Add jobs and papers collection workflow
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# Papers
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这里持续收集 Agent 相关论文,并把论文转化成可复用判断、实验想法和项目输入。
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不按单一主题目录存放论文。每篇论文统一放在 `items/`,通过 metadata 支持多标签、多视图和后续前端索引。
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## Structure
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- [items](items/README.md): 每篇论文一个 Markdown 文件
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- [schema](schema.md): 论文笔记字段和写法
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- [source-registry](source-registry.md): 来源、搜索方式和筛选规则
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- [reading-queue](reading-queue.md): 阅读队列和优先级
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- [paper-insights](paper-insights.md): 周期性研究洞察
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## Collection Questions
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- 哪些论文真正解释了 Agent 系统能力?
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- 哪些方法有可靠证据,哪些只是 demo?
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- 哪些论文能指导项目实现或实验设计?
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- 哪些方向和 JD 市场需求形成呼应?
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- 哪些结论应该沉淀到 `docs/`?
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## Workflow
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1. 收集论文线索。
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2. 放入 [reading-queue](reading-queue.md)。
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3. 用 [paper-note](../templates/paper-note.md) 建立结构化笔记。
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4. 标注 topic、method、benchmark、relevance 和后续实验。
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5. 更新 [paper-insights](paper-insights.md)。
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6. 把稳定结论回流到 `docs/`、`experiments/` 或 `projects/`。
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# Paper Items
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每篇论文一个文件,文件名建议:
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```text
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YYYY-first-author-short-title.md
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```
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例子:
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```text
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2023-yao-react.md
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2024-yang-swe-agent.md
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```
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一篇论文可以有多个 topic,不要因为目录位置限制它的归类。后续可通过 frontmatter 生成搜索索引、标签页、图谱和阅读队列。
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# Paper Insights
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status: seed
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这个文件沉淀论文阅读后的研究判断。单篇论文放在 `papers/items/`,这里记录跨论文的趋势和结论。
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## Current Snapshot
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- date:
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- scope:
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- papers reviewed:
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## Strong Signals
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-
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## Weak or Unproven Claims
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-
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## Mature Directions
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-
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## Emerging Directions
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-
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## Experiment Candidates
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| Idea | Related Papers | Metric | Notes |
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| --- | --- | --- | --- |
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| _TBD_ | _TBD_ | _TBD_ | _TBD_ |
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## Links Back to Knowledge Base
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- docs to update:
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- experiments to create:
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- projects affected:
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- job skills connected:
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# Reading Queue
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status: seed
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| Priority | Paper | Topic | Reason | Status | Link |
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| --- | --- | --- | --- | --- | --- |
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| _TBD_ | _TBD_ | _TBD_ | _TBD_ | queued | _TBD_ |
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## Priority Rules
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- `P0`: 直接影响当前项目或实验。
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- `P1`: 和 Agent 核心能力强相关,有可靠证据。
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- `P2`: 值得了解,但暂不影响近期决策。
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- `P3`: 只作为背景材料。
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## Review Rhythm
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- 每周补充新条目。
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- 每月清理低价值或重复条目。
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- 阅读完成后移动到 `papers/items/`,并在这里更新状态。
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# Papers Schema
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论文笔记使用 YAML frontmatter 加正文。
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## Frontmatter
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```yaml
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---
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type: paper
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title:
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authors:
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year:
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venue:
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url:
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code_url:
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source: arxiv / openreview / acl / github / blog / other
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collected_at: YYYY-MM-DD
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status: queued / skimmed / reading / summarized / reproduced / deprecated
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relevance: high / medium / low
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topics:
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- tool-use
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methods:
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-
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benchmarks:
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-
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models:
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-
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datasets:
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-
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related_concepts:
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-
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related_jobs:
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-
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related_experiments:
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-
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related_projects:
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-
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---
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```
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## Body
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```text
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# <paper title>
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## One-line Takeaway
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一句话说明它对我们有什么价值。
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## Problem
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它解决什么问题,为什么重要?
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## Core Idea
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核心方法、系统设计或实验思路。
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## Evidence
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实验设计、benchmark、样本、对比方法和结果。
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## Limitations
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-
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## Useful For Us
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-
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## Follow-up Experiments
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-
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## Links
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- concepts:
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- jobs:
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- experiments:
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- projects:
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```
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## Field Rules
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- `url` 必填。
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- `collected_at` 必填,方便后续看研究趋势。
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- `topics` 可以多选,不要把论文强行归到单一主题。
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- `status` 反映阅读阶段,不代表论文质量。
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- `relevance` 是对本知识库的价值判断,不是论文绝对价值。
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# Papers Source Registry
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这个文件记录论文收集来源、搜索策略和筛选规则。
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## Source Types
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| Source | Use |
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| --- | --- |
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| arXiv | 快速发现新论文 |
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| OpenReview | ICLR、NeurIPS 等会议投稿和评审线索 |
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| ACL Anthology | NLP、工具使用、评估和 RAG 相关论文 |
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| Conference proceedings | NeurIPS、ICLR、ICML、ACL、EMNLP、KDD、WWW 等 |
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| Papers with Code | 查找 benchmark、代码和任务关联 |
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| GitHub | 查找系统实现、复现代码和高影响项目 |
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| Company research blogs | 查找工业实践和系统设计线索 |
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## Search Queries
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Agent 系统:
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```text
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LLM agent planning tool use evaluation
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language agents benchmark tool use memory
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autonomous agents LLM planning reflection
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```
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RAG 和知识:
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```text
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retrieval augmented generation agent memory evaluation
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LLM long context retrieval agent
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agent knowledge base retrieval planning
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```
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评估:
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```text
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LLM agent evaluation benchmark
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tool use benchmark language model agents
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SWE-bench agent evaluation coding
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```
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多 Agent:
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```text
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multi-agent LLM collaboration benchmark
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LLM agents debate cooperation workflow
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```
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代码 Agent:
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```text
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coding agent software engineering benchmark LLM
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SWE-bench agent repair repository
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```
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## Triage Rules
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优先进入阅读队列:
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- 直接解释 Agent 架构、工具使用、记忆、评估或代码 Agent。
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- 有 benchmark、消融实验或可复现代码。
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- 和 JD 高频技能有关。
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- 能生成可执行实验或项目改进。
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降低优先级:
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- 只有 demo,没有清晰评估。
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- 和 Agent 关联很弱,只是泛泛使用 LLM。
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- 方法高度依赖不可获得的数据或闭源系统。
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## Codex Collection Role
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当你让我收集论文时,我会:
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1. 先确认范围:主题、年份、会议、是否需要最新论文。
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2. 在线检索并优先选择原始论文页、会议页、代码仓库。
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3. 去重并放入 [reading-queue](reading-queue.md)。
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4. 为高优先级论文创建 `papers/items/` 笔记。
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5. 提取方法、证据、限制、可复现实验和项目启发。
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6. 更新 [paper-insights](paper-insights.md),并把稳定结论回流到 `docs/`。
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## Collection Cadence
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- weekly: 新论文和高价值代码仓库线索
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- monthly: 主题趋势和阅读优先级调整
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- quarterly: 把成熟结论合并进 `docs/`,把可验证想法转成 `experiments/`
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