Representational Alignment (Re$^4$-Align)

Badr AlKhamissi, Brian Cheung, Dota Tianai Dong, Stephanie Fu, Erin Grant, Kushin Mukherjee, Ilia Sucholutsky, Siddharth Suresh

Keywords: cognitive science, neuroscience, alignment, representation learning

TL;DR: The 3rd Re-Align workshop at ICLR formalizes last year’s hackathon into a **Challenge** with a submission track with a focus (via talks) on interventions ("neural control") and effects ("downstream tasks") of representational alignment.

Abstract: Representational alignment among artificial and biological neural systems continues to be a rapidly growing research area across machine learning, neuroscience, and cognitive science communities; we counted 688 papers submitted to ICLR 2026 on this set of interdisciplinary topics, up from 443 papers submitted to ICLR 2025, and 303 to ICLR 2024, representing an average 51% yearly increase. The *Re-Align* Workshop at ICLR 2026 facilitates interdisciplinary discussion among these communities, highlights unexpected findings from last year’s hackathon, and pushes beyond the foundational questions of alignment addressed in the previous workshops to focus on two novel and critical interdisciplinary applications of representational alignment: enabling neural control via representational alignment and evaluating the downstream behaviors enabled by representational alignment.

Submission Number: 34