Fusion works now, even with the same model: self-fusion

TrustedRouter reports that self-fusion, in which a model produces multiple research answers and then synthesizes them, can improve performance on DRACO deep-research tasks when the model is sufficiently capable. In tests graded by Claude Sonnet 4.6, Sonnet 4.6 improved from roughly 66 to 74 points after fusing ten runs, an eight-point gain with a 95% bootstrap interval of +4.6 to +11.2 across 23 tasks, while Claude Haiku 4.5 rose from about 55 to 58 across 44 tasks, a smaller gain whose confidence interval included zero. The findings suggest that fusion depends heavily on the synthesizer’s ability to distinguish correct results from a majority of weaker answers, illustrated by a case where Haiku discarded a rare correct finding in favor of an incorrect consensus while Sonnet retained it. The report also cautions that early conclusions based on only eight tasks incorrectly suggested that Haiku fusion was harmful, emphasizing the importance of larger samples and focusing on broad trends rather than small score differences. TrustedRouter positions its multi-provider routing infrastructure as useful for parallel fusion runs by reducing dependence on any single provider’s rate limits or outages, while noting that diverse frontier-model panels still outperform self-fusion by one model.

No tracked trend matches for this post yet.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.