refine: nativescript 第 1 轮（refiner 定向修复）

修复案例 1 unused import / 案例 3 未定义 width height / 学到什么分类混淆
未消化建议: 踩坑第3条 ARC 首次出现未解释（zero-base 建议）

Co-authored-by: Cursor <cursoragent@cursor.com>

Author: estelledc

data/candidates.jsonl

@@ -1,5 +1,5 @@
 {
-  "generated": "2026-06-06T14:02:11.789Z",
+  "generated": "2026-06-06T14:08:17.527Z",
   "count": 0,
   "items": []
 }

data/classification-unresolved.json

@@ -675,7 +675,7 @@
 {"slug":"react","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"智能体与 LLM","source":"category","confidence":"high","rawCategory":"机器学习"}
 {"slug":"realm","area":"papers","theme":"NLP","themeId":"nlp","subcategory":"自然语言处理","source":"category","confidence":"high","rawCategory":"NLP"}
 {"slug":"red-1993","area":"papers","theme":"网络协议","themeId":"network-protocols","subcategory":"网络协议","source":"candidates.topic","confidence":"high","rawCategory":"网络协议"}
-{"slug":"reed-onion-routing-1998","area":"papers","theme":"安全与隐私","themeId":"security-privacy","subcategory":"安全与隐私","source":"candidates.topic","confidence":"high","rawCategory":"security-privacy"}
+{"slug":"reed-onion-routing-1998","area":"papers","theme":"安全与隐私","themeId":"security-privacy","subcategory":"安全与隐私","source":"candidates.topic","confidence":"high","rawCategory":"安全与隐私"}
 {"slug":"reed-solomon-1960","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"信息论","source":"category","confidence":"high","rawCategory":"机器学习"}
 {"slug":"refinement-types-1991","area":"papers","theme":"编程语言","themeId":"programming-languages","subcategory":"类型与 PL 理论","source":"candidates.topic","confidence":"high","rawCategory":"编程语言"}
 {"slug":"reflexion","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"智能体与 LLM","source":"category","confidence":"high","rawCategory":"机器学习"}

data/classification.jsonl

@@ -1,7 +1,8 @@
 {"slug":"lexical","area":"projects","path":"src/content/docs/projects/lexical.md","score":3,"reasons":["lines:685>250","h2-hits:1/11"],"lines":685,"h2_hits":1,"status":"available","claimed_by":null,"attempts":0}
 {"slug":"lottie","area":"projects","path":"src/content/docs/projects/lottie.md","score":3,"reasons":["lines:464>250","h2-hits:1/11"],"lines":464,"h2_hits":1,"status":"available","claimed_by":null,"attempts":0}
 {"slug":"plane","area":"projects","path":"src/content/docs/projects/plane.md","score":3,"reasons":["lines:854>250","h2-hits:1/11"],"lines":854,"h2_hits":1,"status":"available","claimed_by":null,"attempts":0}
-{"slug":"hindley-milner","area":"papers","path":"src/content/docs/papers/hindley-milner.md","score":2,"reasons":["lines:281>250"],"lines":281,"h2_hits":11,"status":"available","claimed_by":null,"attempts":0}
+{"slug":"decord","area":"projects","path":"src/content/docs/projects/decord.md","score":2,"reasons":["lines:251>250"],"lines":251,"h2_hits":11,"status":"available","claimed_by":null,"attempts":0}
+{"slug":"hindley-milner","area":"papers","path":"src/content/docs/papers/hindley-milner.md","score":2,"reasons":["lines:279>250"],"lines":279,"h2_hits":11,"status":"available","claimed_by":null,"attempts":0}
 {"slug":"jitsi-meet","area":"projects","path":"src/content/docs/projects/jitsi-meet.md","score":2,"reasons":["lines:254>250"],"lines":254,"h2_hits":11,"status":"available","claimed_by":null,"attempts":0}
 {"slug":"livekit","area":"projects","path":"src/content/docs/projects/livekit.md","score":2,"reasons":["lines:268>250"],"lines":268,"h2_hits":11,"status":"available","claimed_by":null,"attempts":0}
 {"slug":"lmms-eval","area":"projects","path":"src/content/docs/projects/lmms-eval.md","score":2,"reasons":["lines:255>250"],"lines":255,"h2_hits":11,"status":"available","claimed_by":null,"attempts":0}

