Xinjiang University

Tool-using language-model agents can pick the intended tool yet execute an injected argument, and existing counterfactual guards lose source sensitivity when they score with a reasoning prefix that already encodes the injected decision. BiCP removes the current-step reasoning prefix before scoring, probes both the tool-name target and the complete-call target, and routes proposals by operation risk. On a 922-case fixed replay, the full method reduces confirmed InjecAgent attacks from 83/682 to 0/682 and unsafe conditional executions from 33/240 to 0/240, while benign task success rises from 66/120 to 78/120.

Tool-using language-model agents can pick the intended tool yet execute an injected argument, and existing counterfactual guards lose source sensitivity when they score with a reasoning prefix that already encodes the injected decision. BiCP removes the current-step reasoning prefix before scoring, probes both the tool-name target and the complete-call target, and routes proposals by operation risk. On a 922-case fixed replay, the full method reduces confirmed InjecAgent attacks from 83/682 to 0/682 and unsafe conditional executions from 33/240 to 0/240, while benign task success rises from 66/120 to 78/120.

Distribution-network self-healing has traded decision quality, real-time speed and explainability against one another: mathematical programming nears the optimum but is slow, while deep reinforcement learning decides in milliseconds but stays opaque. Embedding a graph attention network as the shared backbone of a soft actor–critic agent and writing a resilience index into the reward brings online decisions to 2.3 ms on the IEEE 33-bus and 123-bus systems, within 2.8% of the mixed-integer linear programming optimum. Attention heatmaps and SHAP attributions show where the agent looks and why it decides.

Distribution-network self-healing has traded decision quality, real-time speed and explainability against one another: mathematical programming nears the optimum but is slow, while deep reinforcement learning decides in milliseconds but stays opaque. Embedding a graph attention network as the shared backbone of a soft actor–critic agent and writing a resilience index into the reward brings online decisions to 2.3 ms on the IEEE 33-bus and 123-bus systems, within 2.8% of the mixed-integer linear programming optimum. Attention heatmaps and SHAP attributions show where the agent looks and why it decides.