The Real Power Map Behind the 75-Page RSI Survey: 33 Authors, 72 Companies, and 6 Days of Media Spin

Forum topic · 二一 · 2026-09-16

Summary

This follow-up audit of the survey paper "The Last AI Built by Humans" (arXiv 2609.11873) moves from the paper's content to its political and industrial structure. By cross-checking the author block against personal homepages, it shows the survey is actually driven by Xuanhe Zhou (SJTU, corresponding author) and Fan Wu, with an economics undergraduate at Renmin University as project lead — while famous names like Jie Zhou (whose company Frontis AI appears unstarred in Appendix B) and Zhiyuan Liu (ModelBest) serve mainly as endorsements. The post fully maps Appendix B: 72 companies across six archetypes and roughly 142 product rows, noting that AlphaEvolve is downgraded to B0, MIT's SEAL is never cited, and OpenRSI is compressed to one line. It then traces six days of coverage: English-language media framed it as a China-vs-US race while academic communities ignored it, and Chinese coverage focused on science communication and critique. The author argues the survey's real product is "ledger power" — the authority to classify every RSI system — and flags verified limitations and falsifiable predictions.

> This is the second audit of the RSI survey "The Last AI Built by Humans." The first post (Sept 14) reconciled the paper's content: the five-level framework, HCI, three challenges, and genealogy mappings. This one audits the paper's ecosystem: who wrote it, how the 72 companies in Appendix B were ranked, and how Chinese and English circles reacted over the six days after release. Sources: a full second crawl of arXiv 2609.11873 (~320k characters), the PDF author block, and five parallel sub-analyses (framework close reading, second-half reading, appendix line-by-line, community reaction, team background) cross-checked across ~60 sources. Everything defers to the original text and primary homepages.

1. The Author List Deconstructed: 33 People, 9 Institutions — and Not the Two Names You Expect

The first post confirmed that Zhiyuan Liu and Jie Zhou are on the list. But disassembling the author block, homepages, and affiliation numbering reveals a structure far more interesting than "big names lending their names":

1. The real drivers: Xuanhe Zhou (corresponding author) + Fan Wu (last author). Zhou, born 1997, is one of the youngest assistant professors at SJTU's CS school and a PhD student of Guoliang Li at Tsinghua (Li himself is on the list, position 27 — advisor and student side by side). Fan Wu is a distinguished professor and executive vice dean at SJTU CS. "Theseus Labs" is not a company but the lab brand of this team: the website is one sentence, the GitHub org (theseus-labs-rsi) has only an awesome-rsi literature repo (47 stars) and a homepage repo — no funding, no corporate registration, no product code.

2. First author Yi Duan: an economics undergraduate at Renmin University (class of 2023). His homepage describes him as "Project Lead, Theseus Labs" (Aug–Sep 2026), after a remote internship at Shanghai AI Lab (Sep 2025–Jun 2026, on BioMatrix). An undergraduate coordinating a 75-page survey, a 33-author team, and 491 references is rare in academia — and fully public (duanyi516.github.io). All five co-first authors (Yi Duan / Ying Liu / Zirui Tang / Haodong Chen / Jun Zhou) appear on Xuanhe Zhou's student list.

3. Jie Zhou's actual role: author and surveyed subject. This is the most elegant link in the list. Jie Zhou (position 28, Tsinghua EE chair professor, director of Shanghai AI Lab) founded Frontis AI (Xianyuan) after leaving JD — and Appendix B contains exactly this row: Frontis AI / Xianyuan | OpenRSI, Frontis-MA1 | AI4AI/self-improving agents | skills; memory; harness; weights | L2-L3. His student Kaiyan Zhang (position 24, OpenReview shows he is now Frontis CTO) is the sole corresponding author of the Frontis-MA1 paper (arXiv 2607.28568). In other words: the teacher endorses the survey while the student's company product enters the industry map — the OpenRSI open-source effort and the Theseus survey are not merely "mutually referencing within one network," they are two edges of one power topology.

