LLMs favor task-appropriate reasoning over conflicting instructions, yet reasoning types are linearly encoded in middle-to-late layers and can be steered to boost instruction compliance by up to 29%.
Fundamental reasoning paradigms induce out-of-domain generalization in language models
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.
citing papers explorer
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Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models
LLMs favor task-appropriate reasoning over conflicting instructions, yet reasoning types are linearly encoded in middle-to-late layers and can be steered to boost instruction compliance by up to 29%.
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HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.