GSAR is a grounding-evaluation framework for multi-agent LLMs that uses a four-way claim typology, evidence-weighted asymmetric scoring, and tiered recovery decisions to detect and mitigate hallucinations.
Rcagent: Cloud root cause analysis by autonomous agents with tool-augmented large language models
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ArgLLMs build argumentation frameworks from LLMs to support explainable and contestable formal reasoning for claim verification.
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
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GSAR: Typed Grounding for Hallucination Detection and Recovery in Multi-Agent LLMs
GSAR is a grounding-evaluation framework for multi-agent LLMs that uses a four-way claim typology, evidence-weighted asymmetric scoring, and tiered recovery decisions to detect and mitigate hallucinations.
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Argumentative Large Language Models for Explainable and Contestable Claim Verification
ArgLLMs build argumentation frameworks from LLMs to support explainable and contestable formal reasoning for claim verification.