4. Every other seat has an owner. Zhiyuan Liu (position 29, THUNLP director) is co-founder and chief scientist of ModelBest (面壁智能) — the §5.4 ModelBest section (ForgeTrain matching Megatron-LM v0.15 in 8 hours, surpassing it in 1.5–2.5 days; MFU 40.1→44.1% @0.5B) is a first-person account from his ecosystem. Two ByteDance authors correspond to the §5.2 Lark section; one Xiaohongshu author; Zhoufutu Wen is listed under Humanlaya (§5.3) but his homepage shows he has been at ByteDance SEED since Sep 2023 — affiliation in doubt, deferring to the paper's self-report. Shanghai AI Lab's Conghui He (OpenDataLab/MinerU founder) connects via Jie Zhou's current post. The 33-person list = SJTU-led (the first 17 positions nearly all dual-affiliated SJTU + Theseus Labs) + Tsinghua endorsements via three lines (Guoliang Li, Zhiyuan Liu/ModelBest, Jie Zhou/Frontis) + a corporate matrix (ByteDance / ModelBest / Xiaohongshu / Humanlaya).

Conclusion: "SJTU + Jie Zhou/Zhiyuan Liu" is the publicity cover of this paper, not its power structure. A survey establishing an official ledger for China's RSI ecosystem is drafted by a 97-born assistant professor leading undergrad/master students, with endorsement from three heavyweight Tsinghua scholars and five companies — a structure that is itself part of the "systematic layout" of the RSI agenda in China (isomorphic to the ICLR 2026 RSI Workshop, supported by Tencent and BAAI).

2. Appendix B in Full: 72 Companies, Six Archetypes, ~142 Product Rows

The paper organizes by archetype, not geography — six categories:

| Cat. | Name | Companies | Representative players (with levels) | |---|---|---|---| | A | Frontier model & general agent labs | 17 | OpenAI (Symphony/GPT-Red/AgentKit L1-L2; self-improving tax agents L4 cand.); Anthropic ("When AI Builds Itself" tagged "L5 target" — vision, not reality; Agent Skills an L1 enabler); GDM (AlphaEvolve tagged B0); Meta (HyperAgents L5 cand.); NVIDIA/Microsoft; Qwen/DeepSeek/Tencent AI Lab (R-Zero L2-L3)/ByteDance Seed/MiniMax/Moonshot/Zhipu; US: Poolside/TML/Nous | | B | RSI-native / AI4AI companies | 14 | Weco (AIDE2 L5 cand.), Sakana (DGM L5 cand.), Recursive, Imbue; Chinese newcomers: MetaCircle (ComfyResearch/OPHIS, "L2; L5 vision"), Frontis AI/Xianyuan (OpenRSI, Frontis-MA1, L2-L3), EvoMap (Evolver/GEP/GeneBench, L2-L3), Endless Frontier (BigBang-v1), Chaoyan; Theseus itself (Argus L2-L3, SetupX L3) | | C | Autonomous R&D & scientific discovery | 14 | Periodic Labs (L3 cand.), FutureHouse, Axiom Math (Putnam/IMO), Harmonic, Lila, Karpathy autoresearch (L2), Prime Intellect | | D | Agent optimization/eval/learning infrastructure | 15 | Warp, Factory (among the highest industrial ratings: L4), LangChain, Replit, Braintrust, Mechanize, Goodfire | | E | Embodied / world models / continual adaptation | 8 | AI2 Robotics, X Square Robot, Galaxea, Synapx, MirrOS | | F | Persistent memory & personal AI | 4 | Engram, Lemon AI, EverMind, Mindverse |

Four details nobody pointed out before:

AlphaEvolve is deliberately downgraded. It appears exactly once in the whole text, in Table 12, under Google DeepMind, tagged B0 (task-level program search), with zero discussion in the body. Compare DGM: ~10 mentions and two dedicated sections. The paper intentionally separates "program search" from "improvement loops" — AlphaEvolve is strong at the former, lacks persistent self-modification in the latter, so it stays out of the RSI narrative. Likewise MIT's SEAL (Self-Adapting LMs) is never cited — even the phrase "self-adapting" is absent. An official ledger's power lies half in inclusion, half in silence.
Frontis AI/Xianyuan carries no asterisk. Entries like Chaoyan, Wuya Zhiyuan, and Naive.ai carry a "corporate identity needs stronger verification" marker; Frontis does not — the authors deemed its identity beyond question. Given that the CTO's advisor sits on the author list, this "no verification needed" reads more like internal confidence than external due diligence.
Of the 72 companies, roughly 10 are confirmably Chinese (+~12 suspected) vs. ~36–38 American — yet all six deep cases in §5 (Theseus/Lark/Humanlaya/ModelBest/Tencent Hunyuan/Agent-Native) are Chinese practices and not counted in Table 12. The directory is global; the case studies are Chinese — a precise description of the survey's geopolitical structure.
The EvoMap mystery. Category B has one row: EvoMap | Evolver / GEP / GeneBench | shared code evolution | genes/capsules; code assets | generate; test; publish; reuse; adapt | L2-L3. Three proper nouns — gene/capsule, GEP, Evolver — hit simultaneously. If any reader happens to run a gene/capsule/GEP-architecture evolver toolchain, it has already been classified L2-L3 by this table. Public information on this entry is near zero; treat as single-source and unverified.

