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This paper proposes using finite-state machines derived from LLM agent traces to predict failures and next steps, enhancing safety auditing and runtime monitoring for agents.
This paper proposes a transferable autologistic model for predicting rare equipment failures across heterogeneous sensor configurations, evaluating it on a synthetic refrigerator dataset.
This paper introduces AgentForesight, a framework for online auditing and early failure prediction in LLM-based multi-agent systems. It presents a new dataset, AFTraj-22K, and a specialized model, AgentForesight-7B, which outperforms leading proprietary models in detecting decisive errors during trajectory execution.