# BAAI / Zhiyuan AI Research Papers Scraper (`jungle_synthesizer/baai-zhiyuan-ai-research-publication-scraper`) Actor Scrapes curated AI research papers from BAAI (Beijing Academy of AI, hub.baai.ac.cn). Extracts paper titles, authors, abstracts, arxiv IDs, venues, curator notes in Chinese, and links. - **URL**: https://apify.com/jungle\_synthesizer/baai-zhiyuan-ai-research-publication-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## BAAI / Zhiyuan AI Research Papers Scraper Extract the current curated AI research paper feed from BAAI (Beijing Academy of Artificial Intelligence, 智源研究院) at **hub.baai.ac.cn**. Each run fetches the hotness-sorted daily paper feed and enriches every paper with full editorial curator notes written in Chinese by BAAI staff. ### What You Get Each record includes: | Field | Description | |-------|-------------| | `paper_title_en` | Paper title in English | | `arxiv_id` | ArXiv paper ID (e.g. `2606.06624`) | | `authors` | List of author names | | `publication_date` | Release date (ISO 8601) | | `abstract_zh` | Full Chinese-language abstract | | `keywords_zh` | Chinese subject tags (e.g. 机器学习, 生成模型) | | `keywords_en` | ArXiv category codes (e.g. cs.LG, cs.RL) | | `pdf_url` | Direct PDF download link (BAAI-hosted mirror) | | `baai_curator_note` | Structured editorial notes: [简介] abstract, [问题] problem addressed, [思路] key approach, [亮点] highlights, [相关] related work | | `baai_url` | Canonical BAAI paper page URL | | `cited_by_count` | BAAI hotness score | | `source` | Always `hub.baai.ac.cn` | ### Why BAAI? BAAI (智源研究院) is China's premier government-backed AI research institute, behind the WuDao foundation model series, the BGE embedding family, and the Aquila LLM. Their curated daily paper feed covers ~10–30 papers per day with Chinese-language editorial summaries not available on arXiv — the editorial value add is the key moat. **Use cases:** - Track Chinese AI research output for competitive intelligence - Build a joinable dataset with an ArXiv scraper (shared `arxiv_id` key) - Monitor BAAI's curated AI research highlights in Chinese for sino-watchers - Feed into downstream LLM pipelines with Chinese-language summaries ### Input | Parameter | Required | Default | Description | |-----------|----------|---------|-------------| | `maxItems` | Yes | 5 | Maximum number of papers to return (current feed has ~9 per run) | ### How It Works 1. Fetches `hub.baai.ac.cn/papers` — a Nuxt SSR page that embeds the current hotness feed in `window.__NUXT__` state (no JavaScript execution required) 2. Extracts up to 9 paper UUIDs from the SSR data 3. Fetches each paper's detail page (`hub.baai.ac.cn/paper/`) — also fully SSR-rendered 4. Merges listing data (basic fields) with detail data (curator notes, extended keywords) 5. Emits one record per paper > **Note on scope:** The BAAI listing page renders the current editorial feed (~9 papers) via server-side rendering. Further pagination is client-side only (infinite scroll). Each run captures the current curated snapshot — run daily to build a historical archive. ### Sample Output ```json { "paper_title_en": "Rethinking the Trust Region in LLM Reinforcement Learning", "arxiv_id": "2602.04879", "authors": ["Penghui Qi", "Xiangxin Zhou", "Zichen Liu"], "publication_date": "2026-02-04", "abstract_zh": "强化学习（RL）已成为大语言模型（LLM）微调的基石...", "keywords_zh": ["机器学习", "强化学习", "大语言模型"], "keywords_en": ["cs.LG", "cs.CL", "cs.AI"], "pdf_url": "https://simg.baai.ac.cn/paperfile/572bbeac-4516-4c34-8bc2-15ee9ef5bbb7.pdf", "baai_curator_note": "[简介] 强化学习（RL）已成为大语言模型...\n\n[问题] 如何设计更合理的信任域约束...\n\n[思路] 提出散度近端策略优化（DPPO）...", "baai_url": "https://hub.baai.ac.cn/paper/572bbeac-4516-4c34-8bc2-15ee9ef5bbb7", "cited_by_count": 120, "source": "hub.baai.ac.cn" } ```` ### Notes - **China-hosted:** The site is hosted in China. Cross-border latency is factored into timeouts (45 seconds per request). Runs from US/EU Apify datacenters may experience occasional delays. - **No authentication required:** The papers feed is publicly accessible without login. - **Daily curation:** BAAI curates ~10–30 papers per day. Running this actor daily gives you a rolling archive of their editorial picks. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of records to scrape ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 5 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 5 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/baai-zhiyuan-ai-research-publication-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 5, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/baai-zhiyuan-ai-research-publication-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 5 }' | apify call jungle_synthesizer/baai-zhiyuan-ai-research-publication-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/baai-zhiyuan-ai-research-publication-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "BAAI / Zhiyuan AI Research Papers Scraper", "description": "Scrapes curated AI research papers from BAAI (Beijing Academy of AI, hub.baai.ac.cn). Extracts paper titles, authors, abstracts, arxiv IDs, venues, curator notes in Chinese, and links.", "version": "0.1", "x-build-id": "COzWpEIMDNV1g37e1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~baai-zhiyuan-ai-research-publication-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-baai-zhiyuan-ai-research-publication-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~baai-zhiyuan-ai-research-publication-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-baai-zhiyuan-ai-research-publication-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~baai-zhiyuan-ai-research-publication-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-baai-zhiyuan-ai-research-publication-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of records to scrape", "default": 5 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```