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FlowEdit is a novel framework that uses information-theoretic principles to regulate internal reasoning flows in LLMs, enabling them to generate multiple alternative responses in a single pass for ill-posed problems with conflicting conditions. Experiments show 68% improvement in exact-set-match accuracy and 24% boost in response informativeness over leading proprietary models.
This thread discusses the concept of 'Jagged Intelligence' in AI, framing it as a consequence of AI learning being an ill-posed inverse problem, and argues that external stabilizers like scaffolding and verification are essential.
Introduces a perturbative approach for nonparametric instrumental variable estimation that extends kernel ridge methods with higher-order corrections, achieving up to 99% reduction in prediction error in high-dimensional settings.
Soohak is a new benchmark of 439 research-level math problems curated by mathematicians to evaluate the reasoning capabilities of frontier LLMs, highlighting significant gaps in solving advanced problems and recognizing ill-posed questions.