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T2-z: Test-Time Optimization of Environment Latents for Robotic World Models

Paper recorded by Signals 4 on 2026-10-15 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16

Category: cs.AI · 人工智能 · first seen 2026-10-16

Abstract

Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitly specified, from physical properties such as friction and mass to human-defined environmental rules. We introduce T2-Z for test-time optimization of environment latents, which augments a robotic world model with a learn

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#283 most recent of 960 cs.AI papers we have recorded · ↑ newer: Discovering state-of-the-art quantum circuits with large language mode · ↓ older: CoDA: Co-Evolution of Data Engines and Action Policies
Cite this page: T2-z: Test-Time Optimization of Environment Latents for Robotic World Models: the #283 most recent of 960 cs.AI papers we have recorded (as of 2026-10-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/t2-z-test-time-optimization-of-environment-latents-for-robotic-world-models.html
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