paper

name	paper
description	Load the unified SCIPAPER_STANDARD writing guidance, formula derivation conventions, scientific-integrity rules, canonical L0 examples, and field-specific reference anchors for ApJ/MNRAS/PRD-level papers. Use when writing or reviewing scientific content.
disable-model-invocation	false

Normative authority: docs/SCIPAPER_STANDARD.md. This skill supplies concrete scientific-writing guidance and canonical L0 examples. It does not define an independent paper verdict. If this file, a style profile, or a workflow conflicts with the unified standard, the unified standard wins. Project-specific anchors remain marked [WGL].

Paper Writing Standards / 论文写作标准

Target: Top-tier astrophysics journals (ApJ, MNRAS, A&A level).

General Principles

• Accuracy over elegance. Never sacrifice precision for readability. Every claim must be verifiable.
• Quantitative over qualitative. Replace vague descriptions with numbers. Not "significantly improved" but "AUC increased from 0.834 to 0.927 (+11.1%)".
• Reproducibility. The Methods section must contain enough detail for an independent researcher to reproduce all results. All hyperparameters, data splits, and evaluation protocols must be specified.

Structure and Narrative

A motivation → method → validation arc is a useful default for many empirical papers, not a universal document template. Theory papers, methods papers, data releases, and multi-contribution papers may require a different shape. In every case, make the contribution graph explicit: explain how the claims relate, place definitions before use, and ensure that each conclusion is supported by the presented argument or evidence.

When the three-part arc fits:

1. Motivation: identify the scientific problem, prior limitations, and the specific gap addressed.
2. Method: present physical motivation, mathematical formulation, and implementation in dependency order.
3. Validation: state what the results establish, compare like with like, and delimit the evidence and limitations.

Structural Updates · Forward Narrative / 结构式更新 · 正向叙述

每一次写作、修订、纠错都必须把文章重写到"当前真值的最终态"——禁止把"旧态 → 新态"的迁移痕迹留在正文里。 本节是 paper-review §2.O ("update-not-accumulate") 的写作端对偶： 审查端默认 删除 过程残影；写作端默认 一开始就不写进去。

正向叙述（forward narrative）—— 唯一合法形式

正文只描述当前科学声明的最终态。读者应看到：

• ✅ "We use method B; the result is X."
• ✅ "Equation (3) gives Y, which we then evaluate at $z = 0.5$."
• ✅ "We adopt $H_0 = 67.4,\mathrm{km,s^{-1},Mpc^{-1}}$."

读者绝对不该看到（无论初稿还是修订稿）：

• ❌ "我们一开始尝试了 A，发现 A 不对，所以改用 B"
• ❌ "We did A, found it gave incorrect results, and therefore switched to B"
• ❌ "Method A would have suggested X, but in fact ..."（除非这是本文有意做的 baseline 对照，见下"唯一例外"）
• ❌ "An earlier version of Eq. (5) had a factor-of-2 error; the corrected form is ..."
• ❌ "Note: the previous draft used $H_0 = 70$; the present analysis uses $H_0 = 67.4$."
• ❌ "After several iterations we converged on the following architecture."

Patch（禁）vs Structural（要）—— 修订时的硬区分

发现错误 / 公式不对 / 数字漂移 / reviewer 指出问题时：

• ❌ Patch 式修改：在错误旁注释"原方法 A 错在 …，因此现采用 B"；保留旧公式 + 加 footnote 解释"现已修正"；保留旧数字 + 加括号给"修正后值"；段尾追加"this supersedes our earlier claim that ..."。
• ✅ Structural 式修改：删掉 A 的所有痕迹，从段首重写到段尾，让正文呈现为"从一开始就一直是 B"。读者无法从论文本身推断出 A 曾经存在过。

Patch（禁）	Structural（要）
"We initially used $H_0 = 70$; later we adopted $H_0 = 67.4$."	"We adopt $H_0 = 67.4$."
"An earlier version of Eq. (5) had a factor-of-2 error; the corrected form is ..."	只保留 Eq. (5) 的正确形式；下游推导全部 propagate 重写。不提"earlier version"。
"Method $M_1$ failed due to overfitting; we therefore use $M_2$."	"We use $M_2$."
"After several iterations we converged on the following architecture."	只描述最终 architecture。
"上一版图 3 的曲线已更新；新的拟合参数为 ..."	只展示新图 3 + 新参数。

