---
id: llm-evolution-and-meta-optimization/10-sources
title: 'LLM evolution and meta-optimization: 10. Sources'
report: https://ai.hiina.space/reports/llm-evolution-and-meta-optimization.md
section: 10
version: 1
updated: '2026-10-27'
author: Hiina
---

Index of everything on this site: https://ai.hiina.space/index.md

# LLM evolution and meta-optimization: 10. Sources

- Section 10 of 10 of the report "LLM evolution and meta-optimization" by Hiina (v1, updated 2026-10-27)
- Report overview and contents: https://ai.hiina.space/reports/llm-evolution-and-meta-optimization.md
- Previous: https://ai.hiina.space/reports/llm-evolution-and-meta-optimization/09-conclusion.md

## 10. Sources

Every source linked inline above, in order of first citation:

- [Akiba et al., Nature Machine Intelligence 2025](https://arxiv.org/pdf/2403.13187)
- [PopuLoRA, 2026](https://arxiv.org/html/2605.16727v1)
- [Qiu et al., ICML 2026](https://arxiv.org/html/2509.24372v2)
- [Darwin Godel Machine](https://arxiv.org/pdf/2505.22954)
- [Huxley-Godel Machine](https://arxiv.org/pdf/2510.21614v2)
- [AlphaEvolve](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/)
- [Cloud et al., subliminal learning](https://alignment.anthropic.com/2025/subliminal-learning/)
- [Anthropic](https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks)
- [Lorraine et al. 2019](https://arxiv.org/abs/1911.02590)
- [Wu et al. 2018](https://arxiv.org/abs/1803.02021)
- [LES](https://arxiv.org/abs/2211.11260)
- [LGA](https://arxiv.org/pdf/2304.03995)
- [DiscoPOP](https://arxiv.org/abs/2406.08414)
- [README](https://github.com/KellerJordan/modded-nanogpt)
- [Frank](https://arxiv.org/pdf/2507.18549)
- [Laufer, Oderinwale, Kleinberg](https://arxiv.org/pdf/2508.06811)
- [Wortsman et al.](https://ar5iv.labs.arxiv.org/html/2203.05482)
- [Ilharco et al.](https://ar5iv.labs.arxiv.org/html/2212.04089)
- [Yu et al.](https://arxiv.org/pdf/2311.03099)
- [Ainsworth et al.](https://arxiv.org/pdf/2209.04836)
- [Yang et al. survey](https://arxiv.org/html/2408.07666v5)
- [tianpan.co](https://tianpan.co/blog/2026/04/12/model-merging-in-production)
- [Sakana](https://sakana.ai/evolutionary-model-merge/)
- [arXiv HTML](https://arxiv.org/html/2403.13187v2)
- [Sakana CycleQD](https://sakana.ai/cycleqd/)
- [Abrantes, Lange, Tang](https://arxiv.org/pdf/2508.16204)
- [arXiv 2602.08218](https://arxiv.org/pdf/2602.08218)
- [Huang et al.](https://arxiv.org/pdf/2307.13269)
- [Liu et al., COLM 2024](https://arxiv.org/pdf/2401.08565)
- [paper](https://arxiv.org/pdf/2507.14805)
- [TechRadar](https://www.techradar.com/computing/artificial-intelligence/openai-says-deepseek-used-its-models-illegally-and-it-has-evidence-to-prove-it-new-report-claims)
- [third-party ComfyUI docs](https://mintlify.wiki/Comfy-Org/ComfyUI/advanced/model-merging)
- [Liu et al., ECCV 2022](https://arxiv.org/pdf/2206.01714)
- [Wang et al.](https://arxiv.org/abs/2406.04692)
- [Jaderberg et al. 2017](https://arxiv.org/pdf/1711.09846)
- [Borghi, Im, Pareschi](https://arxiv.org/html/2603.19808v2)
- [Salimans et al.](https://arxiv.org/abs/1703.03864)
- [Malladi et al.](https://arxiv.org/abs/2305.17333)
- [ICML 2026 poster](https://icml.cc/virtual/2026/poster/62279)
- [arXiv](https://arxiv.org/pdf/2511.16652)
- [PMLR](https://proceedings.mlr.press/v306/sarkar26a.html)
- [Hoy, Wang, Pan](https://arxiv.org/abs/2604.01499)
- [Lehman et al.](https://arxiv.org/abs/2206.08896)
- [DeepMind](https://deepmind.google/blog/funsearch-making-new-discoveries-in-mathematical-sciences-using-large-language-models/)
- [arXiv](https://arxiv.org/abs/2506.13131)
- [PyPI](https://pypi.org/p/openevolve)
- [Sakana](https://sakana.ai/shinka-evolve/)
- [arXiv](https://arxiv.org/pdf/2509.19349v1)
- [Fernando et al.](https://arxiv.org/abs/2309.16797)
- [Agrawal et al.](https://arxiv.org/abs/2507.19457)
- [Samvelyan et al.](https://arxiv.org/abs/2402.16822)
- [Faldor et al.](https://arxiv.org/abs/2405.15568)
- [Hughes et al.](https://arxiv.org/abs/2406.04268)
- [Hu, Lu, Clune](https://arxiv.org/abs/2408.08435)
- [Robeyns et al.](https://arxiv.org/abs/2504.15228)
- [Sakana DGM](https://sakana.ai/dgm/)
- [The Register](https://www.theregister.com/2025/06/02/self_improving_ai_cheat/)
- [arXiv 1502.03492](https://arxiv.org/abs/1502.03492)
