不值得再投入一个Token：面向高效深度研究智能体的边际价值估计

摘要

长时程研究智能体通过迭代检索、聚合与综合来解决开放式任务，但上下文会迅速增长，而额外证据的边际价值往往随之下降。这导致最终报告生成时产生不必要的令牌成本、更高的延迟以及噪声更大的输入。我们研究了深度研究智能体中上下文管理的边际价值估计问题，并首次提出了跨流水线的剪枝策略的系统性阶段感知比较。我们在检索前、检索后和综合前阶段评估了轻量级启发式标准以及一种学习型价值模型。结果表明，剪枝的效果更多地取决于剪枝应用的阶段，而非具体的评分规则：早期剪枝带来最大的端到端节省，而后期剪枝主要优化最终综合上下文。轻量级启发式方法可将令牌使用量降低多达73%，且几乎不造成质量下降；学习型剪枝在特定权衡下仍具有竞争力；没有任何单一方法能在质量、效率和忠实性方面全面占优。这些发现为设计高效的长时程智能体系统提供了实用指导。

English

Long-horizon research agents solve open-ended tasks through iterative retrieval, aggregation, and synthesis, but context grows rapidly while the marginal value of additional evidence often declines. This leads to unnecessary token cost, higher latency, and noisier inputs for final report generation. We study marginal value estimation for context management in deep research agents and present the first systematic stage-aware comparison of pruning strategies across the pipeline. We evaluate lightweight heuristic criteria and a learned value model at pre-retrieval, post-retrieval, and pre-synthesis stages. Our results show that pruning effectiveness depends more on where pruning is applied than on the specific scoring rule: early pruning yields the largest end-to-end savings, while later pruning mainly refines the final synthesis context. Lightweight heuristics reduce token usage by up to 73% with little quality degradation, learned pruning remains competitive on selected trade-offs, and no single method dominates across quality, efficiency, and faithfulness. These findings provide practical guidance for designing efficient long-horizon agentic systems.