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
10 Pith papers cite this work. Polarity classification is still indexing.
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VulWeaver combines repaired static-analysis graphs, holistic code context, and structured LLM reasoning; it reports F1 0.75 on a new Java benchmark and 0.78 on the C/C++ PrimeVul test set.
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.
SingGuard introduces a policy-adaptive multimodal LLM guardrail with dynamic reasoning regimes and SingGuard-Bench, reporting SOTA F1 scores across 35 datasets and improved policy-following accuracy under runtime shifts.
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.
FitOne-8B/32B models improve average scores on ACSM-EP and NSCA-CSCS certification exams by up to 12.73% over base Qwen3 while retaining general capabilities.
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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VulWeaver: Weaving Broken Semantics for Grounded Vulnerability Detection
VulWeaver combines repaired static-analysis graphs, holistic code context, and structured LLM reasoning; it reports F1 0.75 on a new Java benchmark and 0.78 on the C/C++ PrimeVul test set.
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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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SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning
SingGuard introduces a policy-adaptive multimodal LLM guardrail with dynamic reasoning regimes and SingGuard-Bench, reporting SOTA F1 scores across 35 datasets and improved policy-following accuracy under runtime shifts.
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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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Enhancing Fitness Intelligence through Domain-Specific LLM Post-Training
FitOne-8B/32B models improve average scores on ACSM-EP and NSCA-CSCS certification exams by up to 12.73% over base Qwen3 while retaining general capabilities.
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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.