The Automaton Underneath: The Additive Input Pathway Is a Parasitic Attractor for State Tracking in Householder Linear RNN

Abstract:Linear RNNs with input-dependent Householder-product transitions (DeltaNet/DeltaProduct-class) can provably represent hard state-tracking automata, yet trained models fail to length-generalize -- a gap recent work attributes to optimization, without a causal account. We give one, in a pre-registered, within-architecture causal ablation: the same model with one term deleted -- the additive input injection $b_t = W_b e_t$. With $b_t$, models fit length 32 and collapse out-of-distribution on parity, $S_4$, $A_5$, and non-solvable $S_5$ word problems (0.20 at position 512 on $S_5$). Without it -- input acting only through the orthogonal transitions -- the same architecture learns the exact automaton: median accuracy 1.00 at 16x the training length, at every width admitted by a representation law we state and test: the minimal number of Householder factors per token equals the maximal reflection length of the task's generators in the format-pinned representation (parity 1, $S_4$ 3, $A_5$ and $S_5$ 4); below it, nothing fits. The contrast with DeltaProduct's $S_4$/$A_5$ at $n_h{=}2$ (group-element classification, $SO(3)$ realization) shows the law is representation-relative: task format is a hidden variable in state-tracking benchmarks. Two pre-registered arms locate the mechanism. (i) Initialized at a verified-exact solution with $W_b{=}0$, Adam grows the additive path and pulls the model off the exact solution; $-b$ controls stay at 1.00. (ii) Our registered prediction that the fit routes through $b_t$ fired its kill criterion: all 49 fitting seeds retain in-domain fit under $W_b{:=}0$ -- and at law-minimal width, zeroing $W_b$ at inference restores exact generalization (parity 5/5, $S_5$ 5/5, $S_4$ 4/5, $A_5$ 4/5). The additive pathway is parasitic: it destabilizes, then conceals, a correctly learned automaton. All 202 runs pre-registered; all numbers regenerate from artifacts.

Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)

arXiv:2609.18966 [cs.LG]

(or arXiv:2609.18966v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.18966 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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