HAB applies coarse-to-fine budgeting to LLM reasoning, predicting per-problem depth and learning intra-step token budgets via PPL comparisons and adaptive Pareto optimization, yielding higher accuracy and lower token use than standard CoT on GSM8K and MATH500.
Chain of logic: Rule-based reasoning with large language models
2 Pith papers cite this work. Polarity classification is still indexing.
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An LLM-assisted, keyframe-based animation framework streams cloud-hosted petascale datasets to commodity hardware and generates 3D scientific animations from natural-language requests.
citing papers explorer
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Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs
HAB applies coarse-to-fine budgeting to LLM reasoning, predicting per-problem depth and learning intra-step token budgets via PPL comparisons and adaptive Pareto optimization, yielding higher accuracy and lower token use than standard CoT on GSM8K and MATH500.
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Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scripting
An LLM-assisted, keyframe-based animation framework streams cloud-hosted petascale datasets to commodity hardware and generates 3D scientific animations from natural-language requests.