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Steering latent dynamics with readout feedback can unlock performance improvements in recurrent models that traditional inference methods fail to achieve.
SCSE transforms recurrent computation in Looped Transformers by ensuring that deviations from a learned anchor enhance performance without compromising stability.
Positional encodings aren't just about position - they fundamentally prevent mean-field transformers from collapsing into meaningless single-point token distributions.
Ditch the energy functions: C-voting unlocks better test-time reasoning in recurrent models by simply picking the most confident trajectory.
Forget Transformers; this new recurrent architecture learns more stable representations and generalizes better out-of-distribution by interleaving fast latent updates with slower, self-organizing observation processing.