data/rewrite-pool.jsonl

@@ -198,6 +198,7 @@ cutlass-2020
 cytoscape-js
 dafny-2010
 dalle-2
+danezis-sphinx-2009
 dapper-2010
 dash-numa-1992
 dataflow-model-2015
@@ -226,6 +227,7 @@ dijkstra-1965
 dijkstra-goto
 dijkstra-shortest-path
 din-2018
+dingledine-mixminion-2003
 dino
 disco-1997
 disel-2018
@@ -310,6 +312,7 @@ fpga-hls-2011
 frama-c-2012
 frangipani-1997
 frank-effects
+freedman-psi-2004
 frenetic-2011
 fsdp-2023
 fsrs-spaced-repetition
@@ -673,6 +676,7 @@ react
 react-server-components
 realm
 red-1993
+reed-onion-routing-1998
 reed-solomon-1960
 refinement-types-1991
 reflexion
@@ -963,6 +967,7 @@ aragon
 arangodb
 arbitrum
 ardour
+arduino-cli
 argent-x
 argilla
 argo-workflows
@@ -977,6 +982,7 @@ ast-grep
 asterisk
 astro
 asynq
+atom
 aubio
 audacity
 auth-js
@@ -1325,6 +1331,7 @@ lodestar
 loki
 longhorn
 lottie
+love2d
 lsd
 lucia
 luxon
@@ -1384,6 +1391,7 @@ mysql
 mysql-server
 nanobrowser
 nanostores
+nativescript
 nats
 nats-server
 ncdu

data/written.txt

@@ -178,7 +178,6 @@ for g in gaussians:
 - [[magic3d-2023]] —— Magic3D — 把 DreamFusion 的 NeRF 拆成"先粗后精"两阶段
 - [[marching-cubes-1987]] —— Marching Cubes 1987 — 把体数据切成立方体查表生成三角网格
 - [[meagher-1982-octree]] —— Meagher 1982 八叉树 — 把立方体一分为八，递归地装下一整个 3D 世界
-- [[minetest]] —— Luanti / Minetest — 给自己造一个开源体素游戏引擎
 - [[mode-connectivity-2018]] —— Mode Connectivity — 神经网络的两个最优解之间有低洼走廊
 - [[monaghan-1992-sph]] —— SPH — 把流体拆成一群带核的粒子
 - [[mueller-2007-pbd]] —— Position Based Dynamics — 跳过力，直接挪位置

src/content/docs/papers/3d-gaussian-splatting.md

@@ -143,9 +143,7 @@ vs 李世石第二局第 37 手，AlphaGo 在五线（远离中央）下了一
 
 - [[a3c-2016]] —— A3C — 多个 CPU 同时跑游戏，让 RL 不再吃 GPU
 - [[attention]] —— Attention Is All You Need
-- [[bert]] —— BERT — 双向 Transformer 预训练
 - [[chatbot-arena-2024]] —— Chatbot Arena — 让真人盲投，给 LLM 排出公允座次
-- [[chinchilla]] —— Chinchilla — 训练大模型的数据/参数最优比
 - [[debate-2018]] —— AI safety via debate — 让两个 AI 互辩，人类只当评委
 - [[decision-transformer-2021]] —— Decision Transformer — 把强化学习当成"文字接龙"
 - [[deepseek-r1]] —— DeepSeek R1 — 强化学习推理模型

