Process Log — Post #2: Human Cognitive and Behavioral Limitations in Scientific Discovery

Discovery Engine — Companion documentation
Version 1 — last updated 2026-06-15

This document records how Post #2 was produced. It continues Discovery Engine's standing practice of making the process layer of human–machine collaboration visible. The practice will become more important — not less — as the work moves from the writing layer to the discovery layer.

Post #2 was a substantially larger production than Post #1. The post went through fifteen substantive iterations, received two rounds of external review from Gemini and ChatGPT, generated six adopted edits from the Round 2 disposition, and during production produced a structural split: the original §5 was extracted as Post #3, Connecting Distant Dots. That structural decision shifted the entire series numbering and required a retroactive edit to the published Post #1.

1. Source Materials

The following materials, all authored or curated by Hiroaki Kitano, were used as primary inputs.

Working drafts of the underlying paper

• Version_A_Nature_Perspective_v2_1.docx — Nature Perspective draft of Can Machines Discover? (source of the structural-misalignment framing and the Part I cognitive-limits foundation)
• Version_B_Nature_Machine_Intelligence_v2_1.docx — Nature Machine Intelligence full-paper draft (source of the Warp Drive for Scientific Discovery architectural concept, deferred to Post #4)
• Version_C_PNAS_NatureReviews_v2_1.docx — PNAS / Nature Reviews AI long-form draft (source of the twin-question opening for Post #1, and the integrated diagnosis–architecture–epistemology framing carried forward)

Foundational publications

• Kitano, H. (2016). Artificial intelligence to win the Nobel Prize and beyond. AI Magazine, 37(1), 39–49. (Source of the Marfan syndrome example in §2.3; of the Warp Drive metaphor; of the AI-as-cognitive-prosthesis framing.)
• Kitano, H. (2021). Nobel Turing Challenge. npj Systems Biology and Applications, 7, 29. (The operational benchmark concept extended throughout the series.)
• Kitano, H. (2007). A robustness-based approach to systems-oriented drug design. Nature Reviews Drug Discovery, 6, 202–210. (Source of the long-tail drugs concept in §3.4.)

Talk and visual materials

• TEDAI 2025 slides — post-talk version [TEDAI2025_Kitano_slides_PostTalk.pdf] (source of the minority report problem framing in §2.6 and the source-diversity argument)
• 再生医療学会2026 slides [再生医療学会2026_without_video.pdf] (additional regenerative-medicine examples)
• 「AIにおける科学革命」 slides (source material for §4 — twilight zone reasoning)
• 「実世界における知能」 slides (source material for the embodied-cognition framing in §2.4)
• 「人工知能がノーベル賞を獲る日」 slides (strategic framing of the diagnosis–architecture–benchmark progression)

Empirical anchors (cited in §3.4 — the long-tail material)

• Stoeger et al. (2018). PLOS Biology — gene-attention bias; 96.8% concentration finding.
• Edwards et al. (2011). Nature — the Harlow-Knapp effect (kinase research concentration).
• Su, A. I., & Hogenesch, J. B. (2007). Genome Biology — power-law distribution of references per gene.
• Qian et al. (2024). PLOS ONE — TREM2 bibliometric history (cloned ~2001, sat in long tail until 2013 GWAS).
• Tanaka, R. (2005). Physical Review Letters — scale-rich metabolic networks (multi-scale long tail).
• Nishi, A. et al. (2017). npj Systems Biology and Applications — maoto and complex-disease drug response.
• Armenia, J. et al. (2018). Nature Genetics — prostate cancer genomics long tail.
• Shen, T. et al. (2015). Genetics Research — rare-disease research enabled by next-generation sequencing.

Cognitive-bias and lock-and-key references (added in v11 for the §2.5 case study)

• Tversky, A., & Kahneman, D. (1974). Science — heuristics and biases.
• Fischer, E. (1894) — original lock-and-key model.
• Koshland, D. E. (1958) — induced fit.
• Boehr, D. D. et al. (2009). Nature Chemical Biology — conformational selection.