唯一例外 —— 真正的 baseline 对照

仅当全部三条满足时，可以在正文中保留"方法对照"：

1. 被对照的方法是 领域内已发表的外部 baseline / prior published method——不是作者自己的早期迭代；
2. 本文对该对照做了正式 head-to-head 实验并给出数值；
3. 呈现方式是 "contrast with baseline X (Smith+2020) on protocol P → numerical comparison"，不是 "we initially tried X" 这种第一人称自传式。

判定时若任一条不满足 → 默认按 "Patch（禁）" 处理，重写到只剩当前方法。

写作时强制自检（每段 / 每次修订执行）

1. 这段是不是只描述了当前最终态？（如果包含过去态，删掉过去态。）
2. 一个从未参与本研究的读者，看了这段会觉得有别的方案 / 旧版本存在过吗？如果会 → 重写。
3. 修订时新增的内容是"重写过的最终态"还是"在旧态上贴的补丁"？是补丁 → 撕掉补丁、重写整段，不要"补丁 + 解释为什么打补丁"。
4. 是否出现 initially / originally / previously / at first / earlier / now / currently / corrected / revised / updated / supersedes 等过程时间词指向本文自身研究进程？任一命中 → 重写。（指外部科学时间维度的不算，例如 "recent supernova observations" / "previously published catalogs"。）

与 "Formula Derivation Standards" 中"射箭画靶"的关系

下节的"No shooting arrow then drawing target / 禁止射箭画靶"是本规则在公式推导场景的窄特例（"我想得到 X 结果，所以改了推导步骤"）。本节是更广的写作准则——覆盖正文叙述、方法描述、结果呈现、讨论、结论：整篇论文的任一段落都适用。

改写不堆叠 / Condense, Do Not Accumulate

规范条文：docs/SCIPAPER_STANDARD.md §5.3。用户规则 2026-07-16： "改写、删减、精简，而不是堆叠！"

• 每次修改的默认方向是更短。优先级：删除 > 原位精简 > 等长改写 > 增长。
• 增长只有两种合法理由：用户要求的新内容，或来源可验证的科学必需 （缺失的假设/定义/单位/caveat 属 integrity 缺陷）。
• 典型违规是解释性补丁：对被标记的句子追加从句、句子或脚注去"解释"， 而不是重写句子本身。上节 forward narrative 禁止堆叠状态；本节禁止 堆叠字数。
• 每处修改报告字数差；靠加字消除 detector 信号是缺陷，不是修复。
• 机械执行（标准 §5.3 v3.3）：改写候选经 rewrite_reward.py --original 硬门（超长即 -inf）；编辑循环收尾经 length_gate.py delta 门 （无理由净增长 = exit 1，循环不得收尾）。增长的唯一合法路径是 --allow/--allow-growth 记录的作者批准理由。

Formula Derivation Standards / 公式推导规范

• Multi-line derivations: Use align/gather environments for complete mathematical derivations, not single-line equations. Show the logical chain: a = b (1), then a = c (2), therefore b = c (3).
• Definition completeness: Every variable, compound term, or logical construct appearing in a formula MUST be either (a) previously defined in the text, or (b) defined/derived immediately near the formula. Never introduce undefined symbols.
• No inline formulas for complex expressions: Any formula longer than ~30 characters must be a displayed equation, not inline text. Short expressions (e.g., $\kappa \ll 1$) can remain inline.
• Logical flow over format: Don't force a rigid template. Derivations should flow naturally — define when needed, derive when needed, summarize at the end. The priority is that reasoning is clear and logically connected.
• No "shooting arrow then drawing target" / 禁止射箭画靶: Never write "we wanted X result so we changed to Y approach" or reference historical/deprecated formulas. Present: method → result → conclusion. Do not discuss the iterative path that led to the current approach. （公式推导场景的窄特例；广义写作准则见上节 "Structural Updates · Forward Narrative"。）
• No outdated content: Only present current formulas and label definitions. Do not reference deprecated versions in the paper body — at most a brief footnote if essential for context.
• Summary block: After a derivation chain, include a brief summary: "We therefore obtain [final formula], where [key quantities] are [definitions]. This shows [physical conclusion]."

Physics Descriptions [WGL]

• Describe weak lensing physics using standard notation (Bartelmann & Schneider 2001 conventions).
• Clearly distinguish between: convergence (kappa), shear (gamma), reduced shear (g), aperture mass (M_ap), and S/N maps.
• When describing the detection pipeline, maintain the distinction between signal (E-mode) and noise (B-mode).
• All filter functions must be written with explicit mathematical definitions, not just names.

Method Descriptions

• For each ML model, specify: architecture, input representation, loss function, training procedure, and evaluation metric.
• For ensembles, explain the aggregation strategy and why it is appropriate given any class imbalance.
• Clearly state any transfer learning protocol: what was pretrained, on what data, and how fine-tuning differs.
• For grouped/leave-one-out CV: explain why grouping is necessary and how group IDs prevent leakage.

