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Add de-identified model-index card: HumanEval self-reported results (combination 90.2%, solo 89.0%)

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+ ---
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+ license: other
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+ license_name: sofron-project-license
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+ pipeline_tag: text-generation
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+ tags:
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+ - code-generation
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+ - agent-architecture
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+ model-index:
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+ - name: sofron-v7 (combination)
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+ results:
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+ - task:
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+ type: text-generation
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+ dataset:
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+ name: openai_humaneval
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+ type: openai_humaneval
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+ metrics:
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+ - name: pass@1
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+ type: pass@1
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+ value: 90.2
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+ source:
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+ name: Sofron HumanEval benchmark writeup (own harness, self-reported)
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+ url: https://github.com/s4h8a/sofron-benchmark-results
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+ - name: sofron-v7 (solo)
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+ results:
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+ - task:
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+ type: text-generation
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+ dataset:
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+ name: openai_humaneval
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+ type: openai_humaneval
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+ metrics:
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+ - name: pass@1
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+ type: pass@1
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+ value: 89.0
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+ source:
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+ name: Sofron HumanEval benchmark writeup (own harness, self-reported)
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+ url: https://github.com/s4h8a/sofron-benchmark-results
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+ ---
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+
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+ # Sofron v7
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+
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+ Sofron is a combination-architecture coding agent: its own model weights (v7)
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+ paired with advisory teacher models (GLM-5.2 and DeepSeek) gathered in
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+ parallel as candidates on each problem. Sofron synthesizes its own final
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+ answer from their input, using its own judgment as the foundation rather
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+ than deferring to any single advisor.
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+
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+ **This is a metadata-only repository.** No model weights are hosted here;
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+ it exists to record a real, execution-graded, self-reported benchmark
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+ result with a citable, verifiable source.
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+
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+ Contact / homepage: https://sofron.one
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+
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+ ## HumanEval result (self-reported, not third-party verified)
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+
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+ | Condition | Pass@1 (164/164 problems) |
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+ |---|---|
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+ | combination (v7 + GLM-5.2 + DeepSeek synthesis) | **90.2%** (148/164) |
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+ | solo (v7 alone) | 89.0% (146/164) |
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+
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+ Full methodology, raw results log, and harness scripts:
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+ https://github.com/s4h8a/sofron-benchmark-results
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+
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+ Sofron combines advisory input from external teacher models with its own
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+ model weights — it is not a frontier proprietary model. This result should
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+ be read as an honest, real placement rather than a state-of-the-art claim.