VFR-LLM combines small LLMs with symbolic verification and solving to reach 0.983 and 0.933 accuracy on precedence and logical deduction tasks using one model call versus lower results from self-consistency baselines.
Large language models (LLMs): survey, technical frameworks, and future challenges
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Teaching resists automation because it is interpretive, relational professional work grounded in human cognition and social interaction that cannot be exhaustively modeled.
citing papers explorer
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Resource-Aware Neuro-Symbolic Reasoning for Local Small Language Models
VFR-LLM combines small LLMs with symbolic verification and solving to reach 0.983 and 0.933 accuracy on precedence and logical deduction tasks using one model call versus lower results from self-consistency baselines.
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Why teaching resists automation in an AI-inundated era: Human judgment, non-modular work, and the limits of delegation
Teaching resists automation because it is interpretive, relational professional work grounded in human cognition and social interaction that cannot be exhaustively modeled.