A regime theory selects the optimal controller class for LLM action decisions from a nested lattice of four classes using three data-estimable bottlenecks, with a Bernstein-tight threshold and empirical matches on multiple benchmarks.
Proceedings of the 56th Annual ACM Symposium on Theory of Computing , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
LLM narrative explanations of varying persuasiveness did not improve human decision accuracy over AI predictions alone but increased reliance on AI even when incorrect.
citing papers explorer
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A Regime Theory of Controller Class Selection for LLM Action Decisions
A regime theory selects the optimal controller class for LLM action decisions from a nested lattice of four classes using three data-estimable bottlenecks, with a Bernstein-tight threshold and empirical matches on multiple benchmarks.
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Human Decision-Making with Persuasive and Narrative LLM Explanations
LLM narrative explanations of varying persuasiveness did not improve human decision accuracy over AI predictions alone but increased reliance on AI even when incorrect.