- [arXiv 1703.04782](https://arxiv.org/abs/1703.04782)
- [Yang et al.](https://arxiv.org/abs/2203.03466)
- [Dey et al.](https://arxiv.org/abs/2505.01618)
- [Wen et al., ICLR 2026](https://arxiv.org/pdf/2509.02046v2)
- [Liu et al.](https://arxiv.org/abs/2502.16982v1)
- [Andrychowicz et al.](https://arxiv.org/abs/1606.04474)
- [arXiv 1810.10180](https://arxiv.org/pdf/1810.10180)
- [Metz et al. 2022](https://arxiv.org/pdf/2211.09760)
- [Moudgil et al.](https://arxiv.org/html/2501.12670v2)
- [Moudgil, Knyazev, Belilovsky](https://www.arxiv.org/pdf/2602.19142)
- [Chen et al.](https://arxiv.org/abs/2302.06675)
- [Lange](https://arxiv.org/pdf/2212.04180)
- [arXiv 2402.05828](https://arxiv.org/pdf/2402.05828)
- [PNAS 2005](https://cris.haifa.ac.il/en/publications/spontaneous-evolution-of-modularity-and-network-motifs/)
- [METR](https://arxiv.org/abs/2411.15114)
- [Chan et al.](https://arxiv.org/abs/2410.07095)
- [Zhao et al.](https://arxiv.org/pdf/2506.22419)
- [v1](https://arxiv.org/abs/2408.06292)
- [v2](https://arxiv.org/abs/2504.08066)
- [Prime Intellect](https://primeintellect.ai/auto-nanogpt)
- [Anthropic](https://www.anthropic.com/institute/recursive-self-improvement)
- [Anthropic](https://www.anthropic.com/institute/measuring-pace-of-ai-development)
- [Chan et al.](https://arxiv.org/pdf/2603.03992)
- [Jordan](https://arxiv.org/html/2404.00498v2)
- [arXiv 2303.16200](https://arxiv.org/abs/2303.16200)
- [PhilSci-Archive](https://philsci-archive.pitt.edu/23903)
- [author summary](https://forum.effectivealtruism.org/posts/eEjhSqxAmv58rKJWt/the-selfish-machine)
- [Frontiers 2022](https://frontiersin.org/articles/10.3389/fevo.2022.823588/full)
- [Comptes Rendus Biologies](https://comptes-rendus.academie-sciences.fr/biologies/item/10.5802/crbiol.198.pdf)
- [ICLR 2025](https://arxiv.org/pdf/2407.04503)
- [arXiv 2412.10270](https://arxiv.org/abs/2412.10270v1)
- [Shumailov et al., Nature 2024](https://www.pure.ed.ac.uk/ws/portalfiles/portal/460496122/ShumailovEtalNature2024AIModelsCollapseWhen.pdf)
- [Congdon](https://benjamincongdon.me/blog/2025/12/26/On-and-Contra-Chalmers-on-LLM-Interlocutors/)
- [Marks, Lindsey, Olah](https://alignment.anthropic.com/2026/psm)
- [Dehghani et al.](https://arxiv.org/pdf/2107.07002)
- [Singh et al.](https://arxiv.org/abs/2504.20879v2)
- [Epoch](https://epoch.ai/data-insights/open-weights-vs-closed-weights-models)
- [NeurIPS workshop](https://neurips.cc/virtual/2025/130220)
- [arXiv 2405.13058](https://arxiv.org/html/2405.13058v1)
- [Cebrian](https://arxiv.org/abs/2603.22287)
- [iclr.cc](https://iclr.cc/virtual/2025/poster/28195)
- [arXiv v5](https://arxiv.org/html/2404.04671v5)
- [GitHub](https://github.com/Nicolas-Yax/PhyloLM)
- [arXiv](https://arxiv.org/abs/2502.12150)
- [repo](https://github.com/locuslab/llm-idiosyncrasies)
- [Nikolic et al.](https://arxiv.org/html/2502.00706v2)
- [Bruckner](https://arxiv.org/pdf/2607.10252)
- [arXiv](https://arxiv.org/abs/2501.12619)
- [ACL](https://aclanthology.org/2025.acl-long.248/)
- [Rawat et al.](https://arxiv.org/abs/2607.09692)
- [Xu et al.](https://arxiv.org/abs/2602.03812)
- [LessWrong comments](https://www.greaterwrong.com/posts/G4M5xE3HeReisKqLc/some-models-don-t-identify-with-their-official-name)
- [arXiv](https://arxiv.org/html/2312.04828v5)
- [arXiv](https://arxiv.org/abs/2410.14273)
- [Horwitz et al.](https://arxiv.org/abs/2405.18432)
- [iclr.cc](https://iclr.cc/virtual/2026/poster/10008260)
- [arXiv 2507.03014](https://arxiv.org/html/2507.03014v1)
- [CXO Digital Pulse](https://www.cxodigitalpulse.com/huawei-denies-copying-alibabas-qwen-model-in-pangu-pro-moe-development/)
- [HF docs](https://huggingface.co/docs/hub/en/model-cards)
- [ModelAtlasData](https://huggingface.co/datasets/hrluo/ModelAtlasData)
- [GitHub](https://github.com/eliahuhorwitz/Model-Atlas)
- [arXiv](https://arxiv.org/abs/2503.10633)
- [Epoch](https://epoch.ai/data/ai-models)
- [LLMsPracticalGuide](https://github.com/Mooler0410/LLMsPracticalGuide)
- [Gao & Gao](https://arxiv.org/abs/2307.09793)
- [Augment overview](https://www.augmentcode.com/learn/system-prompts-28-ai-coding-tools)
- [Anthropic](https://www.anthropic.com/legal/commercial-terms)
- [Variant](https://variant.fund/articles/license-distill-law-distillation-anthropic/)

---

Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space