Results Presentation

• All performance metrics must include: (a) the metric name and definition, (b) the evaluation protocol, (c) uncertainty estimates where possible.
• Tables should be self-contained — a reader should understand the table without reading the text.
• Figures should have: descriptive captions, labeled axes with units, legends, and consistent color schemes.
• When comparing methods, use the same evaluation protocol for all. Never compare training metrics of one model to validation metrics of another.

Discussion and Limitations

• Honestly discuss limitations. Acknowledge sample-size and selection-effect limits.
• Distinguish between: limitations of the method vs. limitations of the data.
• For detection-boundary analyses: discuss where sim-to-real transfer breaks down and why.
• Avoid overclaiming. Detection frameworks are not definitive physical measurement tools.

Anti-AI-isms / 去 AI 表达规范

LLM 生成的学术写作有一组明显的 tell；本节保留既有 L0 词汇与标点 目标，同时把结构、信息分布和 learned field-similarity 信号纳入统一反馈协议。 style-profile/<field>/style_dossier.md、lexicon 和 baseline 是可更新的经验 证据，不是独立政策，也不能把论文判为 AI 或非 AI。后续动作由 docs/SCIPAPER_STANDARD.md 的 consequence class、measurement state、ranking 和 disposition 规则决定。

根本层（fundamental）—— 结构性 AI 味，关键词 lint 抓不到。 上面的 Tier A/B 是词汇层（lexical），必要但不充分：一篇文章可以 0 关键词命中、 甚至逐段读着都像人，却仍通篇 AI 味。真正的 tell 活在结构里，分两个尺度：

1. 信息分布层（token / 句长）：过度均匀的信息密度、句长同质、重复的 signposting 和缺少局部节奏变化。由 distribution 与 UID axes 度量。
2. 句式与文档形状层：句子或段落的构造被重复模板化。需要重点检查：
   • 报数式枚举：rests on five elements. First, ... Fifth, ... / there are three reasons。
   • 先设数目 → 列举 → 收尾：inherits three obligations. [A][B][C]. These three requirements ...。
   • 排比 / 首语重复：≥3 句同一开头或同一模态（must ... must ... must）。
   • 对称收尾：A is one limit of it, and B another。
   • 段落或章节同形：多个段落重复相同的主题句、展开和收束几何。

这些模式不是单次出现即错误。它们在适用 baseline 下构成测量证据；阈值、样本量、 置信度和效应量属于 EVALUATION.md 或 profile calibration，不写死在规范里。

这一层由 de-AI 子系统统一度量（docs/DEAI_SUBSYSTEM.md）：

python tools/ai_ism_lint.py <file> --field <field> \
  --structure --distribution --document-structure --oracle --voice \
  --format json --output <scratch>/writing-feedback.json

• Tier A、em-dash 和超过每节每词 cap 的 Tier B 是 l0_target。
• 句式模板、burstiness、UID、document shape 与 learned field-similarity 是 advisory；必须保留 measured / degraded / unmeasured / not_applicable 区别，不能把缺失测量当作零命中。
• 命中的段可用 /sci-paper:de-ai（Pass 3）从 claim graph 重建，而不是做 同义词替换。任何候选先通过 scientific-fidelity eligibility，再比较风格证据。
• 强 advisory 必须行动或显式 disposition；普通 advisory 可以保留并报告。

因此，Tier A / em-dash 清零与 Tier B cap 是 L0 地板；结构和信息分布信号用于 排序后续动作，不构成必须全部归零的通用 prose gate。

最强 L0 标点目标：em-dash (— / \textemdash / ---)

• 正文目标为 0。插入语改用逗号、括号、分号或独立句；范围使用 --。
• 该规则是项目锁定的 L0 policy。当前 corpus 频率及比较值只在 profile 和 EVALUATION.md 中维护，避免把会漂移的测量写进规范。

Tier A — L0 target

正文命中必须重写。canonical set：

类别	词
动词类	delve / delves / delving / delved, leverages / leveraging / leveraged, pave / paves / paving, shed / sheds / shedding（含 "shed light on"）, showcase / showcases / showcasing, utilizing / utilizes, underscore / underscores / underscored / underscoring
形容词/副词	seamless / seamlessly, holistic / holistically, comprehensively, crucially, pivotal
名词类	tapestry, testament, realm / realms
段首套话	Recent advances in..., Despite significant progress..., With the advent of..., In recent years,..., It is worth noting, 段首 Crucially,, Importantly,, Notably,, Interestingly,

