GRIP uses contrastive training on LMM feedback to retrieve beneficial in-context examples for multimodal tasks, outperforming similarity-based methods and transferring across models including GPT-4o.
Dr.ICL: Demonstration-retrieved in-context learning
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VRSD is defined by maximizing query-to-sum similarity, proven NP-complete, with a parameter-free heuristic outperforming MMR and DPP baselines.
Many-shot CoT-ICL improves when demonstrations are ordered for smooth conceptual progression, with CDS delivering up to 5.42 percentage-point gains on math tasks using 64 examples.
Adaptive exemplar retrieval plus just-in-time prerequisite checks let LLM agents complete distribution-grid analysis workflows at 94–100% Pass@1 across six models, beating ReAct, LangChain, CrewAI, and PowerChain.
citing papers explorer
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GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models
GRIP uses contrastive training on LMM feedback to retrieve beneficial in-context examples for multimodal tasks, outperforming similarity-based methods and transferring across models including GPT-4o.
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Vector Retrieval with Similarity and Diversity: How Hard Is It?
VRSD is defined by maximizing query-to-sum similarity, proven NP-complete, with a parameter-free heuristic outperforming MMR and DPP baselines.
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Many-Shot CoT-ICL: Making In-Context Learning Truly Learn
Many-shot CoT-ICL improves when demonstrations are ordered for smooth conceptual progression, with CDS delivering up to 5.42 percentage-point gains on math tasks using 64 examples.
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PowerDAG: Supervisory Agentic AI System for Automating Distribution Grid Analysis
Adaptive exemplar retrieval plus just-in-time prerequisite checks let LLM agents complete distribution-grid analysis workflows at 94–100% Pass@1 across six models, beating ReAct, LangChain, CrewAI, and PowerChain.