From Experience to Deliberation: An Adaptive Cognitive Harness for LLM in Long-Term Traffic Simulation

Long-term traffic simulation with large language model (LLM) agents requires stable routines and adaptation to disruptions under limited personal information. Existing agents use memory, planning, and reflection to sustain behavior, but retaining experience does not determine when acquiring information or invoking an LLM will improve an action. We propose an adaptive cognitive harness that couples persistent beliefs and habits with execution-supervised allocation of cognitive effort. Beliefs encode uncertain outcome expectations and habits encode context-dependent repetition; together with accessible evidence and constraints, they guide fast action, querying, LLM comparison, or their combination. We use paired simulator rollouts to train a lightweight predictor of task gains and resource costs relative to FAST. During deployment, the predictor remains frozen while observed outcomes revise personal state. Across five 30-day world seeds evaluated in a paired design, the hybrid policy achieves lower mean task loss than all four mandatory-LLM controls while using approximately 40–44% fewer online LLM tokens, with quality gains concentrated in the disruption period. These results establish a favorable quality–cost trade-off for selective deliberation and support an execution-grounded approach to longterm LLM simulation: cognitive effort is allocated according to decision demands rather than uniformly spent on every action. Code will be released at https://anonymous.4open.science/r/Adaptive-Cognitive-Harness-CB35/.