Post #1 published versions

• https://thediscoveryengine.ai/can-machines-dis/ (English, published 2026-05-27) — direct upstream of Post #2's opening framing. Established the structural-misalignment vocabulary, the Class I/II/III taxonomy, and the original 5-item roadmap that Post #2's existence forced to be expanded.
• https://thediscoveryengine.ai/can-machines-discover/ (Japanese, published 2026-03-23) — established the Japanese register and lexical rules that Phase D of Post #2 inherits.
• Post #1 Process Log v8 (process_log_post_01.md) — the methodological template for this document.

2. Collaborators

The division of labor matches Post #1's: Claude executes, the human author judges, Gemini and ChatGPT supply external critique against which the editorial position is tested. The novelty in Post #2 is that the same two external reviewers were brought back for a second round at v14 — after the six edits identified in Round 1 had been applied — to test whether the revised manuscript still elicited the same critique vectors. The result of that test is recorded in §4.4 (the Round 2 moderation finding).

3. Iteration Log

The post went through fifteen substantive iterations. Each iteration was driven by a specific human directive, an external review, or a deliberate revision pass, with Claude executing and the human author judging.

v1 — Initial outline + Part I cognitive limitations.
Source: Version A draft + Post #1 published. Built the §1 framing (structural-misalignment continuity from Post #1) and the §2 cognitive limitations skeleton (information horizon, information gap, representation, dimensionality, biases, minority report). Part II initially conceived as a single section.

v2 — Part II social and behavioral limitations elaborated.
Directive: develop the social side at parallel depth to the cognitive side. Added §3 with career incentives, collective evaluation bias, institutional contingency, and a preliminary §3.4 long-tail empirical signature. The two-part structure (Part I cognitive + Part II social) crystallized as the essay's spine.

v3 — Twilight zone reasoning section added (§4).
Directive: name the cognitive territory where both kinds of limitation operate together. Added §4 Twilight zone reasoning — the regime where neither pattern-matching nor mechanism-tracing alone is sufficient. This section also became the pivot to (what was then) §5, the boundary-drawing implication.

v4 — Implication section drafted, structure cleaned.
Drafted §5 Implication as the bridge to (what was then planned as) Post #3 architecture. Structural cleanup throughout. Working title and section hierarchy stabilized.

v5 — Lexical pass: register and consistency.
Pass over the entire draft to apply the structural misalignment vocabulary consistently (replacing residual structural blindness from earlier drafts; the retermination decision had been made at the close of Post #1 production but had not been fully propagated). Adjusted register to the Ver1.5 baseline established in Post #1: plainer vocabulary, classical sentence structure, fewer meta-prose interruptions ("what this means is...", "to put it differently..."), italicized technical terms on first occurrence.

v6 — TREM2 and gene-attention examples expanded.
Directive: §3.4 needs concrete empirical anchors, not only statistics. Added the TREM2 / Qian 2024 bibliometric finding (cloned ~2001, sat in the long tail until 2013 GWAS), reinforced the Stoeger 2018 96.8% finding already in Post #1, and added the gene-attention literature (Edwards 2011, Su & Hogenesch 2007). At this stage §3.4 was named empirical signature but contained only gene-attention examples.

v7 — Embedded references converted to inline citations + reference list.
Production hygiene. All cited works listed in proper APA-with-DOI format. ~25 references at this stage.

v8 — Event horizon terminology introduced (§2.1).
Directive: name the threshold where the volume of information generated exceeds the reading capacity of any one mind. Initially used event horizon (the cosmology analogy). This term would later be reterminated in v12 — see below.

v9 — Submission for Round 1 external review.
v9 was the draft submitted to Gemini and ChatGPT for their first round of structured critique. ~5,800 words. See §4.1 and §4.2 below for the substance of those reviews.

v10 — Round 1 disposition: integrate the easy wins.
The human author and Claude went through the two reviews. Round 1 produced a disposition of which recommendations to adopt as-is, which to adapt, and which to reject (with reasons). Six edits were planned. v10 incorporated the lower-friction half: small wording fixes, citation completions, the §1 micro-rephrasing of "how good, really, is human-led discovery?", the §2.6 source-diversity-not-count rephrasing that made the discriminator explicit.

v11 — Round 1 Edit 1 applied: lock-and-key case study added to §2.5.
Adopted from Round 1 ChatGPT critique: §2.5 (Cognitive biases in scientific reasoning) was asserting a claim about scientific cognition without grounding it in a worked example. Option 6 (assertion → demonstration) was chosen: added the lock-and-key model as the case study, with Fischer 1894 → Koshland 1958 (induced fit) → Boehr 2009 (conformational selection) as the progression that demonstrates how a single cognitive model (key fits lock) was extended and complicated by successive generations of researchers. The Tversky-Kahneman 1974 Science paper added as the meta-frame.

v12 — Event horizon → information horizon retermination (§2.1). §5-as-Post-#3 split decided.
Two structural decisions in this iteration.

