deep-research

name	deep-research
description	Deep research and analysis tool. Generates comprehensive HTML reports on any topic, domain, paper, or technology. Use when user asks to research, analyze, investigate, deep-dive, or generate a report on any subject. Supports academic papers (arXiv), technologies, trends, comparisons, and general topics.

Deep Research

Analyze any topic, domain, or paper and generate a beautiful HTML report using Actionbook browser automation and json-ui rendering.

Usage

/deep-research:analyze <topic>
/deep-research:analyze <topic> --lang zh
/deep-research:analyze <topic> --output ./reports/my-report.json

Or simply tell Claude: "帮我深度研究 XXX 并生成报告" / "Research XXX and generate a report"

Parameters

Parameter	Required	Default	Description
topic	Yes	-	The subject to research (any text)
--lang	No	both	Language: en, zh, or both (bilingual)
--output	No	./output/<topic-slug>.json	Output path for JSON report

Topic Detection

Pattern	Type	Strategy
arxiv:XXXX.XXXXX	Paper	arXiv Advanced Search (Step 2b) + ar5iv deep read
doi:10.XXX/...	Paper	Resolve DOI, then arXiv Advanced Search for related work
Academic keywords (paper, research, model, algorithm)	Academic topic	arXiv Advanced Search (Step 2b) + Google for non-academic sources
URL	Specific page	Fetch and analyze the page
General text	Topic research	Google search + arXiv Advanced Search if relevant

Architecture

┌──────────┐     ┌──────────────┐     ┌──────────────┐     ┌──────────┐
│  Claude   │────▶│  Actionbook  │────▶│  Web Pages   │────▶│ Extract  │
│  Code     │     │  Browser CLI │     │  (multiple)  │     │ Content  │
└──────────┘     └──────────────┘     └──────────────┘     └─────┬────┘
      │                                                           │
      │          ┌──────────────┐     ┌──────────────┐           │
      ├─────────▶│  Actionbook  │     │ arXiv Adv.   │           │
      │          │  search/get  │────▶│ Search Form  │──────────▶│
      │          │  (selectors) │     │ (40+ fields) │           │
      │          └──────────────┘     └──────────────┘           │
      │                                                           │
      │    Actionbook indexes arXiv form selectors,               │
      │    enabling field-specific, filtered academic              │
      │    searches that WebFetch/WebSearch CANNOT do.             │
      │                                                           │
┌──────────┐     ┌──────────────┐     ┌──────────────┐           │
│  Open in │◀────│   json-ui    │◀────│  Write JSON  │◀──────────┘
│  Browser │     │   render     │     │  Report      │  Synthesize
└──────────┘     └──────────────┘     └──────────────┘

Why Actionbook, Not WebFetch/WebSearch?

Capability	Actionbook	WebFetch/WebSearch
Operate complex web forms (dropdowns, checkboxes, date pickers)	Yes — uses indexed selectors	No
arXiv: search by Author, Title, Abstract separately	Yes — #terms-0-field select	No — keyword only
arXiv: filter by subject (CS, Physics, Math, ...)	Yes — category checkboxes	No
arXiv: filter by date range or specific year	Yes — date inputs	No
Read pages with verified selectors (no guessing)	Yes — actionbook get	No — raw HTML parse
Interact with any indexed site's UI	Yes — click, type, select	No — read-only

This is the core value of Actionbook for research: it turns web forms into structured, programmable interfaces for AI agents.

MUST USE Actionbook CLI

Always use actionbook browser commands for web browsing. Never use WebFetch or WebSearch.

actionbook browser open <url>          # Navigate to page
actionbook browser snapshot            # Get accessibility tree
actionbook browser text [selector]     # Extract text content
actionbook browser screenshot [path]   # Capture visual
actionbook browser click <selector>    # Click element
actionbook browser close               # Close browser (ALWAYS do this at end)

Complete Workflow

Step 1: Plan Search Strategy

Based on the topic, generate 5-8 search queries from different angles:

• Core definition / overview
• Latest developments / news
• Technical details / implementation
• Comparisons / alternatives
• Expert opinions / analysis
• Use cases / applications

Search order — ALWAYS query Actionbook API first, then search:

Step	Action	Why
Step 2 (FIRST)	Query Actionbook API	Get verified selectors for arXiv Advanced Search form, ar5iv papers, and any other known sites BEFORE browsing. This is the foundation for all subsequent steps.
Step 3 (SECOND)	arXiv Advanced Search	Use Actionbook selectors from Step 2 to perform multi-field, filtered academic search. Even non-academic topics often have relevant papers.
Step 4 (THIRD)	Google / Bing search	Supplement with blogs, news, code, discussions, non-academic sources.

