SOURCE-LINKED INTELLIGENCE
What Can a Recurrent State Safely Forget?
Recurrent models must preserve information that changes future behavior while suppressing hidden-state error. These objectives conflict: contraction improves stability, but contraction along a future-distinguishing direction destroys memory. We formalize this boundary through the predictive quotient of a recurrent state space. Two hidden states are equivalent when they induce the same conditional future; their equivalence classes form predictive fibers. Every exact semantics-preserving corrector acts as the identity on this quotient. At a regular point with hidden dimension d and predictive di
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-20T05:21:16.000Z
First collected: 2026-09-23T10:01:48.231Z. This is not the publication date.