Metacognitive Consolidation lets LLMs accumulate reusable meta-reasoning skills from past episodes to improve future performance across benchmarks.
Meta-prompting: Enhancing lan- guage models with task-agnostic scaffolding,
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DyLAN automatically selects and dynamically organizes LLM agents for collaboration, outperforming fixed-agent baselines on code generation, reasoning, and decision tasks with up to 25% accuracy gains on some MMLU subjects.
VulWeaver improves Java vulnerability detection to 0.75 F1 by enhancing dependency graphs with LLM semantic fixes, extracting full context from slices plus implicit usage info, and applying type-specific meta-prompting with majority voting.
Expert interviews demonstrate that context in generative AI workplace use collapses or rots over time, limiting tool effectiveness and revealing pitfalls in computational context approaches.
SDSR places human metadata at file primacy and combines it with prompt routing rules to reach 100% primary category accuracy on a 119-category benchmark, far above the 65% no-guidance baseline.
ARIEL evaluates LLMs and LMMs on full-length biomedical summarization and figure interpretation with blinded expert review, identifies limitations, and demonstrates gains from prompt engineering, fine-tuning, and an integrated agent for hypothesis generation.
The survey organizes LLM-based multi-agent collaboration mechanisms into a framework with dimensions of actors, types, structures, strategies, and coordination protocols, reviews applications across domains, and identifies challenges for future research.
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.
citing papers explorer
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Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning
Metacognitive Consolidation lets LLMs accumulate reusable meta-reasoning skills from past episodes to improve future performance across benchmarks.
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A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration
DyLAN automatically selects and dynamically organizes LLM agents for collaboration, outperforming fixed-agent baselines on code generation, reasoning, and decision tasks with up to 25% accuracy gains on some MMLU subjects.
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VulWeaver: Weaving Broken Semantics for Grounded Vulnerability Detection
VulWeaver improves Java vulnerability detection to 0.75 F1 by enhancing dependency graphs with LLM semantic fixes, extracting full context from slices plus implicit usage info, and applying type-specific meta-prompting with majority voting.
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Context Collapse: Barriers to Adoption for Generative AI in Workplace Settings
Expert interviews demonstrate that context in generative AI workplace use collapses or rots over time, limiting tool effectiveness and revealing pitfalls in computational context approaches.
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Self-Describing Structured Data with Dual-Layer Guidance: A Lightweight Alternative to RAG for Precision Retrieval in Large-Scale LLM Knowledge Navigation
SDSR places human metadata at file primacy and combines it with prompt routing rules to reach 100% primary category accuracy on a 119-category benchmark, far above the 65% no-guidance baseline.
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Advancing AI Research Assistants with Expert-Involved Learning
ARIEL evaluates LLMs and LMMs on full-length biomedical summarization and figure interpretation with blinded expert review, identifies limitations, and demonstrates gains from prompt engineering, fine-tuning, and an integrated agent for hypothesis generation.
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Multi-Agent Collaboration Mechanisms: A Survey of LLMs
The survey organizes LLM-based multi-agent collaboration mechanisms into a framework with dimensions of actors, types, structures, strategies, and coordination protocols, reviews applications across domains, and identifies challenges for future research.
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Large Language Model Agent: A Survey on Methodology, Applications and Challenges
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.