IMPORTANT: Always query Actionbook API first (Step 2) to get selectors, then use them in arXiv Advanced Search (Step 3). This is what makes Actionbook-powered research fundamentally different from WebFetch/WebSearch — the agent knows the exact selectors for every form field before it even opens the browser.

Step 2: Query Actionbook API for Selectors (ALWAYS DO THIS FIRST)

BEFORE browsing any URL, query Actionbook's indexed selectors. This gives you verified CSS/XPath selectors instead of guessing.

# Search for indexed actions by domain
actionbook search "<keywords>" -d "<domain>"

# Get detailed selectors for a specific page
actionbook get "<domain>:/<path>:<area>"

Pre-indexed sites useful for research:

Site	area_id	Key Selectors
arXiv Advanced Search	arxiv.org:/search/advanced:default	40+ selectors: field select, term input, category checkboxes (CS/Physics/Math/...), date range filters, cross-list control — used in Step 3
ar5iv paper	ar5iv.labs.arxiv.org:/html/{paper_id}:default	h1.ltx_title_document (title), div.ltx_authors (authors), div.ltx_abstract (abstract), section.ltx_section (sections)
Google Scholar	scholar.google.com:/:default	#gs_hdr_tsi (search input), #gs_hdr_tsb (search button)
arXiv homepage	arxiv.org:/:default	Global search across 2.4M+ articles

For any URL you plan to visit, run actionbook search "<keywords>" -d "<domain>" to check if it's indexed. Use indexed selectors when available; fall back to actionbook browser snapshot for unindexed sites.

Example: Get arXiv Advanced Search selectors before searching:

# Query Actionbook for arXiv form selectors
actionbook get "arxiv.org:/search/advanced:default"
# Returns 40+ selectors: #terms-0-field, #terms-0-term, #classification-computer_science, etc.

Step 3: arXiv Advanced Search (Using Actionbook Selectors)

Key differentiator: WebFetch/WebSearch can only do simple keyword searches. Actionbook has indexed the entire arXiv Advanced Search form with 40+ verified selectors (queried in Step 2), enabling multi-field, multi-criteria academic searches — just like a human researcher would use the form.

Using the selectors obtained from Step 2, the Agent can:

Capability	Actionbook Selector	WebFetch/WebSearch
Search by specific field (Title, Author, Abstract)	#terms-0-field select → choose field	Not possible
Add multiple search terms with boolean logic	button "Add another term +"	Not possible
Filter by subject (CS, Physics, Math, etc.)	#classification-computer_science checkbox	Not possible
Filter by date range	#date-filter_by-3 radio + #date-from_date / #date-to_date	Not possible
Filter by specific year	#date-filter_by-2 radio + #date-year input	Not possible
Include/exclude cross-listed papers	#classification-include_cross_list-0/1 radio	Not possible
Control results display	#size select, #abstracts-0/1 radio	Not possible

Example: Search for recent CS papers by a specific author:

# Open arXiv Advanced Search
actionbook browser open "https://arxiv.org/search/advanced"

# 1. Set search field to "Author" and type author name
actionbook browser click "#terms-0-field"
actionbook browser click "option[value='author']"
actionbook browser type "#terms-0-term" "Yann LeCun"

# 2. Filter to Computer Science only
actionbook browser click "#classification-computer_science"

# 3. Restrict to past 12 months
actionbook browser click "#date-filter_by-1"

# 4. Show abstracts in results
actionbook browser click "#abstracts-0"

# 5. Submit search
actionbook browser click "button:has-text('Search'):nth(2)"

# 6. Extract results
actionbook browser text "#main-container"

Example: Search by title keywords in a date range:

actionbook browser open "https://arxiv.org/search/advanced"

# Search in "Title" field
actionbook browser click "#terms-0-field"
actionbook browser click "option[value='title']"
actionbook browser type "#terms-0-term" "large language model agent"

# Date range: 2025-01 to 2026-02
actionbook browser click "#date-filter_by-3"
actionbook browser type "#date-from_date" "2025-01-01"
actionbook browser type "#date-to_date" "2026-02-09"

