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.
arXiv preprint arXiv:2507.21407 , year=
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The paper maps agent memory research via three forms (token-level, parametric, latent), three functions (factual, experiential, working), and dynamics of formation/evolution/retrieval, plus benchmarks and future directions.
BlindGuard introduces an unsupervised hierarchical agent encoder plus corruption-guided contrastive detector that identifies malicious agents in LLM-based multi-agent systems without any attack labels or prior knowledge of malicious behaviors.
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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Memory in the Age of AI Agents
The paper maps agent memory research via three forms (token-level, parametric, latent), three functions (factual, experiential, working), and dynamics of formation/evolution/retrieval, plus benchmarks and future directions.
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BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks
BlindGuard introduces an unsupervised hierarchical agent encoder plus corruption-guided contrastive detector that identifies malicious agents in LLM-based multi-agent systems without any attack labels or prior knowledge of malicious behaviors.