Autonomous agents excel in self-improvement through reflection and iterative refinement, which reuse successful task trajectories as in-context examples to assist subsequent reasoning. However, shifting across tasks often introduces a context mismatch. Hence, existing approaches either discard the trajectories or manipulate them using heuristics, leading to a non-negligible fine-tuning cost or unguaranteed performance. To bridge this gap, we reveal a context-trajectory correlation, where shifts of context are highly parallel with shifts of trajectory. Based on this finding, we propose BrIdge contextual gap FoR imprOvised trajectory STeering (Bifrost), a training-free method that leverages context differences to precisely guide the adaptation of previously solved trajectories towards the target task, mitigating the misalignment caused by context shifts. Our trajectory adaptation is conducted at the representation level using agent hidden states, ensuring trajectory transformation accurately aligns with the target context in a shared space. Across diverse benchmarks, Bifrost consistently outperforms existing trajectory reuse and finetuned self-improvement methods, demonstrating that agents can effectively leverage past experiences despite substantial context shifts.

翻译：自主智能体通过反思与迭代优化在自我改进方面表现出色，这类方法通常将成功的任务轨迹作为情境示例重复使用，以辅助后续推理。然而，任务间的切换常会引入情境失配问题。因此，现有方法要么直接丢弃这些轨迹，要么依赖启发式规则对其进行调整，导致不可忽略的微调成本或性能无法保证。为弥合这一差距，本文揭示了情境与轨迹之间的关联性，即情境的迁移与轨迹的迁移高度并行。基于这一发现，我们提出了面向即兴轨迹引导的情境差异弥合方法（Bifrost），这是一种无需训练的方法，其利用情境差异精确引导已解决轨迹向目标任务的适配，从而缓解因情境迁移导致的错位问题。我们的轨迹适配在表示层面通过智能体隐藏状态实现，确保轨迹变换在共享空间中与目标情境精确对齐。在多种基准测试中，Bifrost 始终优于现有的轨迹复用方法及经过微调的自我改进方法，证明智能体即使面临显著的情境迁移，仍能有效利用过往经验。