Reasoning-tuned LLMs align with human comprehension failure patterns under code obfuscation using the Block Model, unlike instruction-tuned variants.
Understanding understanding source code with functional magnetic resonance imaging,
2 Pith papers cite this work. Polarity classification is still indexing.
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A multisite biometric study finds lower cognitive engagement under AI assistance via EEG and blink rate, with physiological-performance links present only in the non-AI condition.
citing papers explorer
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Do Machines Struggle Where Humans Do? LLM and Human Comprehension of Obfuscated Code
Reasoning-tuned LLMs align with human comprehension failure patterns under code obfuscation using the Block Model, unlike instruction-tuned variants.
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Using Biometrics to Understand AI-Assisted Coding Performance and its Perception
A multisite biometric study finds lower cognitive engagement under AI assistance via EEG and blink rate, with physiological-performance links present only in the non-AI condition.