introduce TasteVal, a benchmark to evaluate the experimental research taste of frontier models. We define research taste as the ability to pick interesting problems to solve, de…

cs.AI2025★ 4 cited

PaperBench: Evaluating AI's Ability to Replicate AI Research

Giulio Starace, Oliver Jaffe, Dane Sherburn +10

We introduce PaperBench, a benchmark evaluating the ability of AI agents to replicate state-of-the-art AI research. Agents must replicate 20 ICML 2024 Spotlight and Oral papers fro…

cs.CL2024★ 164 cited

GPT-4o System Card

OpenAI, :, Aaron Hurst +416

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's…

physics.geo-ph2024★ 2 cited

Python workflow for segmenting multiphase flow in porous rocks

Catherine Spurin, Sharon Ellman, Dane Sherburn +2

X-ray micro-computed tomography (X-ray micro-CT) is widely employed to investigate flow phenomena in porous media, providing a powerful alternative to core-scale experiments for es…

cs.CL2024★ 9 cited

MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

Jun Shern Chan, Neil Chowdhury, Oliver Jaffe +9

We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering. To this end, we curate 75 ML engineering-related competitions from Kag…

cs.LG2024

Can Language Models Explain Their Own Classification Behavior?

Dane Sherburn, Bilal Chughtai, Owain Evans

Large language models (LLMs) perform well at a myriad of tasks, but explaining the processes behind this performance is a challenge. This paper investigates whether LLMs can give f…

cs.LG2019★ 128 cited

Relational Graph Attention Networks

Dan Busbridge, Dane Sherburn, Pietro Cavallo +1

We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these…