First, the human author judged that the event horizon metaphor was technically apt but overdetermined: in cosmology it carries an irreversibility connotation (no information returns from beyond it) that overshoots the claim here (no individual mind can read past it; the literature itself is not lost). Reterminated to information horizon. The §2.1 paragraph was restructured to define the new term explicitly via analogy with physics event horizon, then to map the physical-to-informational threshold. Verified that Post #1's published body did not use event horizon (only the drafting notes did), so no retroactive edit to Post #1 was required for vocabulary continuity.

Second, with Round 1 Edit 1 applied (the §2.5 case study) and Round 1 Edit 2 (the AI inheritance counter-argument) projected at full intended size, the manuscript had grown to ~6,316 words. §5 had always been a different kind of section from §1–§4 (boundary-drawing vs. diagnosis). The human author elected to split rather than compress: §5 was extracted as an independent essay, becoming Post #3 Connecting Distant Dots. This shifted the series structure from five-posts-after-Post-#1 to seven-posts-after-Post-#1, and forced the retroactive edit to the published Post #1 roadmap (applied 2026-06-15; see Post #1 Lab Notebook §10 Amendment 1).

v13 — §3.4 long-tail expansion: multi-scale biological examples.
Directive: §3.4 (empirical signature — the two long tails) needed examples beyond gene-attention. Added a four-example sequence demonstrating that the long tail recurs across biological scales: metabolic networks (Tanaka 2005), Kampo medicine and complex-disease drug response (Nishi 2017; Kitano 2007 on long-tail drugs), prostate cancer genomics (Armenia 2018), and rare-disease research enabled by next-generation sequencing (Shen 2015). The four examples cross scales (network → pharmacology → genomics → rare disease) and together demonstrate that long-tail underweighting is not specific to gene attention but is a recurrent feature of how human research effort distributes across biological reality. All four citations verified via web search (DOIs, journal volumes, page numbers).

v14 — Submission for Round 2 external review.
v14 was the post-Edit-1, post-retermination, post-split, post-empirical-expansion draft submitted to Gemini and ChatGPT for a second round. The result is recorded in §4.4 — the Round 2 moderation finding.

v15 — Round 2 disposition Edit 6: §2.3 Marfan compression + structural completion.
Round 2 disposition Edit 6: §2.3 (the representation problem) was assertion-heavy and the Marfan syndrome example, while well-chosen, was given more prominence than its load-bearing role warranted. Option 1 was adopted: Marfan was demoted from independent sentence to parenthetical clause; the drug-development consequence was absorbed inline; the meta-comment was removed; the structural punchline was preserved intact. §2.3 fourth paragraph went from ~140 to ~80 words. With this edit, all six Round 2 edits were complete across Post #2 v15 + Post #3 v2.

v15.1 — Fig 2.4 retired (post-Phase E editorial decision).
The Phase E figure package contained four figures (2.1 SBGN information gap, 2.2 non-linear object, 2.3 reality vs cognition, 2.4 minority report). During publication-ready package preparation, the human author decided Figure 2.4 would not be used. The §2.6 text retains the minority report problem concept; no figure accompanies it. Rationale: §2.6's 99% / 1% rhetorical contrast already visualizes the asymmetry in the reader's head; the figure would have visualized counts, but §2.6's argument is about source independence, not count, so the visualization would have reinforced the wrong axis; Figures 2.1–2.3 each show structural relationships, and removing the lone quantitative-distribution figure tightened the visual register across the remaining three.

