TEM: Transformer Energy-Based Models Claim to Outscale Diffusion Without Guidance
du_yilun · x · 2026-10-30
Researchers introduce TEM (Transformer Energy-based Models), billed as the first scalable contrastive energy-based model.
- TEM reportedly outscales diffusion without guidance.
- Claims better generalization, test-time scaling, and OOD detection than diffusion models.
If the claims hold, this challenges the assumption that diffusion is the only scalable route, bringing contrastive EBMs back into the scaling race. Details in the original thread.
Related event: TEM: Energy-Based Models Scale Up for the First Time, Beating Diffusion(2 posts)→
More from Models
- Coding agent spots overclaim in Tencent Hunyuan paper, sparking "AI-slopped paper era" meme — CSProfKGD · 2026-10-30
- OpenAI doubles Pro-tier usage limits after reported plan to halve them — koltregaskes · 2026-10-30
- Liquid AI launches LFM3 model family; 1.7B hits 185 tok/s on-device — JosephJacks_ · 2026-10-30
- GLM-5.3 completes 3 of 13 multistage cyberattack scenarios, strongest open-weight model tested by Irregular — cyb3rops · 2026-10-30
- Meta opens 816B-param Muse Spark Open with 42B active and 1M context on Open Math OS — AIatMeta · 2026-10-30
- Hitting content filters on a retrieval eval for SEC data is 'insane', user says — xeophon · 2026-10-30