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This paper proposes LMT, a Bayesian causal discovery framework that combines LLM-extracted semantic signals from textual alarm records with timestamp-based statistical evidence to infer causal graphs in manufacturing systems.
This paper studies agentic misalignment in multi-agent systems with automated workflows, proposing Agentic Evidence Attribution (AEA) to correct misaligned agent behavior using context-specific evidence.