# Submit and extract
actionbook browser click "button:has-text('Search'):nth(2)"
actionbook browser text "#main-container"

Step 4: Supplement with Google / Bing Search

After arXiv, use Google/Bing to find non-academic sources (blogs, news, docs, code, discussions):

# Search via Google
actionbook browser open "https://www.google.com/search?q=<encoded_query>"
actionbook browser text "#search"

# Or search via Bing
actionbook browser open "https://www.bing.com/search?q=<encoded_query>"
actionbook browser text "#b_results"

Parse the search results to extract URLs and snippets. Collect the top 5-10 most relevant URLs. For each discovered URL, query Actionbook API (Step 2 pattern) to check if the site is indexed before visiting.

Step 5: Deep Read Sources

For each relevant URL, first query Actionbook API (same as Step 2) to check if the site is indexed, then use verified selectors:

actionbook browser open "<url>"
actionbook browser text                # Full page text (fallback)
actionbook browser text "<selector>"   # Use Actionbook selector if indexed

For arXiv papers, try sources in this order (newer papers often fail on ar5iv):

# 1. Try ar5iv first (best structured selectors from Actionbook)
actionbook browser open "https://ar5iv.org/html/<arxiv_id>"
actionbook browser text "h1.ltx_title_document"  # Title
actionbook browser text "div.ltx_authors"         # Authors
actionbook browser text "div.ltx_abstract"        # Abstract
# NOTE: section.ltx_section often fails on newer papers — use "article" as fallback

# 2. If ar5iv content is truncated (<5KB), fall back to arxiv abstract + other sources
actionbook browser open "https://arxiv.org/abs/<arxiv_id>"
actionbook browser text "main"

# 3. Supplement with HuggingFace model cards and GitHub READMEs for full details
actionbook browser open "https://huggingface.co/papers/<arxiv_id>"
actionbook browser text "main"

Key lesson: Don't rely solely on ar5iv. Always cross-reference 3-4 sources for completeness.

For Google Scholar (indexed by Actionbook):

actionbook browser open "https://scholar.google.com"
# Type into search: use selector #gs_hdr_tsi
actionbook browser click "#gs_hdr_tsi"
# ... type query, click #gs_hdr_tsb to search

For unindexed sites, use snapshot to discover page structure:

actionbook browser open "<url>"
actionbook browser snapshot            # Get accessibility tree to find selectors
actionbook browser text "<discovered_selector>"

Step 6: Synthesize Findings

Organize collected information into a coherent report:

1. Overview / Executive Summary
2. Key Findings
3. Detailed Analysis
4. Supporting Data / Evidence
5. Implications / Significance
6. Sources

Step 7: Generate json-ui JSON Report

Write a JSON file following the @actionbookdev/json-ui schema. Use the Write tool.

Output path: ./output/<topic-slug>.json (or user-specified --output path)

Step 8: Render HTML

CRITICAL: You MUST try ALL fallback methods before giving up. Do NOT stop at the first failure.

IMPORTANT: Always use ABSOLUTE paths for JSON_FILE and HTML_FILE. Relative paths break when git rev-parse returns an absolute repo root.

Try each method one by one until one succeeds:

# Method 1: npx (recommended — works anywhere if npm is available)
npx @actionbookdev/json-ui render /absolute/path/to/report.json -o /absolute/path/to/report.html

# Method 2: Global install (if user ran: npm install -g @actionbookdev/json-ui)
json-ui render /absolute/path/to/report.json -o /absolute/path/to/report.html

# Method 3: Monorepo local path (fallback if inside actionbook project)
node "$(git rev-parse --show-toplevel)/packages/json-ui/dist/cli.js" render /absolute/path/to/report.json -o /absolute/path/to/report.html

NEVER give up silently. If all methods fail, tell the user:

1. The JSON report is saved at <path>
2. To install the renderer, run: npm install -g @actionbookdev/json-ui

Step 9: Open in Browser

# macOS
open <report.html>

# Linux
xdg-open <report.html>

Step 10: Close Browser

Always close the browser when done:

actionbook browser close

json-ui Report Template

IMPORTANT: Always include BrandHeader and BrandFooter.

