AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models
Luo and co-authors (2026) study news sentiment and text signals using a large language model. The abstract does not mention a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Experiments demonstrate that AutoL2S effectively reduces reasoning length up to 71% with minimal accuracy loss, yielding markedly better trade-off in token length and inference time while preserving accuracy.”
StrategyNews sentiment
TechniqueLarge language model
Long/shortLong-short
InputsText and news
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodNews sentiment and text signals by year
FamilyNews sentiment and text signals148Momentum and trend following289Factor investing240Machine learning return prediction15318 more · 854
AssetEquities97
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