428 citations · 1.2k across the 32 of their papers we have counts for
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cs.LG2026
MA-JEPA: Joint-Embedding World Models for Multi-Agent Reinforcement Learning
Brandon Gary Kaplowitz, Osaze James Obahor, Christian Schroeder de Witt
World models improve sample efficiency by training policies on imagined trajectories, but their usefulness depends on learning representations that capture the information needed f…
cs.CR2026
Exploring the Cryptographic Limits of Transformer Networks
Stefan Domunco, Andis Draguns, Philip Torr +2
In recent work it has been shown that colluding AI agents can use steganographic methods to exchange malicious information. Whether a transformer can implement steganographic metho…