{
  "type": "Report",
  "props": { "theme": "auto" },
  "children": [
    {
      "type": "BrandHeader",
      "props": {
        "badge": { "en": "Deep Research Report", "zh": "深度研究报告" },
        "poweredBy": "Actionbook"
      }
    },
    {
      "type": "Section",
      "props": { "title": { "en": "Overview", "zh": "概述" }, "icon": "paper" },
      "children": [
        {
          "type": "Prose",
          "props": {
            "content": { "en": "English overview...", "zh": "中文概述..." }
          }
        }
      ]
    },
    {
      "type": "Section",
      "props": { "title": { "en": "Key Findings", "zh": "核心发现" }, "icon": "star" },
      "children": [
        {
          "type": "ContributionList",
          "props": {
            "items": [
              {
                "badge": { "en": "Finding", "zh": "发现" },
                "title": { "en": "...", "zh": "..." },
                "description": { "en": "...", "zh": "..." }
              }
            ]
          }
        }
      ]
    },
    {
      "type": "Section",
      "props": { "title": { "en": "Detailed Analysis", "zh": "详细分析" }, "icon": "bulb" },
      "children": [
        {
          "type": "Prose",
          "props": { "content": { "en": "...", "zh": "..." } }
        }
      ]
    },
    {
      "type": "Section",
      "props": { "title": { "en": "Key Metrics", "zh": "关键指标" }, "icon": "chart" },
      "children": [
        {
          "type": "MetricsGrid",
          "props": { "metrics": [], "cols": 3 }
        }
      ]
    },
    {
      "type": "Section",
      "props": { "title": { "en": "Sources", "zh": "信息来源" }, "icon": "link" },
      "children": [
        {
          "type": "LinkGroup",
          "props": { "links": [] }
        }
      ]
    },
    {
      "type": "BrandFooter",
      "props": {
        "timestamp": "YYYY-MM-DDTHH:MM:SSZ",
        "attribution": "Powered by Actionbook",
        "disclaimer": {
          "en": "This report was generated by AI using web sources. Verify critical information independently.",
          "zh": "本报告由 AI 基于网络来源生成，请独立验证关键信息。"
        }
      }
    }
  ]
}

Paper Report Template (for arXiv papers)

When analyzing academic papers, use a richer template with:

• PaperHeader (title, arxivId, date, categories)
• AuthorList (authors with affiliations)
• Abstract (with keyword highlights)
• ContributionList (key contributions)
• MethodOverview (step-by-step method)
• ResultsTable (experimental results)
• Formula (key equations, LaTeX)
• Figure (paper figures from ar5iv)

Available json-ui Components

Component	Use For	Key Props
BrandHeader	Report header	badge, poweredBy
PaperHeader	Paper metadata	title, arxivId, date, categories
AuthorList	Authors	authors: [{name, affiliation}], maxVisible
Section	Major section	title, icon (paper/star/bulb/chart/code/link/info/warning)
Prose	Rich text	content (supports bold, italic, code, lists)
Abstract	Abstract text	text, highlights: ["keyword"]
ContributionList	Numbered findings	items: [{badge, title, description}]
MethodOverview	Step-by-step	steps: [{step, title, description}]
MetricsGrid	Key stats	metrics: [{label, value, trend, suffix}], cols
ResultsTable	Data table	columns, rows, highlights: [{row, col}]
Table	Generic table	columns: [{key, label}], rows, striped, compact
Callout	Info/tip/warning	type (info/tip/warning/important/note), title, content
Highlight	Blockquote	type (quote/important/warning/code), text, source
KeyPoint	Key finding card	icon, title, description, variant
CodeBlock	Code snippet	code, language, title, showLineNumbers
Formula	LaTeX equation	latex, block, label
Figure	Image(s)	images: [{src, alt, width}], label, caption
Image	Single image	src, alt, caption, width
DefinitionList	Term/definition	items: [{term, definition}]
LinkGroup	Source links	links: [{href, label, icon}]
Grid	Grid layout	cols, children
Card	Card container	padding (sm/md/lg), shadow
TagList	Tags	tags: [{label, color, href}]
BrandFooter	Footer	timestamp, attribution, disclaimer

json-ui Known Pitfalls

Pitfall	Symptom	Fix
MetricsGrid.suffix as i18n object	text.replace is not a function	suffix must be a plain string, not { "en": ..., "zh": ... }
MetricsGrid.value as number	Render error	value must be a string (e.g., "58.5" not 58.5)
Missing BrandHeader/BrandFooter	Report looks broken	Always include both
Table row values as i18n object	[object Object] in cells	Row cell values must be plain strings. Column label supports i18n, but row data does not. Use "Runtimes / 运行时" instead of { "en": "Runtimes", "zh": "运行时" }
Very long Prose content	Truncated render	Split into multiple Prose blocks or use subsections

i18n Support

All text fields support bilingual output unless noted above:

{ "en": "English text", "zh": "中文文本" }

For --lang en, use plain strings. For --lang zh, use plain Chinese strings. For --lang both (default), use i18n objects.

