Held-out transition-pair falsifier on S3 x S3 shows projected recurrent state model achieves perfect final-state prediction up to 1M+ tokens while matched baselines fail.
Structured sparse transition matrices to enable state tracking in state-space models.arXiv preprint arXiv:2509.22284
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
M²RNN achieves perfect state tracking at unseen lengths and outperforms Gated DeltaNet hybrids by 0.4-0.5 perplexity on 7B models with 3x smaller recurrent states.
citing papers explorer
-
A Held-Out Transition-Pair Falsifier for Long-Horizon Non-Abelian State Tracking
Held-out transition-pair falsifier on S3 x S3 shows projected recurrent state model achieves perfect final-state prediction up to 1M+ tokens while matched baselines fail.
-
M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling
M²RNN achieves perfect state tracking at unseen lengths and outperforms Gated DeltaNet hybrids by 0.4-0.5 perplexity on 7B models with 3x smaller recurrent states.