Can Hyperbolic Embeddings Boost Multi‑Step Visual Planning? Introducing GeoWorld

GeoWorld tackles the geometric neglect and multi‑step shortcomings of energy‑based predictive world models by mapping latent representations onto hyperbolic manifolds and applying a geometry‑aware reinforcement learning framework, achieving notable success‑rate gains on long‑horizon visual planning benchmarks.

Energy-Based Modelsgeometric reinforcement learninghyperbolic embeddings

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Can Hyperbolic Embeddings Boost Multi‑Step Visual Planning? Introducing GeoWorld

DeepHub IMBA

Feb 28, 2026 · Artificial Intelligence

Why Energy‑Based Models Could Outperform Probabilistic LLMs, According to Yann LeCun

Yann LeCun argues that the probability‑driven, token‑by‑token design of current large language models may never reach human‑level intelligence, and explains how Energy‑Based Models replace probability distributions with an energy function, offering more flexible training, inference, and multi‑modal capabilities.

Contrastive DivergenceDensity EstimationEBM

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Why Energy‑Based Models Could Outperform Probabilistic LLMs, According to Yann LeCun