2026-07-16 扩充（underscore*, pivotal, tapestry, testament, realm*） 采自 academic-humanizer 词表（MIT，见文末 Provenance），并经两域 curated corpus 复核为 0 出现后才入 Tier A；landscape 虽在其词表中，但它是本领域 正当术语（detection landscape 等，corpus 高频），不入表。

替换原则：使用直接、具体、可核验的动词或范围，不做机械同义词交换。例如 leverages X to Y → uses X to Y，pave the way for → enable， comprehensively → 明确列出覆盖范围。

Tier B — per-section/per-word cap

Tier B 可以使用，但同一个 Tier B 词在同一 section 最多出现 1 次。第 2 次及 以后是 l0_target；cap 内的出现不是 finding。当前词表由 linter 与 profile 共同维护，常见项包括 Furthermore, Moreover, Additionally, robust/robustly, comprehensive, utilize/utilized, leverage, Importantly, Interestingly, Notably, intricate, foster/fosters/fostering/fostered（后两组 2026-07-16 加入；curated corpus 各有 1 次出现，非零故不入 Tier A）。经验频率只从当前 profile 读取，不在 本文件复制。优先用直接陈述或可验证数字，但不要为了避词而损害准确性。

模糊量化 / 修饰词

• a wide range of, a variety of, a number of, several, numerous, many：有可核验数量时写数量；没有时检查该模糊程度是否科学必要。
• cutting-edge, state-of-the-art, novel, powerful：需要明确比较对象和证据， 否则删除。它们是 claim-quality advisories，不因单词本身自动成为 L0 target。

自指与套话

• This paper presents... / In this work, we... 类 boilerplate 每段最多 1 处。
• 删除：In summary,, To summarize,, In conclusion,（除 conclusion 节外）。

LLM 高频动词替换（dossier 未实证但语法层面是 tell）

• facilitate → enable
• In order to → To
• aim to → we [verb]（直接动词）
• serves as → is（copula 回避；linter style-substitution advisory）

结构 tell（L2 advisory）

• repeated parallel frames，例如连续三句相同首语或 modal；
• announced enumeration 与 setup/list/wrap-up symmetry；
• 多段重复相同开场、展开和收束几何；
• X — that is, Y 同时触发 em-dash L0 target 与可能的冗余 advisory；
• 分词尾巴（-ing tail）：..., highlighting/underscoring/demonstrating X 把解读挂在句尾冒充分析深度。改写为带主语和证据的独立句，或删除 （linter ing-tail advisory）；
• 阐释式冒号（colon-appositive）：X: the rule that ... 这类 "名词: 展开" 结构是 X — that is, Y 的冒号变体。改写为限定从句、 两个句子或 ", so ..."；caption 标签（Left: ...）与真正的列表规格 说明可保留（linter colon-elaboration advisory；用户规则 2026-07-16）。

结构模式必须结合 section、样本量、calibration 和科学功能判断。技术列表若编码真实 分类，不应为了制造参差而破坏可读性。

punctuation / 排版

• 数字与单位之间用 \,（thin space），不要 LLM 习惯的普通空格。
• 千分位用 \,（thin space）或 ,，不要无分隔。
• 不要在文中写 etc.（学术写作可接受但 LLM 滥用），改为完整列举或具体范围。

review 阶段的强制 grep

# em-dash：必须 0
grep -n -E '—|---|\\textemdash' main.tex

# Tier A（必删；正文中不允许出现，包括变体）
grep -n -E -i '(delve|leveraged|leverages|leveraging|paved?|paves|paving|shed[s]?|shedding|showcase[sd]?|showcasing|seamless(ly)?|holistic(ally)?|comprehensively|crucially|utilizes|utilizing|underscor(e|es|ed|ing)|tapestry|testament|pivotal|realms?|recent advances|despite significant|with the advent|in recent years|it is worth)' main.tex

# Tier B（定位；是否超过每节每词 cap 由 linter 按 section 计算）
grep -n -E -i '^\s*(Furthermore|Moreover|Additionally|Importantly|Interestingly|Notably),' main.tex
grep -n -E -i '\b(robust|robustly|comprehensive|utilize[sd]?|leverage|leverages|leveraging|leveraged|intricate|foster(s|ing|ed)?)\b' main.tex

# 顽固替换组（不分级）
grep -n -E -i '\b(in order to|aim to|facilitate|serves as)\b' main.tex

# 结构 advisory 定位（-ing 尾巴 / 阐释式冒号）
grep -n -E -i ',\s+(highlighting|underscoring|showcasing|emphasi[sz]ing|illustrating|demonstrating|signal[l]?ing|revealing|reflecting)\b' main.tex
grep -n -E '([A-Za-z0-9]|\}): [a-z$\\]' main.tex

Tier A / em-dash 残留 = l0_target。 Tier B 超频 = 同词在同 section 的第 2 次及以后为 l0_target。

Companion evidence from /sci-paper:de-ai calibration: corpus assets supply descriptive frequencies and calibration. Re-run python tools/extract_style.py when the corpus changes. They may suggest future policy changes, but do not silently redefine the current consequence classes or cap.