Exceptions:

• MetricsGrid props value and suffix must always be plain strings.
• Table row cell values must be plain strings (column label supports i18n, but row data does not). For bilingual, use "English / 中文" format.

Academic Paper Support

arXiv Papers

ar5iv.org HTML (preferred for reading, but often incomplete for papers < 3 months old):

Element	Selector (Actionbook-verified)	Reliability	Fallback
Title	h1.ltx_title_document	High	div.ltx_abstract includes title context
Authors	div.ltx_authors	High	—
Abstract	div.ltx_abstract	High	—
Full article	article	Medium	Use when section selectors fail
Sections	section.ltx_section	Low on new papers	article for all content
Section title	h2.ltx_title_section	Low on new papers	Parse from article text
Figures	figure.ltx_figure	Medium	—
Tables	table.ltx_tabular	Medium	—
Bibliography	.ltx_bibliography	Medium	—

Note: For papers submitted within the last ~3 months, ar5iv often renders incomplete content. Always check actionbook browser text 2>&1 | wc -c — if < 5KB, the page didn't fully render. Fall back to other sources.

arXiv API (for metadata via actionbook browser):

actionbook browser open "http://export.arxiv.org/api/query?id_list={arxiv_id}"
actionbook browser text

Recommended Source Priority for Papers

Based on testing, use this priority order for maximum coverage:

Priority	Source	What you get	Reliability
1	arxiv.org/abs/<id>	Abstract, metadata, submission history	Very high
2	huggingface.co/papers/<id>	Abstract, community comments, related models/datasets	Very high
3	GitHub repo (from search results)	README with method details, model zoo, code	High
4	HuggingFace model card	Training recipe, benchmark results, quick start	High
5	ar5iv.org/html/<id>	Full paper HTML with structured selectors	Medium (fails on new papers)
6	Google Scholar / Semantic Scholar	Citations, related work	Medium

Key insight: Don't rely on a single source. The combination of arxiv abstract + HuggingFace + GitHub typically gives 90%+ of what you need, even when ar5iv fails.

Other Academic Sources

Use actionbook browser to visit and extract content from:

• Google Scholar (scholar.google.com) — Actionbook indexed, use #gs_hdr_tsi for search
• Semantic Scholar (semanticscholar.org)
• Papers With Code (paperswithcode.com)
• Conference proceedings sites

Error Handling

Error	Action
Browser fails to open	Run actionbook browser status, retry
Page load timeout (30s)	Skip source, try next. Common on papers.cool, slow academic sites
ar5iv content truncated (<5KB)	Paper too new for ar5iv. Fall back to arxiv abstract + HuggingFace + GitHub
section.ltx_section not found	ar5iv rendering incomplete. Use actionbook browser text "article" or "main" instead
Actionbook selector not found	Use actionbook browser snapshot to discover actual page structure
actionbook search returns no results	Site not indexed. Use actionbook browser snapshot to find selectors manually
json-ui render crash (text.replace)	Check MetricsGrid suffix/value — must be plain strings, not i18n objects
npx @actionbookdev/json-ui fails	Run npm install -g @actionbookdev/json-ui and retry with json-ui render. If still fails, try monorepo local path
No search results	Broaden search terms, try different angles
Render failed	Save JSON, tell user path, and suggest: npm install -g @actionbookdev/json-ui

IMPORTANT: Always run actionbook browser close before finishing, even on errors.

Quality Guidelines

1. Breadth: Research from at least 3-5 diverse sources
2. Depth: Read full articles, not just snippets
3. Accuracy: Cross-reference facts across sources
4. Structure: Use appropriate json-ui components for each content type
5. Attribution: Always include source links in the report
6. Freshness: Prefer recent sources when relevance is equal

Chinese Content Quality Guidelines (中文内容质量规范)

CRITICAL: Chinese text must be written as native Chinese, NOT translated from English.

