SST V2 introduces parallel-trainable nonlinear recurrence in latent space to let transformers reason continuously across positions, delivering +15 points on GPQA-Diamond and halving remaining GSM8K errors over matched baselines.
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Humans produce language more like greedy local choices than globally optimal planning when vocabulary is tightly constrained, with skilled speakers showing more revision.
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State Stream Transformer (SST) V2: Parallel Training of Nonlinear Recurrence for Latent Space Reasoning
SST V2 introduces parallel-trainable nonlinear recurrence in latent space to let transformers reason continuously across positions, delivering +15 points on GPQA-Diamond and halving remaining GSM8K errors over matched baselines.
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Greedy or not, here I come: Language production under vocabulary constraints in humans and resource-rational models
Humans produce language more like greedy local choices than globally optimal planning when vocabulary is tightly constrained, with skilled speakers showing more revision.