MultiHashFormer enables hash-based autoregression in LMs by encoding tokens as multi-hash signatures, outperforming standard Transformers at 100M-3B scales while keeping parameter count constant for multilingual expansion.
B., Hoffer, E., and Reichart, R
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
CellxPert uses inference-time MCMC steering on a multi-omics single-cell foundation model to predict genome-wide transcriptomic responses to gene perturbations and outperforms baselines on cell-type annotation, perturbation prediction, and multi-omic integration benchmarks.
MedTPE compresses EHR token sequences by up to 31% via merging common medical token pairs, reducing LLM inference latency 34-63% while maintaining or improving performance on mortality and phenotyping tasks.
citing papers explorer
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MultiHashFormer: Hash-based Generative Language Models
MultiHashFormer enables hash-based autoregression in LMs by encoding tokens as multi-hash signatures, outperforming standard Transformers at 100M-3B scales while keeping parameter count constant for multilingual expansion.
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CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation
CellxPert uses inference-time MCMC steering on a multi-omics single-cell foundation model to predict genome-wide transcriptomic responses to gene perturbations and outperforms baselines on cell-type annotation, perturbation prediction, and multi-omic integration benchmarks.
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From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction
MedTPE compresses EHR token sequences by up to 31% via merging common medical token pairs, reducing LLM inference latency 34-63% while maintaining or improving performance on mortality and phenotyping tasks.