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TAPDreamer: Transferable Adversarial Patches for World Action Models

Paper recorded by Signals 4 on 2026-10-05 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-05 on arXiv · recorded by Signals 4 on 2026-10-06

Category: cs.AI · 人工智能 · first seen 2026-10-06

Abstract

World models learn to predict how their environment will evolve, making them an important foundation for general-purpose robotic control. Yet world action models depend on camera inputs whose manipulation can corrupt the visual representations used across tasks and action policies. Existing attacks on these models optimize against the victim's actions or predicted futures and therefore require acc

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#452 most recent of 980 cs.AI papers we have recorded · ↑ newer: Paradee: Distilling Kokoro-82M into an 8M-Parameter Single-Voice Text- · ↓ older: Sharpen Without Search: On-Policy Distillation of Sequence-Level Power
Cite this page: TAPDreamer: Transferable Adversarial Patches for World Action Models: the #452 most recent of 980 cs.AI papers we have recorded (as of 2026-10-05). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/tapdreamer-transferable-adversarial-patches-for-world-action-models.html
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