v15.2 — Long-tail figure scope change: Post #3 deliverable → Post #2 §3.4 (in-publication editorial decision).
At v15.1 the long-tail figure visualizing the gene-attention distribution was flagged as a Post #3 forward-looking deliverable. During Ghost CMS preparation for Post #2 publication, the human author elected to insert this long-tail figure into Post #2 §3.4 directly, before publication, rather than hold it for Post #3. The figure was produced as fig3_1_long_tail.jpg (96.8% of publications on top 10 of 100 random human genes; © 2025 Hiroaki Kitano) and embedded just before the Harlow-Knapp paragraph. The figure earns its place: §3.4 establishes "the two long tails" as the empirical signature of structural misalignment, and the figure shows the first long tail in its starkest form. Without it, the reader has to take the 96.8% figure on the page's word; with it, the magnitude is visible and the diagnostic argument lands harder. (Caption number was corrected from "Figure 3-2" to "Figure 3-1" in a post-publication in-Ghost edit; figure numbering uses chapter-dash-sequence in honor of the figure's origin as a Post #3 artifact and is preserved as a record of production history rather than retrofitted to Post #2's chapter-dot-sequence convention.)

4. AI Inputs and External Reviews

Post #2 received two full rounds of external review from Gemini and ChatGPT. Round 1 critiqued v9; Round 2 critiqued v14. The substance of each review is recorded below.

§4.1 Gemini — Round 1 review (v9 → v10)

Gemini's Round 1 review focused on three areas: clarity of structure, depth of empirical anchoring, and accessibility for non-specialist readers. Adoption summary:

• Add explicit signposting between Part I and Part II. Rejected. The Ver1.5 register prefers structural signaling through section numbering and titles rather than meta-prose transitions. The Part I / Part II naming itself does this work.
• Strengthen the §1 framing of why these limits matter "now." Adopted in modified form. The opening paragraph of §1 was tightened to make the now claim explicit: structural misalignment is the result of how knowledge production has changed in volume, not a perennial complaint about human limits.
• Compress §4 (twilight zone reasoning) to about half its current length. Rejected. §4 carries the synthesis that connects Part I to Part II; compressing it would dissolve the bridge.
• Add a glossary or vocabulary box for non-specialist readers. Rejected. The essay defines its terms inline; a separate glossary would suggest the essay was harder to read than it actually is and create a maintenance burden across language versions.

§4.2 ChatGPT — Round 1 review (v9 → v10)

ChatGPT's Round 1 review was more strategically aggressive, identifying the post as belonging to the diagnosis-essay genre and pushing for sharper rhetorical commitments. Major recommendations and adoption:

• §2.5 (cognitive biases) currently asserts rather than demonstrates; add a worked case study showing how a cognitive model develops and is revised across generations. Adopted (Edit 1, applied in v11). Added the lock-and-key case study: Fischer 1894 → Koshland 1958 → Boehr 2009; Tversky-Kahneman 1974 as meta-frame.
• The AI inheritance counter-argument (mentioned briefly in §5) is the most important objection and deserves its own section, not a single paragraph. Adopted (Edit 2, eventually became Post #3 §5). This recommendation became the seed of the §5-extraction decision in v12: rather than expand §5 within Post #2, the entire §5 was extracted as Post #3, and Edit 2 became Post #3 §5 — a full three-part rebuttal (the error-cancellation regime, the law of large numbers, biases-as-reason-not-objection).
• The opening twin questions ("how good, really, is human-led discovery?") need slightly more rhetorical authority — currently land as throwaway. Adopted in v10. Reworded to lead with structural framing before the quieter question.
• Cut the Marfan example as too biomedical-specific. Rejected at v10; partially reconsidered at v15. v10 retained Marfan in full; v15 compressed Marfan to a parenthetical (Edit 6 of Round 2 — see §4.3 below).
• §3.4 (empirical signature) needs more examples than gene attention. Adopted (in v13). Added Tanaka, Nishi, Armenia, Shen — four examples spanning network, pharmacology, genomics, rare disease.

§4.3 Round 2 disposition (v11–v15)

After v11–v14 applied the bulk of Round 1, the manuscript was submitted to Gemini and ChatGPT for Round 2. The Round 2 reviews verified that the manuscript still elicited critique vectors aligned with each reviewer's known tradition, but at substantially calibrated intensity (see §4.4). Six adopted edits, with their final placement:

Each edit's drafting notes record the option-space considered and the reasoning for the chosen option. The full Round 2 disposition document is preserved as an archive artifact (post2_v10_round2_disposition.md).