Claim–Evidence Discipline / 声明-证据纪律

QD 类规则（claim-evidence defects 在 SCIPAPER_STANDARD §2 QD 下是 integrity_blocker）。本节给出操作化检查；条目改编自 academic-humanizer Layer 4（MIT，见文末 Provenance），并按天体物理语料重校准。

对每个经验性声明检查两件事：(a) 它是否有正文内的数字、图、表或引用支撑； (b) 动词强度是否不超过证据强度。

• 无支撑声明 → 补证据指针或降级。 ❌ Our method is more robust. ✅ Our method's accuracy drops by 2 points under distribution shift, versus 11 points for the baseline (Figure 3).
• 动词强于证据 → 降级。 ❌ This demonstrates that our method is universally superior. ✅ On these three datasets, our method matches or exceeds the strongest baseline (Table 2).
• 模糊量级 → 有归属的数字或区间。 ❌ a large improvement. ✅ a 2--6% improvement in balanced accuracy over the strongest baseline. 区间优于单一均值（除非均值方法已声明）；每个数字注明方法、指标、基线。 做比较时先打最强对手，不打平凡基线。
• significantly 必须有伴随检验或数字；孤立的 "significantly better" 是声明缺陷。注意这是证据条件规则，不是词法禁令：astro curated corpus 中 demonstrate*（0.147/1k）与 significantly（0.274/1k，合并语料实测 2026-07-16）都是正常用词，禁词式移植（ML 会议口味）会误伤本领域写作—— 只有"动词/副词超出证据"才构成 finding。

防过度纠正 / Preserve List（rewrite 护栏）

改编自 academic-humanizer Layer 3（MIT）。De-AI 重写循环的反向风险是 "把校准的 hedging 改强"——这会制造 over-claiming，比留下 AI 词更糟。 与 SCIPAPER_STANDARD §6 rewrite eligibility（stance/modality/qualifier 不可变）同源；此处是写作端明细。

• 证据绑定的 hedging 是正确且必需的。 suggests, is consistent with, we hypothesize that, may indicate, appears to 在声明真有 不确定性时保留。把 "the results suggest X" 改成 "the results prove X" 是制造 over-claim，属 rewrite eligibility 违规。
• 被动语态在施动者无关时合法："Samples were normalized to total protein." 不要为主动而主动。
• 第一人称复数 "we" 是学术标准，不改写回避。
• 分号与偶发三联适度可用；em-dash 是唯一零容忍标点。
• 正式定义、命名方法/指标、术语、公式、符号逐字保留。
• 数字、公式、引用永不发明、丢弃或改动；cite key 全保留。

Citation Standards

• Only cite papers that genuinely support the claim being made.
• For established results, cite the original paper, not a review (unless the review adds value).
• For software: cite the primary paper for each library.
• Never fabricate or hallucinate citations. If unsure, flag with [CITATION NEEDED].

Key References / 关键参考文献 [WGL]

• Weak lensing formalism: Bartelmann & Schneider (2001), Schneider et al. (1998)
• Aperture mass / Schirmer filter: Schirmer et al. (2007), Schneider (1996)
• NFW profile: Navarro, Frenk & White (1996, 1997)
• E(2)-equivariant CNNs: Weiler & Cesa (2019), escnn library
• Swin Transformer: Liu et al. (2021)
• SBI / Neural Posterior Estimation: Cranmer, Brehmer & Louppe (2020)
• Persistent homology / TDA: Edelsbrunner & Harer (2010)
• LoVoCCS survey: Fu et al. (2022)

Provenance / 借鉴出处

The 2026-07-16 additions (Tier A/B word extensions, serves as, the -ing-tail and colon-elaboration structure tells, the Claim–Evidence Discipline section, and the Preserve List) adapt material from academic-humanizer (MIT License, Copyright (c) 2026 AIScientists-Dev, itself building on blader/humanizer, MIT). Every lexical adoption was re-verified against the curated field corpora before tier assignment; venue-specific rules that conflict with astro usage (landscape, blanket demonstrate/ significantly bans) were deliberately not adopted.