SGH replaces implicit agent loops with explicit static DAGs, immutable execution plans, layered planning/recovery, and strict escalation protocols to improve controllability in LLM agents.
DynTaskMAS : A dynamic task graph-driven framework for asynchronous and parallel LLM -based multi-agent systems
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RGAO combines retrieval-based complexity assessment with a formal budget algebra to enable dynamic topology selection in multi-agent code generation with provable conservation.
A comprehensive review of self-evolving AI agents that improve themselves over time, organized via a framework of inputs, agent system, environment, and optimizers, with domain-specific and safety discussions.
citing papers explorer
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From Agent Loops to Structured Graphs:A Scheduler-Theoretic Framework for LLM Agent Execution
SGH replaces implicit agent loops with explicit static DAGs, immutable execution plans, layered planning/recovery, and strict escalation protocols to improve controllability in LLM agents.
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Retrieval-Conditioned Topology Selection with Provable Budget Conservation for Multi-Agent Code Generation
RGAO combines retrieval-based complexity assessment with a formal budget algebra to enable dynamic topology selection in multi-agent code generation with provable conservation.
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A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
A comprehensive review of self-evolving AI agents that improve themselves over time, organized via a framework of inputs, agent system, environment, and optimizers, with domain-specific and safety discussions.