§4.4 Meta-observation: three-traditions thesis, and the Round 2 moderation finding

The three-traditions thesis established in Post #1 §4.4 (Claude → Op-Ed / Nature Perspective; Gemini → web-optimized accessibility; ChatGPT → visionary-manifesto) carried directly into Post #2. Both Round 1 reviews exhibited the predicted gravitational pulls of their respective traditions: Gemini pushed for signposting, TL;DR-equivalent, and glossary; ChatGPT pushed for sharper rhetorical commitments, independent treatment of the highest-stakes counter-argument, and compression of biomedical detour material.

The Round 2 reviews — submitted on a manuscript that had already been disposed-of through Round 1 — revealed something new. The same gravitational pulls were still detectable, but with materially decreased amplitude. Specifically:

• Gemini's Round 2 critique made no recommendation analogous to "add TL;DR / glossary / explicit signposting". The Round 2 Gemini focused on the depth of the empirical anchoring in §3.4 — a structural / argumentative critique (which became Edit 3), not a register critique. The accessibility-pull characteristic of its tradition was present but quiet.
• ChatGPT's Round 2 critique no longer pushed for compressed visionary register. Instead it critiqued the specificity of arguments in three places (Marfan as too detour-like, isomorphism as too mathematically committed, Darwin / Yamanaka / Charpentier-Doudna as not-identical-cases-presented-as-identical). All three Round 2 critiques were edits within the essay's existing register — not pulls away from it.

The moderation suggests that when the work being reviewed (a) has a clearly committed register, (b) has been internally consistent on that register across many iterations, and (c) is well-executed on its own terms, external AI reviewers calibrate to the work rather than pull toward their native tradition. The pull does not vanish; its amplitude decreases. This is a fourth confirmation extension to the three-traditions thesis (which had been confirmed cross-modally and cross-linguistically in Post #1; see Post #1 Process Log §4.4 postscripts). The new finding is that the pulls are attenuated, not eliminated, by well-formed work.

Operational consequence for subsequent posts: at Round 1, expect the full gravitational pull of each reviewer's tradition. At later rounds, if Round 1 has been well-disposed, expect a more calibrated review focused on what the work itself needs. This argues against treating later rounds as "just more of the same" — they may carry signal that early rounds do not, because the reviewer is no longer arguing for its tradition but is now operating within the work's chosen tradition.

The finding will be tested again in Post #3, Post #4, and beyond. If it survives, it becomes a stable extension of the three-traditions thesis: AI reviewers' literary gravity attenuates against well-formed work at successive review rounds.

5. Editorial Decisions (Human Author)

The following decisions were made by the human author and are not negotiable by the AI collaborators:

• Title: Human Cognitive and Behavioral Limitations in Scientific Discovery — direct, descriptive, not metaphor-clad. The Post #1 register-policy (avoid clever titles) carries forward.
• Subtitle: The second essay in the Discovery Engine series. — minimal positioning. The essay does its own framing internally.
• Two-part structure (cognitive + social/behavioral) is the spine. Not negotiable across reviews. The two halves are parallel limitations of the human discovery system; they require parallel treatment, not a unified ordering by topic.
• The §5 split (Post #2 §5 → Post #3 Connecting Distant Dots) is structural, not editorial. Compressing §5 to fit Post #2 was rejected. The boundary-drawing material is a different kind of argument from the diagnosis material; it needs its own essay. This decision restructured the post-Post-#1 series from a 5-post sequence into a 7-post sequence.
• Information horizon replaces event horizon. v12 retermination. The cosmology metaphor carried irreversibility freight that overshot the claim. Information horizon preserves the physics analogy without the irreversibility.
• Marfan example retained, then compressed, never removed. ChatGPT proposed removal in Round 1; rejected. ChatGPT's Round 2 critique suggested it had too much prominence; accepted, but as compression, not removal. The clinical specificity of "coarse category → real clinical consequence" earns its place; the parenthetical placement honors its load-bearing-but-not-leading role.
• Figure 2.4 retired from publication. §2.6 retains the minority report problem in text; no figure accompanies it. The §2.6 argument is about source independence, not count distribution; the histogram would have visualized the wrong axis.
• The long-tail figure (Fig 3-1) inserted into Post #2 §3.4 at publication time. Originally a Post #3 deliverable; the figure earned its place in Post #2's diagnostic argument. The chapter-dash numbering (3-1) is preserved rather than retrofitted to Post #2's chapter-dot convention — production history is honestly recorded, not back-corrected.
• Connecting Distant Dots is a proper concept name (Editorial Decision E, applied during the Post #1 roadmap retroactive edit). Like Discovery Engine, Warp Drive, Nobel Turing Challenge, and Can Machines Discover?, it is kept untranslated in Japanese — category names translate, proper concept names do not. The Japanese roadmap reads: Connecting Distant Dots —— 遠く離れた分野や知識を繋げて発見に至る経路。
• Roadmap registers in path-finding, not boundary-drawing. The Post #1 roadmap retroactive edit, applied 2026-06-15, describes Post #3 as paths that connect distant observations, concepts, and fields to generate new discoveries — generative, forward-leaning. An earlier proposal had used what a differently built system could in principle reach, and what it could not — boundary-drawing register. The author replaced the boundary register with the path register at the moment of in-Ghost application. The essay's internal arguments still use boundary structure; the external roadmap describes the essay's operation, not its deliverable.
• Japanese roadmap item #1: 診断 (diagnosis) → 限界論. A bonus edit made at the same moment as the Post #1 roadmap expansion. The original metaphorical translation (診断) was replaced with the philosophical-direct rendering (限界論) that more honestly describes Post #2. The English version retains The limits (which already carries the direct sense in English). This is another instance of the JA → EN asymmetry pattern (see §6 below).