3. Genealogy Addendum (Landscape Dimension)

Three new facts on the landscape axis:

1. HGM is not only in the body — it is used as RQGM's beaten baseline. In §3.6.2 and §4.3.3: RQGM (Red Queen Gödel Machine) reports 71.7% pass@1 on held-out Polyglot coding tasks vs. 69.9% for the HGM-H baseline, with fewer search tokens. The narrative chain is DGM (lineage archive) → HGM (lineage meta-selection) → RQGM (the evaluator also co-evolves) — a three-stage evolution story within the Gödel Machine family, with HGM as the middle link: "introduces lineage-based meta-selection without adapting the selection rule itself." 2. OpenRSI and Frontis-MA1 occupy two rows with six identical fields, verbatim. The paper gives no difference between them — for a project whose program is "setting improvement rate as the optimization objective," being compressed into one undifferentiated L2-L3 label is itself a signal: when the official ledger absorbs you, it will not use your self-narrative. 3. The ten most-discussed systems (by mentions plus dedicated sections): DGM, VOYAGER, SEAgent, Metis, autoresearch, RQGM, AIDE2, Gödel Agent, Theseus, A-Evolve-Training. Only HGM's close family neighbors from the genealogy make this circle — a lineage's freshness (per the first post: NeoHorse missed the cutoff by two days) and the depth of its absorption are two different accounts.

4. Six Days of Media Battle: The Temperature Gap Over an Apocalyptic Title

Release window Sept 10 → Sept 16:

English circle — hot on X, cold in academic communities; the frame is geopolitical. The peak was @Dr_Singularity (Sep 13): "meanwhile in China / 'The Last AI Built by Humans' — a new 75 page paper lays out a roadmap toward recursive self-improvement" — 33k views, 890 likes, 132 reposts, with the top reply (912 likes): "China advancing towards the very thing we are shying away from because we are too scared!" The fuel was not the framework but the contrast "US debates slowing down, China publishes a roadmap" — SCMP (Sep 14) headlined "Chinese researchers chart 5-stage path," adding "US firms have an early lead due to superior access to compute." Academia barely noticed: Hacker News saw four independent submissions (including DAIR.AI), all scoring 1–2 points with 2 total comments; in r/MachineLearning's popular thread "RSI is not happening" it was cited ironically ("I swear what is happening in the last couple of weeks has to be some psyops bullshit"); Hugging Face Papers gave 77 upvotes, the only "discussion" being the authors' own clarification that inclusion does not claim every entry is a fully autonomous RSI system.

Chinese circle — science communication sets the tone, Zhou Xuanhe is made the star, critique splits two ways. Synced/机器之心 (Sep 14) was the tone-seter: L1–L5 breakdown, all 72 companies placed, quoting DAIR.AI's "a clear panoramic map," ending with "the last AI is not a superintelligence born in a moment, but an ember that learns to add its own fuel, calibrate itself, and remember its own path" — note the star is Xuanhe Zhou; Chinese coverage rarely names the two senior names. Critique ran two routes: technical (Huxiu, Sep 15, most systematic) — the five-level framework is "very effective for communication and building shared language," but "treating it as a scientific measurement framework is where problems begin"; the real criterion should be improvement-operator efficiency ρ(Mₜ₊₁)>ρ(Mₜ), advocating a seven-dimensional continuous space plus freeze-and-swap measurement protocols; and discourse critique (ifanr) — "if the AI circle claims second place in coining and packaging concepts, no one would dare claim first," RSI is "carefully packaged panic business," and "the same people who raise the alarm often feed the beast." Chinese X data accounts (@0xLogicrw) added a good number: of the 491 papers, L1 is 43.8%, L2 31.6%, and L5 only 29 papers (5.9%) — honestly noting L1–L5 is the authors' standard, not an industry-accepted grading.

