# Monitor new LLM reasoning and reinforcement learning papers

**Use case:** 

Finds the most relevant arXiv and Hugging Face daily papers from the last 3 days about LLM reasoning, chain-of-thought and reinforcement learning, ranked by keyword relevance and community upvotes.

## Input

```json
{
  "sources": [
    "arxiv",
    "hfDailyPapers"
  ],
  "arxivCategories": [
    "cs.CL",
    "cs.LG",
    "cs.AI"
  ],
  "keywords": [
    "reasoning",
    "chain-of-thought",
    "reinforcement learning"
  ],
  "daysBack": 3,
  "maxPapers": 20,
  "rankBy": "radarScore",
  "maxArxivScan": 200,
  "minUpvotes": 0,
  "onlyWithCode": false,
  "modelTasks": [
    "text-generation"
  ],
  "maxModels": 0,
  "onlyNew": false,
  "llmEnrichment": false
}
```

## Output

```json
{
  "rank": {
    "label": "Rank"
  },
  "radarScore": {
    "label": "Radar score"
  },
  "title": {
    "label": "Title"
  },
  "published": {
    "label": "Published"
  },
  "hfUpvotes": {
    "label": "Hf upvotes"
  },
  "githubStars": {
    "label": "Github stars"
  },
  "githubRepo": {
    "label": "Github repo",
    "format": "link"
  },
  "primaryCategory": {
    "label": "Primary category"
  },
  "matchedKeywords": {
    "label": "Matched keywords"
  },
  "absUrl": {
    "label": "Abs url",
    "format": "link"
  }
}
```

## How it works

### 1. Sign up on Apify or log in

The AI Research Radar: arXiv + Hugging Face Papers Actor is ready to run with the inputs already set for "Monitor new LLM reasoning and reinforcement learning papers". [Sign up or log in](https://console.apify.com/create-task-from-example/WFaS8DENP2FIw627Q) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Monitor new LLM reasoning and reinforcement learning papers" task

The AI Research Radar: arXiv + Hugging Face Papers Actor will start running based on the input automatically. You can also change all the input fields, like sources, arxiv categories, and more, to match your specific use case.

### 3. Receive the "Monitor new LLM reasoning and reinforcement learning papers" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as rank, radar score, title, and more.

### 4. Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also [run the "Monitor new LLM reasoning and reinforcement learning papers" task via API](https://apify.com/tendoyo/ai-research-radar.md).

## About this Actor

This example demonstrates how to use [AI Research Radar: arXiv + Hugging Face Papers](https://apify.com/tendoyo/ai-research-radar.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/tendoyo/ai-research-radar.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/tendoyo/ai-research-radar.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).