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The GitHub project GVS5H proposes a training-free ledger-based zero-shot self-orchestration method that lets multiple new instances of the same model decompose problems and collaborate through a shared file system (storing plans, notes, and the current solution). On 100 of the latest LiveCodeBench Hard problems, the method yields up to a 25.6 percentage point improvement with a fixed backend: orchestrated Qwen3.8 Flash Next reaches 93.0% pass@1, higher than Fable 5's 90.4%, at 9% of its cost; the locally deployed open-source Qwen3.8-27B rises from 66.8% to 92.4%. The authors also note that the improvement is not universal, with some models staying flat or performing worse, and transcript analysis attributes the gains to task decomposition and persistent context.
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GVS5H: Five Qwen3.8-27B instances match Claude on LiveCodeBench Hard…Developments
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GVS5H proposes a training-free ledger self-orchestration method
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Oct 25
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GVS5H: Five Qwen3.8-27B instances match Claude on LiveCodeBench Hard…
The GitHub project GVS5H proposes a training-free ledger-based zero-shot self-orchestration method that lets multiple fresh instances of the same model decompose problems and collaborate through a shared file system (storing plans, notes, and the current solution). On 100 of the latest LiveCodeBench Hard problems, the method yields up to a 25.6 percentage point improvement on a fixed backend: orchestrated Qwen3.8 Flash Next reaches 93.0% pass@1, above Fable 5's 90.4%, at 9% of its cost; the locally deployed open-source Qwen3.8-27B rises from 66.8% to 92.4%. The authors also note that the improvement is not universal, with some models staying flat or performing worse, and transcript analysis attributes the gains to task decomposition and persistent context.