Long-horizon tool-use agents need not only to search and plan, but also to decide when to finalize. We study late-stage pressure states, in which an agent is biased toward submitting a final answer that appears complete and polished while key constraints remain unresolved. We first train a linear probe to show that this pressure state is identifiable from the agent’s hidden states. Then, we use activation interventions along this pressure direction and find that shifting the hidden states changes both the pressure score and whether the agent continues tool use or submits early. Through controlled context manipulations, we further see that the pressure is mitigated by constraint clarity and action mapping. Based on these findings, we propose Probe-Sensed Pressure Relief (PSPR), a plugin that applies lightweight pressure relief direction under moderate pressure and moves to structured organization under high pressure risk. Experiments on multiple long-horizon benchmarks show that our method consistently strengthens existing agent methods.

E-commerce search systems typically separate query-item relevance matching from downstream personalized ranking. While effective for large-scale serving, this design leaves relevance levels dominated by objective query-item matching, forcing follow-up ranking to operate over candidate structures that may not reflect user behavioral preference. We define a new personalized behavioral relevance task, which predicts whether a relevance-stage candidate item is supported by the user’s behavioral evidence under current query intent. We develop a corresponding framework, BRIDGE, for Behavioral Relevance Inference with Dynamic Guidance from Evidence. It organizes multi-timescale behavioral context from online logs, obtains reliable supervision with multi-perspective annotation and adjudication, and trains an LLM to produce structured evidence-grounded decision rationales. It particularly incorporates sample-specific rubric rewards into GRPO to improve generated reasoning traces. Offline experiments validate that BRIDGE improves both prediction accuracy and reasoning quality. Online A/B tests show gains of 0.19% in active user-days and 0.67% in transaction-related user-days, both significant in statistics.