Neural Network Artifacts as a New Data Modality

Machine learning has been transformed by learning from large populations of data, yet it has rarely turned that same population-level lens on its own products: trained models and the artifacts they generate. Today's repositories hold millions of models, and within their weights, gradients, internal representations, and optimization trajectories lies a vast but largely untapped reservoir of knowledge: we still lack principled methods to compare models, search among them, predict or modify their behaviour, or understand how they relate. This workshop advances an agenda to close that gap by treating neural artifacts as a data modality in their own right, amenable to modelling and learning. Its goals are twofold: to encourage tailored methodologies that analyse, interpret, modify, control, and synthesise these artifacts in an automated manner; and to connect, under a shared data-centric perspective, the communities that have approached them in isolation, spanning model merging, meta-learning, mechanistic interpretability, neural architecture search, and neural-field processing. Building on the inaugural ICLR 2025 edition, this second edition broadens the scope from weights to the full range of neural artifacts, adds neural lineages and AI supply chains, and places strong emphasis on standardised datasets, benchmarks, and tasks. Through invited talks, contributed papers and discussions, the workshop aims to consolidate these scattered efforts into a coherent field.