6. What Did Not Change

Recording what was not altered, despite two rounds of review and fifteen iterations, is as important as recording what was.

• The central thesis: Structural misalignment is not the failure of any individual scientist but a misalignment in how human discovery is organized as a whole. Present from v1, unchanged through v15.
• The two-part structure (Part I cognitive + Part II social/behavioral). Stable from v2.
• The closing claim of §5 (Implication) — that the diagnosis is necessary but not yet sufficient, and the next essay (Post #3) addresses which parts of the diagnosis a differently-built system can actually relieve. Stable from v4; the closing claim moved with the §5 extraction.
• The information horizon → information gap → representation problem → dimensionality limit → biases → minority report sequence in Part I. Six subsections, six distinct cognitive limits, ordered from outer (literature volume) to inner (individual reasoning).
• The TREM2 example, defended against ChatGPT's Round 1 recommendation to cut. As in Post #1, it provides the empirical anchor for the otherwise abstract long-tail argument.
• The AI co-authorship disclosure as a constitutive feature of the essay, not a disclaimer. Same model as Post #1.

Vocabulary Inventory (added v15)

Post #1 established the working lexicon of Discovery Engine (see Post #1 Process Log §6). Post #2 extends that inventory with the following terms — all first introduced in Post #2 and now established for the series:

Two further terms are established in Post #3 v2 (forthcoming): mechanistic distance and analogical distance as an asymmetric pair, and isomorphism used as a technical descriptor with Round 2 Edit 5's soft-land framing on first occurrence.

These terms are now the shared lexicon between the human author and the AI collaborators for this project. Their consistent use across posts is itself part of how Discovery Engine accumulates its identity.

Japanese Lexical Rules (added v15)

The Post #1 Japanese Lexical Rules (Post #1 Process Log §6) established the baseline. Post #2 adds the following bindings:

Cross-Linguistic Asymmetry (extended in v15)

The cross-linguistic asymmetry observation established in Post #1 (Process Log §6) carries directly into Post #2: the Japanese and English versions are parallel realizations of the same project, not translation pairs. Post #2 contributes a second concrete instance of the JA → EN back-propagation pattern.

In Phase D drafting of Post #2 §1, the English phrase "there is now more to know than any mind or institution can keep up with" was first rendered as "今日、知るべきことは、どんな個人の知性も、どんな制度も追いつけないほど膨大になっている" (regulative/subjective: what should be known). The human author revised it to "今日、生み出される知識の量は、どんな個人の知性も、どんな制度も追いつけないほど膨大になっている" (descriptive/objective: the volume of knowledge being produced).

The revision aligned the Japanese with the information horizon framing that follows in §2.1 — both are about the volume of knowledge production, not about epistemic obligation. The English version retains a slight residual ambiguity ("more to know") that the Japanese revision resolved. This is held as a candidate for a future back-propagation pass to the English original.

