Parallel-Synthesis

Parallel-Synthesis framework enables LLM agents to consume KV caches directly, cutting synthesis latency 2.5x–11x

Researchers introduce Parallel-Synthesis, a plug-and-play framework that allows a synthesizer LLM to directly consume KV caches produced by parallel worker agents instead of concatenating their textual outputs. The system combines a cache mapper for calibrating independently generated branch caches with a fine-tuned synthesizer adapter, trained via distillation from standard text-concatenation synthesis. Evaluated across nine datasets spanning math, science QA, code generation, GAIA, and multi-agent database diagnosis, it matches or outperforms text-based synthesis on seven datasets while reducing time-to-first-token by 2.5x–11x. The work proposes a fundamentally different interface for multi-agent synthesis that avoids redundant prefill computation inherent in sequential text merging.