Freight brokerages negotiate thousands of carrier rates daily under dynamic pricing conditions where models frequently revise targets mid-conversation. Classical time-dependent concession frameworks use a fixed shape parameter $β$ that cannot adapt to these updates. Deriving $β$ from the live spread enables adaptation but introduces a new problem: a pricing shift can cause the formula to retract a previous offer, violating monotonicity. LLM-powered brokers offer flexibility but require expensive reasoning models, produce non-deterministic pricing, and remain vulnerable to prompt injection. We propose a two-index anchor-and-resume framework that addresses both limitations. A spread-derived $β$ maps each load's margin structure to the correct concession posture, while the anchor-and-resume mechanism guarantees monotonically non-decreasing offers under arbitrary pricing shifts. All pricing decisions remain in a deterministic formula; the LLM, when used, serves only as a natural-language translation layer. Empirical evaluation across 115,125 negotiations shows that the adaptive $β$ tailors behavior by regime: in narrow spreads, it concedes quickly to prioritize deal closure and load coverage; in medium and wide spreads, it matches or exceeds the best fixed-$β$ baselines in broker savings. Against an unconstrained 20-billion-parameter LLM broker, it achieves similar agreement rates and savings. Against LLM-powered carriers as more realistic stochastic counterparties, it maintains comparable savings and higher agreement rates than against rule-based opponents. By decoupling the LLM from pricing logic, the framework scales horizontally to thousands of concurrent negotiations with negligible inference cost and transparent decision-making.

翻译：货运经纪公司在动态定价条件下每日需协商数千条承运人费率，此时模型常在对话中途调整目标。经典的时间依赖让步框架使用固定形状参数 $β$，无法适应此类动态更新。从实时价差推导 $β$ 虽能实现适应性，但引发新问题：定价偏移可能导致公式收回先前报价，违反单调性。基于大语言模型（LLM）的经纪人虽具灵活性，但需昂贵推理模型、产生非确定性定价，且易受提示注入攻击。我们提出双指标锚定与恢复框架，同时解决上述两种局限。基于价差的 $β$ 将每批货物的利润结构映射至正确让步姿态，而锚定与恢复机制确保在任意定价偏移下报价单调非减。所有定价决策均受确定性公式约束；LLM（若使用）仅作为自然语言翻译层。基于115,125轮谈判的实证评估表明，自适应 $β$ 按场景调整行为：窄价差时快速让步以优先达成交易与覆盖货运，中等及宽价差时在经纪人节省方面达到或超越最优固定 $β$ 基线。相比未受约束的200亿参数LLM经纪人，本框架达成相似成交率与节省效果。面对LLM驱动的承运人（更逼真的随机对手），相比基于规则的对手，本框架维持可比节省且达成更高成交率。通过将LLM与定价逻辑解耦，本框架可水平扩展至数千场并发谈判，推理成本可忽略且决策透明。