activity
20222026
most citedA Developmentally-Inspired Examination of Shape versus Texture Bias in Machines

10 citations · 12 across the 7 of their papers we have counts for

collaborators

8 papers

cs.HC2026

Humans are Missing from AI Coding Agent Research

Zora Z. Wang, John Yang, Kilian Lieret +10

Recent progress in AI coding agent research has led to rapid improvements in agents' ability to autonomously perform complex software engineering tasks, from editing large codebase…

cs.CL2026

Outcome Rewards Do Not Guarantee Verifiable or Causally Important Reasoning

Qinan Yu, Alexa Tartaglini, Peter Hase +2

Reinforcement Learning from Verifiable Rewards (RLVR) on chain-of-thought reasoning has become a standard part of language model post-training recipes. A common assumption is that…

cs.CV2026

I Walk the Line: Examining the Role of Gestalt Continuity in Object Binding for Vision Transformers

Alexa R. Tartaglini, Michael A. Lepori

Object binding is a foundational process in visual cognition, during which low-level perceptual features are joined into object representations. Binding has been considered a funda…

cs.LG2025

Addressing divergent representations from causal interventions on neural networks

Satchel Grant, Simon Jerome Han, Alexa R. Tartaglini +1

A common approach to mechanistic interpretability is to causally manipulate model representations via targeted interventions in order to understand what those representations encod…

cs.CV2025

Diagnosing Bottlenecks in Data Visualization Understanding by Vision-Language Models

Alexa R. Tartaglini, Satchel Grant, Daniel Wurgaft +2

Data visualizations are vital components of many scientific articles and news stories. Current vision-language models (VLMs) still struggle on basic data visualization understandin…

cs.CV2024

Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects

Michael A. Lepori, Alexa R. Tartaglini, Wai Keen Vong +3

Though vision transformers (ViTs) have achieved state-of-the-art performance in a variety of settings, they exhibit surprising failures when performing tasks involving visual relat…