The pattern is now stable across two confirmations:

• Post #1 構造的盲目 → 構造的ミスアラインメント (JA review forced an EN retermination of structural blindness).
• Post #2 §1 生み出される知識の量 (JA-side rendering refines a residual ambiguity in the English).

A third instance accompanies this Process Log: the Post #1 roadmap retroactive edit included a JA-only rendering improvement (診断 → 限界論) without an EN counterpart, because the English The limits already does the philosophical-direct work that the Japanese needed retranslation to do.

The asymmetry is not a deficiency in either direction. It is a feature of two intellectual ecologies running in parallel, each with affordances the other lacks. Subsequent posts should be composed with this asymmetry in mind, not against it.

7. Visual Assets — Placement Plan

For site implementation (Discovery Engine site, Note), the following figures are embedded in Post #2.

Visual identity inherits from Post #1: Class I blue #5b9fdb, Class III gold #d4a35c, soft blue-grey #8fa8c8, soft slate #a0adc8, dark navy gradient #0a1028 → #0e1632, Georgia / serif body, ViewBox 1200×640. Post #2 adds SBGN Process Description Level 1 strict rendering (Le Novère et al. 2009) for Fig 2.1: macromolecule = rounded rectangle, complex = cut-corner rectangle, compartment = thick boundary, process = small square, consumption arc = line, production arc = arrow, stimulation arc = open arrowhead, unit of information = rectangle on EPN boundary, clone marker = dark band. Color does not carry meaning, only form does — Far1, Cdc24, and Msn5 all use the same Class I blue, with micro-tone differentiation only.

Visual identity is language-unified, as established in Post #1 v13. The English figures are used in both English and Japanese publication venues.

A Figure 2.4 (the minority report problem, histogram) was produced during Phase E but retired during publication preparation (v15.1). It is preserved in the Phase E archive as a record of what was produced but is excluded from the publishable package. See §5 above for the rationale.

8. Open Questions Carried Forward

Items raised during Post #2 production but reserved for later posts:

• The Warp Drive architecture. Named in Post #1, restructured into Post #4 by the §5 → Post #3 extraction. The architectural details — what a closed-loop autonomous discovery system actually consists of — are reserved for Post #4. Post #2 §5 (Implication) and Post #3 §6 jointly set up the architectural question without answering it.
• Class II / III machine discovery. Post #2 diagnoses why human science under-explores Class I. Post #3 develops the boundary of what differently-built systems could in principle reach. Whether autonomous systems will produce Class II frameworks (Cooper-pair-equivalent discoveries) or Class III shifts (gauge-theory-equivalent reframings) is reserved for Posts #4 (capability claims) and #5 (benchmark, evaluation).
• The English-version back-propagation candidate for Post #2 §1 (生み出される知識の量 → revise the English from "more to know" to "the volume of knowledge being produced"). Held as a candidate; tracked.
• Phase D completion for Post #2. Title and §1 are complete. §2.1 through §5 in Japanese are pending.
• Post #3 publication package. Post #3 v2 is text-final. With the long-tail figure now in Post #2, the Post #3 figure plan is reopened — whether Post #3 will have its own figures, and which sections they would anchor, is to be decided in Phase E of Post #3.
• Third-round external review (optional). If a third round of external review is undertaken on any post, the human author's preference is a human domain expert rather than another LLM round — informed by the Round 2 moderation finding (§4.4), which suggests diminishing returns for repeated LLM review of well-formed manuscripts.

9. Availability of Full Records

This Process Log is itself a co-authored document. Claude drafted the structure based on the actual sequence of revisions, the verbatim AI inputs and reviews, and the human author's directives. The human author reviewed, corrected, and approved before publication. The Round 2 moderation finding in §4.4 — that AI reviewers' literary gravity attenuates against well-formed work at successive review rounds — was articulated by Claude during Round 2 disposition analysis, accepted by the human author, and is presented here as a finding of this collaboration to be tested in Posts #3, #4, and beyond. If it survives those tests, it joins the three-traditions thesis itself (Post #1 §4.4) as a stable empirical generalization about how AI reviewers calibrate to manuscripts that have committed to a register and executed it well.