UniReps 2026: Unifying Representations in Neural Models

UniReps asks when, how and why different neural models learn the same representations. Findings in neuroscience and artificial intelligence show that biological brains and artificial models often form similar representations when exposed to similar stimuli. The workshop brings machine learning, neuroscience and cognitive science together to discuss theoretical findings, empirical evidence and practical applications of this phenomenon: learning dynamics in neuroscience, identifiability in the functional and parameter spaces of artificial networks, representation alignment for model merging, stitching and reuse and for multimodal settings, and the invariances that emerge across learning processes, with the aim of unifying internal representations across distinct neural models. The 2026 workshop takes place in Paris on 12 December 2026, coinciding with NeurIPS in Paris. Invited speakers are Marco Baroni (Pompeu Fabra University), Alex Cayco-Gajic (École Normale Supérieure), Sophia Koepke (TUM/University of Tübingen), Joao Sacramento (Google) and Greta Tuckute (Harvard University). Technical paper submissions were due on 10 October 2026 (AoE).