AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Results show that intervening on the world, rather than on what the model sees, helps latent world models predict what their actions cause.
This work investigates when and how JEPAs can recover the underlying causal states from observations, and establishes identifiability conditions under which representations learned by this general objective recover the underlying latent causal states up to component-wise invertible transformations and permutation.
It is suggested that reasoning validity is better read from state-conditioned motion than from either static states or decontextualized trajectories alone, andlations show that motion, region, and direction provide complementary signals.
This work empirically validate predictions through exact calculations and controlled stochastic optimization of the worst-case joint-distribution error permitted by excess risk at most $\varepsilon$-identifiability modulus using an $\varepsilon$-identifiability modulus.
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