CP-SynC uses coordinated LLM agents to generate, validate via synthesized checkers, and select MiniZinc models from natural language, substantially outperforming baselines on a 100-problem benchmark.
NeurIPS36, 46534–46594 (2023)
2 Pith papers cite this work. Polarity classification is still indexing.
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Learning in Blocks uses heterogeneous multi-agent debate to score CEFR-aligned conversational competence, enforce 70% mastery progression, and deliver spaced review, yielding better outcomes than feedback alone in an 8-week study of 180 A2 learners.
citing papers explorer
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CP-SynC: Multi-Agent Zero-Shot Constraint Modeling in MiniZinc with Synthesized Checkers
CP-SynC uses coordinated LLM agents to generate, validate via synthesized checkers, and select MiniZinc models from natural language, substantially outperforming baselines on a 100-problem benchmark.
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Learning in Blocks: A Multi Agent Debate Assisted Personalized Adaptive Learning Framework for Language Learning
Learning in Blocks uses heterogeneous multi-agent debate to score CEFR-aligned conversational competence, enforce 70% mastery progression, and deliver spaced review, yielding better outcomes than feedback alone in an 8-week study of 180 A2 learners.