中国NeoLab时刻：EverMind用3篇论文，交出全栈自进化首份答卷
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TL;DR - 量子位 covers EverMind (incubated by Shanda), a Chinese "NeoLab"-style research team that released three papers laying out a full-stack self-evolving AI approach spanning skills, agent harness, and model weights. It matters as a claimed systematic answer to recursive self-improvement while overseas labs (RSI, Ineffable, Engram, Adaption Labs) raise large rounds on mostly single-point research.
- HarnessBank (arXiv 2607.13683) separates "propose change" from "attribute change": an LLM only diagnoses failures and proposes patches, while deterministic code handles sampling and significance testing. A "Harness Gene Bank" indexes patches by WHERE × WHY semantics plus gated screening; reported 5.1–15.4% gains across seven benchmarks (Terminal-Bench-2, LiveCode, Omni-MATH, BrowseComp+, GDPval, AppWorld, SWE-bench) with paired significance z≥1.96, vs GEPA and Darwin Gödel Machine. Winning patches track model-specific "pathologies" rather than a universal harness.
- SkillCorpus (arXiv 2607.15557) curates ~821k crawled Skill.md files down to 96,401 skills across a 16-category taxonomy scored on utility, robustness, and safety, plus a fine-tuned retrieval stack; integrated into the Raven harness it improves all benchmarks, up to +7.5 points on SkillsBench, and identifies coverage vs harness gain boundaries.
- DASH (arXiv 2608.06243v1) fixes on-policy self-distillation's uniform per-step weighting by converting each step's divergence deviation from the sequence mean into an adaptive propagation gate for multi-step backward aggregation; on AIME 2024/2025 and HMMT 2025 it tops all baselines (Qwen3-1.7B: 41.87→45.07 avg; Qwen3-8B: 65.00→66.40) with no extra forward-pass cost.
- The work is framed as a four-layer stack — task (EverOS/SkillCorpus), harness (Raven), model weights (DASH), and meta-improvement — complemented by the EvoAgentBench evaluation benchmark; note this is a vendor-framed media piece, so comparative "first/leading" claims are promotional rather than independently verified.
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中国NeoLab时刻：EverMind用3篇论文，交出全栈自进化首份答卷
TL;DR - 量子位 covers EverMind (incubated by Shanda), a Chinese "NeoLab"-style research team that released three papers laying out a full-stack self-evolving AI approach spanning skills, agent harness, and model weights. It matters as a claimed systematic answer to recursive self-improvement while overseas labs (RSI, Ineffable, Engram, Adaption Labs) raise large rounds on mostly single-point research.
- HarnessBank (arXiv 2607.13683) separates "propose change" from "attribute change": an LLM only diagnoses failures and proposes patches, while deterministic code handles sampling and significance testing. A "Harness Gene Bank" indexes patches by WHERE × WHY semantics plus gated screening; reported 5.1–15.4% gains across seven benchmarks (Terminal-Bench-2, LiveCode, Omni-MATH, BrowseComp+, GDPval, AppWorld, SWE-bench) with paired significance z≥1.96, vs GEPA and Darwin Gödel Machine. Winning patches track model-specific "pathologies" rather than a universal harness.
- SkillCorpus (arXiv 2607.15557) curates ~821k crawled Skill.md files down to 96,401 skills across a 16-category taxonomy scored on utility, robustness, and safety, plus a fine-tuned retrieval stack; integrated into the Raven harness it improves all benchmarks, up to +7.5 points on SkillsBench, and identifies coverage vs harness gain boundaries.
- DASH (arXiv 2608.06243v1) fixes on-policy self-distillation's uniform per-step weighting by converting each step's divergence deviation from the sequence mean into an adaptive propagation gate for multi-step backward aggregation; on AIME 2024/2025 and HMMT 2025 it tops all baselines (Qwen3-1.7B: 41.87→45.07 avg; Qwen3-8B: 65.00→66.40) with no extra forward-pass cost.
- The work is framed as a four-layer stack — task (EverOS/SkillCorpus), harness (Raven), model weights (DASH), and meta-improvement — complemented by the EvoAgentBench evaluation benchmark; note this is a vendor-framed media piece, so comparative "first/leading" claims are promotional rather than independently verified.