The zh field is not a translation — it is an independent Chinese version of the content. Write it as if authoring a Chinese tech article from scratch.

Common Problems to Avoid

Problem	Bad Example	Good Example
被动语态过多	"Wasm 已被广泛采用"	"Wasm 已经大规模落地"
英语语序直译	"广泛但常常不可见地被采用"	"已经深入渗透到各类产品中，只是用户浑然不觉"
生硬术语拼接	"语言无关的异步通信"	"不绑定特定编程语言的异步通信机制"
直译英文短语	"关键缺失部分"	"最后一块拼图"
"地道"当形容词	"地道绑定"	"符合各语言习惯的绑定"
逗号长句（一逗到底）	"A，B，C，D，E。"	拆成 2-3 个短句
引用原文硬翻	"「许多用户并未意识到它正在被使用」"	用中文重新表述引用的核心意思，必要时保留原文人名

Writing Rules

1. 先写中文，再对照英文查漏：不要从英文逐句翻译。先理解内容本质，用中文重新组织表达。
2. 主动语态优先：中文天然偏好主动句。"X 被 Y 采用" → "Y 采纳了 X" 或 "Y 已在用 X"。
3. 短句 > 长句：中文读者偏好短句。超过 40 字的句子应拆分。
4. 术语处理原则：
   • 有公认中文译名的术语用中文：服务器 → 服务器、容器 → 容器、沙箱 → 沙箱
   • 无公认译名的术语保留英文：Component Model、WASI、SIMD、Cold Start
   • 首次出现时中英对照：「组件模型（Component Model）」，之后可只用中文
   • 不要硬造中文译名（如"非干扰分析"，应写"Noninterference 分析"或解释性表述）
5. 数据和事实保持一致：中英文的数字、日期、引用来源必须完全一致。
6. 语气自然：可以用"说白了"、"换句话说"、"简单来说"等口语化连接词，避免全篇书面腔。
7. 标点规范：用中文标点（，。；：「」）而非英文标点。引号优先用「」。

Table & Short Text Rules (表格与短文本规则)

Table 行中的 "English / 中文" 格式是中文质量的重灾区。短文本更容易暴露机翻痕迹。

规则：中文部分必须是独立的中文表述，不是英文的逐词翻译。

Bad	Why Bad	Good
"应用级错误处理"	太生硬，像翻译腔	"应用层统一报错"
"高性能 Web 框架"	可以，但太平淡	"主打性能的 Web 框架"
"序列化框架"	可以接受	"序列化框架" ✓
"嵌入式异步执行器"	太绕	"嵌入式异步运行器"
"凸块分配器"	硬造译名，没人这么说	"Bump 分配器（arena 风格）"
"安全内存擦除"	太书面	"安全清零内存"
"多生产者多消费者通道"	太长	"MPMC 通道"
"类 React UI 框架"	OK	"类 React 的 UI 框架" ✓

表格中文短文本的核心原则：

1. 保留英文术语：Bump allocator、MPMC、ECS 这些没有公认中文译名的概念，直接保留英文。别硬翻。
2. 口语化 > 书面化：短文本读者扫一眼就过，要像说话一样自然。"主打性能" 比 "性能优化的" 好。
3. 别逐词翻译：先理解英文描述的意思，然后用中文重新说一遍。"Full-featured datetime" → "功能齐全的时间日期库"，不是 "全功能日期时间"。
4. 可以省略冗余修饰：英文 "A generic serialization/deserialization framework" → 中文只需 "序列化框架"。
5. 数量控制在 10 字以内：表格中文描述尽量控制在 10 个汉字以内，简短精准。

Bilingual Tone Reference

English Tone	Chinese Equivalent	Example
"X has reached a critical milestone"	不要直译"关键里程碑"	"X 迎来了重要转折点" 或 "X 进入成熟期"
"This is the key missing piece"	不要直译"关键缺失部分"	"这是最后一块拼图" 或 "补上了最关键的短板"
"excels at cold starts"	不要直译"在冷启动方面表现卓越"	"冷启动速度远超同类方案"
"security scrutiny intensifies"	不要直译"安全审查加强"	"安全领域的关注度持续升温"
"emerging as a standard"	不要直译"正成为标准"	"逐渐确立了标准地位" 或 "大有一统江湖之势"
"widespread but invisible adoption"	不要直译"广泛但不可见的采用"	"已经悄然渗透到各类产品中"