大型语言模型越来越多地充当代理系统, 的执行引擎，但它们仍然通过顺序文本接口消耗上下文。这与现代结构化代理工作流程, 不匹配，其中独立分支探索子任务, 检索证据, 或在最终综合步骤之前生成候选解决方案。现有系统通常通过连接它们的文本输出,来合并这些分支，这会丢弃并行结构并产生冗余的预填充计算。在这项工作,中，我们引入了Parallel-Synthesis,，一个即插即用的框架，使合成器能够直接使用并行工作代理生成的KV缓存。并行合成将校准独立生成的分支缓存的缓存映射器与可从该非顺序缓存接口生成的微调合成器适配器相结合。我们使用将合成器暴露给并行缓存上下文的数据来训练并行合成, 教授跨缓存分支, 的聚合，并从基于标准文本串联的合成中提取推理行为。在涵盖数学, 科学 QA, 代码生成, GAIA, 和多智能体数据库诊断, 的九个下游数据集上，并行合成在七个数据集上匹配或优于基于文本的合成，并且与其他两个数据集保持接近。它还将首次令牌时间缩短了 2.5x-11x,，这表明基于缓存的直接合成是一个有前景的接口，可以在并行代理分支上进行更原生、更高效的合成。

Large language models increasingly serve as execution engines for agentic systems, yet they still consume context through a sequential text interface. This creates a mismatch with modern structured agent workflows, in which independent branches explore subtasks, retrieve evidence, or generate candidate solutions before a final synthesis step. Existing systems typically merge these branches by concatenating their textual outputs, which discards the parallel structure and incurs redundant prefill computation. In this work, we introduce Parallel-Synthesis, a plug-and-play framework that enables a synthesizer to directly consume the KV caches produced by parallel worker agents. Parallel-Synthesis combines a cache mapper that calibrates independently generated branch caches with a fine-tuned synthesizer adapter that enables generation from this non-sequential cache interface. We train Parallel-Synthesis using data that exposes the synthesizer to parallel cache contexts, teaches aggregation across cached branches, and distills reasoning behavior from standard text-concatenation-based synthesis. Across nine downstream datasets spanning math, science QA, code generation, GAIA, and multi-agent database diagnosis, Parallel-Synthesis matches or outperforms text-based synthesis on seven datasets and remains close on the other two. It also reduces time-to-first-token by 2.5x-11x, suggesting that direct cache-based synthesis is a promising interface for more native and efficient synthesis over parallel agent branches.

科目: 人工智能 (cs.AI); 计算和语言 (cs.CL)

Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)