Transformers are limited to a linearly growing number of accessible output sequences with prompt length, with exponential decay in accessible proportion beyond a critical point, even under unbounded context.
arXiv preprint arXiv:2504.06214 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
years
2026 4verdicts
UNVERDICTED 4representative citing papers
ZAYA1-8B is a reasoning MoE model with 700M active parameters that matches larger models on math and coding benchmarks and reaches 91.9% on AIME'25 via Markovian RSA test-time compute.
RoPE-Perturbed Self-Distillation improves positional robustness during long-context fine-tuning of LLMs by training models to produce consistent outputs across RoPE-perturbed views of the input.
ZONOS2 8B is a scaled MoE TTS model with 900M active parameters trained on 6M hours of data that reports competitive SOTA results on naturalness, speaker similarity, WER, and a new ZTTS1-Eval benchmark while releasing weights and code.
citing papers explorer
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How Many Different Outputs Can a Transformer Generate?
Transformers are limited to a linearly growing number of accessible output sequences with prompt length, with exponential decay in accessible proportion beyond a critical point, even under unbounded context.
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ZAYA1-8B Technical Report
ZAYA1-8B is a reasoning MoE model with 700M active parameters that matches larger models on math and coding benchmarks and reaches 91.9% on AIME'25 via Markovian RSA test-time compute.
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Shuffle the Context: RoPE-Perturbed Self-Distillation for Long-Context Adaptation
RoPE-Perturbed Self-Distillation improves positional robustness during long-context fine-tuning of LLMs by training models to produce consistent outputs across RoPE-perturbed views of the input.
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ZONOS2 Technical Report
ZONOS2 8B is a scaled MoE TTS model with 900M active parameters trained on 6M hours of data that reports competitive SOTA results on naturalness, speaker similarity, WER, and a new ZTTS1-Eval benchmark while releasing weights and code.