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NeurIPS 2026TechnionTechnion / NVIDIA ResearchRepresentation & contrastive learning

Positional Encoding Is All You Need For Scalable Equivariance Constraint Relaxation

Hagay Michaeli, Haggai Maron, Daniel Soudry

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026U St. GallenUniversität St. GallenU Notre DameNational University of SingaporeWeight Space Labs & UniversiRepresentation & contrastive learning

Weight Space Learning needs to unify benchmarking! A taxonomy of evaluation practices

Tobias Ettling, Damian Falk, Aron Asefaw, Léo Meynent and 6 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026StanfordTechnion - Israel Institute of TUSIU OxfordTechnion / NVIDIA ResearchLong-context modeling

Training Transformers for KV-Cache Compressibility

KV-compressibility is a learnable property, so KV-CAT trains transformers via masked KV slots to yield representations more amenable to post-hoc compression without sacrificing quality.

Yoav Gelberg, Yam Eitan, Michael Bronstein, Yarin Gal and 1 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 14 of 20 reviewers recommend it
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