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This article discusses how the movie Moon can serve as a conceptual model for designing agentic large language models (LLMs), exploring themes of AI agency and identity.
This paper proposes an agentic LLM framework for automated structural analysis of 3D frame systems from natural language inputs, achieving 90% accuracy on ten representative 3D frames through a multi-agent pipeline.
This paper introduces SearchSwarm, a model trained on synthesized delegation intelligence to improve long-horizon deep research tasks via task decomposition and subagent coordination, achieving state-of-the-art results on BrowseComp benchmarks.
SAGE proposes a novelty gate for memory evolution in agentic LLMs, using a von Mises-Fisher-based density estimator to decide whether to add, merge, or ignore new facts, reducing LLM calls while maintaining memory quality.