src/content/docs/papers/alphago.md

@@ -139,7 +139,6 @@ base 模型 8 个头独立学：头 1 学语法（主语↔谓语）、头 2 学
 - [[din-2018]] —— DIN — 让推荐模型按你看的广告决定该激活你哪段历史
 - [[distserve]] —— DistServe — 把 prefill 和 decode 拆到不同 GPU 上跑
 - [[dit]] —— DiT — Diffusion Transformer
-- [[dns]] —— DNS — 把全球域名解析切成一棵可分布维护的树
 - [[dpo]] —— DPO — Direct Preference Optimization
 - [[dpr-2020]] —— DPR — 用 BERT 双塔把检索从 BM25 时代拉进稠密向量时代
 - [[dqn]] —— DQN — Deep Q-Network
@@ -190,6 +189,7 @@ base 模型 8 个头独立学：头 1 学语法（主语↔谓语）、头 2 学
 - [[ppo]] —— PPO — Proximal Policy Optimization
 - [[program-comprehension-fmri]] —— Program Comprehension fMRI — 程序员读代码时大脑亮的是语言区不是数学区
 - [[prototypical-networks-2017]] —— Prototypical Networks — 每类算个均值，比距离就够了
+- [[quic]] —— QUIC — 把可靠传输从内核搬到用户空间
 - [[realm]] —— REALM — 把检索器和 BERT 一起预训练的第一篇论文
 - [[reformer-2020]] —— Reformer — 用哈希分桶把 attention 从 O(L²) 压到 O(L log L)
 - [[resnet]] —— ResNet — 残差连接

src/content/docs/papers/attention.md

@@ -155,7 +155,6 @@ STARK 用 Merkle 树承诺多项式求值：
 - [[bohme-aflfast-2016]] —— AFLFast — 灰盒 Fuzz 的马尔可夫调度
 - [[bowe-halo-2019]] —— Halo: Recursive Proof Composition without a Trusted Setup
 - [[cadar-klee-2008]] —— KLEE — 符号执行自动生成高覆盖测试
-- [[ducas-dilithium-2018]] —— CRYSTALS-Dilithium — 量子计算机来了也签不掉的数字签名
 - [[gabizon-plonk-2019]] —— PLONK: Permutations over Lagrange-bases for Oecumenical Noninteractive arguments of Knowledge
 - [[gentry-fhe-2009]] —— Gentry FHE — 全同态加密开山
 - [[yao-garbled-circuits-1986]] —— Yao 混淆电路 — 让两人合算函数却互不泄密

src/content/docs/papers/ben-sasson-stark-2018.md

@@ -130,6 +130,7 @@ GPT 像同传翻译，只能基于已经听到的内容判断；BERT 像填空
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
 - [[adamw-2017]] —— AdamW — 把 weight decay 从梯度里拆出来
+- [[alphago]] —— AlphaGo — 击败围棋世界冠军
 - [[ance-2020]] —— ANCE — 让模型自己挖训练负例，对比学习的"自给自足"
 - [[attention]] —— Attention Is All You Need
 - [[bert4rec-2019]] —— BERT4Rec — 把 BERT 的 MLM 搬进序列推荐做双向建模
@@ -141,8 +142,8 @@ GPT 像同传翻译，只能基于已经听到的内容判断；BERT 像填空
 - [[colbert-2020]] —— ColBERT — 让 BERT 检索既准又能扛大规模
 - [[colbert-v2]] —— ColBERTv2 — 让向量检索既精又能扛百万文档
 - [[deberta-2021]] —— DeBERTa — 把"内容"和"位置"拆成两路独立看的 BERT
-- [[dns]] —— DNS — 把全球域名解析切成一棵可分布维护的树
 - [[dpr-2020]] —— DPR — 用 BERT 双塔把检索从 BM25 时代拉进稠密向量时代
+- [[dqn]] —— DQN — Deep Q-Network
 - [[drmm-2016]] —— DRMM — 检索里的匹配是相关性不是语义相似
 - [[dssm-2013]] —— DSSM — 把 query 和文档各编码成 128 维向量再算余弦
 - [[e5-2022]] —— E5 — 用海量"自然出现的文本对"训通用 embedding
@@ -156,6 +157,7 @@ GPT 像同传翻译，只能基于已经听到的内容判断；BERT 像填空
 - [[longformer-2020]] —— Longformer — 滑窗加少数全局 token，把长文档喂进 Transformer
 - [[mae]] —— MAE — Masked Autoencoders
 - [[maml-2017]] —— MAML — 学一个"好起点"，几步就能学会新任务
+- [[ppo]] —— PPO — Proximal Policy Optimization
 - [[quic]] —— QUIC — 把可靠传输从内核搬到用户空间
 - [[rag-lewis-2020]] —— RAG (Lewis 2020) — 检索增强生成奠基
 - [[realm]] —— REALM — 把检索器和 BERT 一起预训练的第一篇论文