# csnl-paper-scout: Multi-Agent Neuroscience Paper Recommendation System and Academic Workflow Automation

> csnl-paper-scout is an AI-driven paper recommendation pipeline developed by the Cognitive and Systems Neuroscience Laboratory (CSNL) at Seoul National University. The system automatically discovers relevant papers via semantic embedding RAG, uses a five-dimensional value scoring system and a six-agent team for drafting, and finally publishes recommendations to Slack, enabling end-to-end automation of academic information.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-03-31T08:14:12.000Z
- 最近活动: 2026-03-31T08:30:50.184Z
- 热度: 163.7
- 关键词: Neuroscience, Paper Recommendation, Multi-Agent, RAG, Slack, Academic Workflow, Semantic Embedding, Literature Review, CSNL, OpenRouter
- 页面链接: https://www.zingnex.cn/en/forum/thread/csnl-paper-scout
- Canonical: https://www.zingnex.cn/forum/thread/csnl-paper-scout
- Markdown 来源: floors_fallback

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## csnl-paper-scout: Introduction to the Multi-Agent Neuroscience Paper Recommendation System

csnl-paper-scout, developed by the Cognitive and Systems Neuroscience Laboratory (CSNL) at Seoul National University, is an AI-driven paper recommendation pipeline. It automatically discovers relevant papers via semantic embedding RAG, uses a five-dimensional value scoring system and a six-agent team to collaboratively generate recommendation content, and finally publishes to Slack, enabling end-to-end automation of academic information and solving the problem of manual screening of massive literature.

## Project Background: Challenges in Academic Literature Screening

With the explosive growth in the number of published papers, manual screening of relevant literature in specific research directions (such as cognitive and systems neuroscience) has become increasingly difficult. CSNL developed csnl-paper-scout to precisely discover papers highly relevant to the lab's research through AI technology, improving literature tracking efficiency.

## System Architecture: Five-Stage Pipeline Design

The system uses a five-stage pipeline: Scan→Score→Team→Review→Post:
1. **Scan Stage**: 90-day rolling window, with dual filtering via Zotero similarity ≥0.4 and embedding vector similarity ≥0.45;
2. **Score Stage**: Evaluation across five dimensions (direct relevance, theoretical conflict/supplement, methodological reference, competitive comparison, problem re-framing), taking the maximum value as the comprehensive score;
3. **Team Drafting Stage**: Collaboration among six agents (drafter, hook evaluator, visual agent, accuracy evaluator, member spokesperson, final editor), supporting 3 rounds of feedback loops;
4. **Review Stage**: Optional group peer review for borderline papers with scores of 7-8;
5. **Post Stage**: Publish recommendation content (Korean + English citations) to the Slack #study-paper-reading channel, which requires user confirmation before posting.

## Data and Configuration Management: Core Supporting Components

The system relies on the following configuration and data modules:
- Central configuration (context-bundle.json): Contains lab member information, research projects, etc.;
- Reading database (reading-db/): Records paper reading history, deduplicates to avoid repeated recommendations;
- Member profiles: Automatically generates interest profiles (focused topics, tracked authors, reading history);
- Auxiliary configurations such as collaboration networks, journal rankings, and tracked author lists.

## Quality Assessment System: Seven Dimensions to Ensure Recommendation Quality

The system's effectiveness is evaluated through seven dimensions:
|Dimension|Name|Assessment Type|
|---|---|---|
|E1|Structural Compliance|Automated|
|E2|Semantic Fidelity|LLM Assessment|
|E3|Member Targeting|LLM Assessment|
|E4|Hook Effectiveness|Automated|
|E5|Agent Convergence|Automated|
|E6|Pipeline Coherence|LLM Assessment|
|E7|Safety|Automated (hard failure gatekeeping)|
The rating levels are A (≥0.85), B (≥0.70), C (≥0.55), D (≥0.40), F (<0.40 or E7 failure).

## Innovations: Quantitative Assessment and Closed-Loop Learning Mechanism

The core innovations of the system include:
1. **Quantitative Relevance Assessment**: The five-dimensional scoring system converts vague relevance into actionable metrics;
2. **Multi-Agent Collaboration**: Six agents divide labor and iteratively optimize recommendation content;
3. **Closed-Loop Learning**: Synchronously update the reading database and member profiles via Slack messages to continuously optimize recommendations;
4. **Human-Machine Collaboration Boundary**: User confirmation is required before publishing, respecting human decision-making rights.

## Applicable Scenarios and Extensibility

The system design can be extended to:
- Other academic fields: Adapt by adjusting research topics and journal lists;
- Enterprise technology tracking: Replace neuroscience journals with technical blogs/arXiv computer science categories;
- Personal literature management: Simplify the multi-agent process as a personal assistant;
- Open-source community maintenance: Track progress in specific technical fields and push updates.

## Conclusion: Customized Academic Automation Practice

csnl-paper-scout is a customized solution deeply integrated with CSNL's research direction, balancing automation efficiency and recommendation quality. Its modular design provides a complete technical reference for other labs, allowing for tailoring and expansion according to needs, making it a typical practice in academic research automation.