Language models achieve a perfect LSAT score, with experiments showing that internal thinking phases and a fine-tuned process reward model are key to high performance on logical reasoning questions.
Enhancing performance of explainable AI models with constrained concept refinement
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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A periodic framework is proposed to characterize, compare, and predict behaviors across distributed computing solutions by mapping system properties in a structured space inspired by the chemical periodic table.
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AI Achieves a Perfect LSAT Score
Language models achieve a perfect LSAT score, with experiments showing that internal thinking phases and a fine-tuned process reward model are key to high performance on logical reasoning questions.
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A Periodic Space of Distributed Computing: Vision & Framework
A periodic framework is proposed to characterize, compare, and predict behaviors across distributed computing solutions by mapping system properties in a structured space inspired by the chemical periodic table.