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Eric Alcaide

2 accepted papers

2026

Multiple Token Divergence: Measuring and Steering In-Context Computation Density

ICLR 2026poster

Measuring the in-context computational effort of language models is a key challenge, as metrics like next-token loss fail to capture reasoning complexity. Prior methods based on latent state compressibility can be invasive and unstable. We propose Multiple Token Divergence (MTD), a simple measure of…

Cited by 0SourcecodeScholar
2023

RWKV: Reinventing RNNs for the Transformer Era

EMNLP 2023long findings

Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but st…

Cited by 0SourceScholar