Signals 4 · free daily AI digest

world2vec: Mechanistic world models through latent dynamics learning

Paper recorded by Signals 4 on 2026-10-29 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.LG · 机器学习 · first seen 2026-10-30

Abstract

World models can achieve strong control performance without faithfully capturing the dynamics of their environment, limiting their potential for mechanistic interpretability and scientific discovery. Here, we introduce world2vec to learn mechanistic world models without reward information directly from observations and actions. World2vec learns latent dynamics that faithfully reproduce the true sy

Read on arXiv →

#69 most recent of 703 cs.LG papers we have recorded · ↑ newer: Adaptive Scenario Generation for KL-DRO Around a Learned Density Model · ↓ older: An ODE-based Sampler for Discrete Diffusion Models
Cite this page: world2vec: Mechanistic world models through latent dynamics learning: the #69 most recent of 703 cs.LG papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/world2vec-mechanistic-world-models-through-latent-dynamics-learning.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.LG papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions