BEAM reformulates LLM-based heuristic design as bi-level optimization using GA for structures, MCTS for placeholders, and adaptive memory to outperform prior single-layer methods on CVRP and MIS tasks.
Chain-of-thought prompting elicits reasoning in large language models
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LLMs with two prompting strategies and model validation tools produce mostly syntactically correct, conforming, semantically realistic and diverse instances of UML class diagrams.
Vision-language models can serve as zero-shot ODD sensors for autonomous driving when using definition-anchored chain-of-thought prompting with persona decomposition.
citing papers explorer
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BEAM: Bi-level Memory-adaptive Algorithmic Evolution for LLM-Powered Heuristic Design
BEAM reformulates LLM-based heuristic design as bi-level optimization using GA for structures, MCTS for placeholders, and adaptive memory to outperform prior single-layer methods on CVRP and MIS tasks.
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LLM-based Generation of Semantically Diverse and Realistic Domain Model Instances
LLMs with two prompting strategies and model validation tools produce mostly syntactically correct, conforming, semantically realistic and diverse instances of UML class diagrams.
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Operating Within the Operational Design Domain: Zero-Shot Perception with Vision-Language Models
Vision-language models can serve as zero-shot ODD sensors for autonomous driving when using definition-anchored chain-of-thought prompting with persona decomposition.