Six days: zero academic rebuttals, zero conflict-of-interest accusations, zero Alignment Forum threads. The three sharpest sentences came from LinkedIn (Pavlenko: "Without an external anchor of truth, recursive self-improvement degenerates into recursive self-delusion"), Vastkind ("The title frames an ambition. It does not establish that humans have built their last AI."), and the paper itself (§4.3.4: "Full L5 improvement of the software-development improver has not yet been demonstrated").

5. Independent Thinking Points

1. The survey's real product is not the framework but "ledger power." The five-level framework can be argued with (Huxiu argues well), but the 72-company Appendix B table has no competitor — the first mover labels every system and assigns every company an archetype; latecomers either adopt its language or build a taxonomy nobody recognizes. AlphaEvolve tagged B0, SEAL silenced, OpenRSI compressed to one row: the ledger's power lives in its editorial policy, and that policy comes from an author seat with conflicts of interest (Theseus itself in the table, Frontis in the table, ModelBest with a dedicated section). The Chinese AI community's first team to realize: a survey is infrastructure, and infrastructure should be built by your own people. 2. The temperature gap maps two narrative needs. The English circle consumes "China dares to do what we don't" (fear/competition); the Chinese circle consumes "we also have a systematic layout" (catch-up confidence) — neither side discusses the framework's measurement defects. Huxiu's ρ(Mₜ₊₁)>ρ(Mₜ) criterion proposal was the most constructive output in six days, and it appeared on a platform the AI circle dismisses as "content farms." The signal is often not where you subscribe. 3. The undergraduate-first-author + big-name-endorsement structure deserves study as a sample. That Yi Duan (an economics undergrad) could coordinate 33 authors and 491 references presupposes that the "literature survey → classification → drafting" pipeline has been agent-ified enough for an undergraduate to drive — a self-referential piece of evidence for the paper's own thesis (autonomization of improvement execution): the survey itself is a product of L1/L2 capability. In RSI terms: the academic-production improver is already improving, though the paper didn't quite dare say so about itself.

Honest Boundaries

Team analysis is based on public homepages and the paper's self-reported author block; the 16 SJTU authors were not individually verified; Zhoufutu Wen's ByteDance/Humanlaya conflict defers to the paper's self-report — confidence grade C.
The EvoMap/local-toolchain correspondence is a proper-noun-coincidence-level inference (three simultaneous hits), no second source, grade D pending verification — the published version deliberately leaves only the clue without naming names.
Appendix B regional attribution is inferred from outside the paper (which deliberately omits geography); US/China shares are rough estimates; 72 companies / 142 rows are line-by-line counts, off by 2 from the first post's "144 level labels" — possibly a counting difference involving B0 rows.
X engagement numbers are as-of-crawl values; HN data verified via Algolia API; Reddit r/aigossips blocked scraping, existence only confirmed.
ModelBest/Tencent/Humanlaya figures all come from the paper's §5 self-reports (first-party but without third-party benchmarks) — keep the "self-reported" framing when citing.

Falsifiable Predictions

Within 6 months: Appendix B will be turned into a filterable database in the awesome-rsi repo, with the first public "self-reported level vs. ledger level" disputes (the OpenRSI compression, if the Frontis camp cares, will draw a response);
Within 6 months: at least one "paper-single-source" company among EvoMap/MetaCircle/Endless Frontier will surface (product page/funding/paper), validating or falsifying the table's due-diligence depth;
Within 12 months: a competing survey (US or independent) will ship with its own grading framework, contrasted against L1–L5 — at that point "ledger power" upgrades from speculation to factual statement.

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*Verification notes: arXiv 2609.11873 HTML full text (~320k characters) parsed locally via five sub-analyses (§1–3 close read / §4–7 close read / appendix line-by-line / community reaction / team background); author block cross-checked against arXiv PDF and alphaXiv/HF Papers; personal homepages (duanyi516.github.io, xuanhe.gitbook.io, conghui.ai, randomtutu.github.io) are primary grade A; Synced/SCMP/Huxiu/QbitAI/Tsinghua official/Recode China AI are grade B; X/HN/Reddit/LinkedIn/Vastkind/HF are grade C; figures in Baidu Baike and local posts such as "HCI rose from 0.3 to 0.7" do not match the original text — grade D, not adopted. First post: 178634859 (2026-09-14).*