Enterprise AI Analysis
PaSa: An LLM Agent for Comprehensive Academic Paper Search
This research introduces PaSa, a cutting-edge LLM agent designed to automate comprehensive and accurate academic paper searches. By mimicking human research behavior through iterative search, paper reading, and citation network navigation, PaSa significantly outperforms existing baselines, including advanced GPT-4 models, in retrieving relevant scholarly articles for complex queries.
Quantifiable Impact for Your Organization
Leverage PaSa's advanced capabilities to dramatically improve research efficiency and depth, ensuring your teams have access to the most relevant and accurate information with less manual effort.
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Recall@20 Improvement
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Recall vs. GPT-40 Baselines
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Selector F1 Score
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Recall@50 Improvement
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
LLM Agent Architecture
Reinforcement Learning
High-Quality Datasets
Performance & Ablation
Dual-Agent System: Crawler & Selector
PaSa operates with two specialized LLM agents: the Crawler and the Selector. The Crawler autonomously collects relevant papers by generating search queries, utilizing search tools, and navigating citation networks. The Selector meticulously reviews each collected paper to determine its relevance to the user's query, ensuring high precision in the final results. This dual-agent approach mimics human research strategies effectively.
Optimizing with AGILE & Session-Level PPO
PaSa is optimized within the AGILE reinforcement learning (RL) framework. A novel session-level PPO algorithm is employed to tackle challenges such as sparse rewards and long trajectories inherent in paper search. This allows for an end-to-end optimization of the agent's skills, enabling it to learn complex decision-making processes for comprehensive academic literature surveys.
AutoScholarQuery & RealScholarQuery
The research introduces two critical datasets: AutoScholarQuery, a synthetic yet high-quality dataset of 35k fine-grained academic queries for RL training, and RealScholarQuery, a benchmark of 50 real-world queries for realistic performance assessment. These datasets are crucial for training and evaluating PaSa's ability to handle diverse and complex academic information retrieval tasks effectively.
Superior Performance Across Benchmarks
PaSa-7B demonstrates significant improvements over all baselines, including Google Scholar and search-enabled GPT-40. On the RealScholarQuery dataset, it achieved a 37.78% increase in Recall@20 and 39.90% in Recall@50 compared to Google with GPT-40. Its performance also surpassed PaSa implemented with GPT-40 by 30.36% in recall, underscoring the effectiveness of the RL-trained agent.
Enterprise Process Flow: PaSa Architecture
User Query
→
Crawler (Search, Expand, Stop)
→
Paper Queue
→
Selector (Select / Drop)
37.78%
Higher Recall@20 on Real-World Queries than Google with GPT-40
| Method | Recall@20 | Key Advantages |
|---|---|---|
| PaSa-7b | 0.5798 |
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| Google with GPT-40 | 0.2020 |
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| ChatGPT (search-enabled GPT-40) | 0.2007 |
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Case Study: Real-World Academic Search with RealScholarQuery
The RealScholarQuery dataset, comprising 50 fine-grained, real-world academic queries, served as a crucial benchmark for evaluating PaSa's effectiveness in practical scenarios. On this challenging dataset, PaSa-7b demonstrated its superior ability to mimic human research behavior, outperforming all baselines by substantial margins. For instance, it achieved a 39.90% increase in Recall@50 over the best Google-based method. This validates PaSa's potential to significantly streamline literature review processes for researchers and enterprises, delivering more comprehensive and accurate results in less time.
Calculate Your Potential AI ROI
Estimate the tangible benefits of integrating advanced AI paper search agents like PaSa into your research workflow.
Your Path to Enhanced Research Capabilities
A phased approach to integrating advanced LLM agents for academic search, ensuring seamless adoption and maximum impact.
Phase 01: Strategic Assessment & Customization
Identify core research needs, evaluate current workflows, and customize PaSa's agentic behavior for optimal alignment with your organization's specific academic domains and query complexities. This includes refining reward models and search strategies.
Phase 02: Integration & Pilot Deployment
Integrate PaSa with existing internal knowledge bases and academic search APIs. Conduct pilot programs with a small team to gather feedback, fine-tune performance, and ensure smooth operation within your technical environment.
Phase 03: Scaled Rollout & Continuous Optimization
Expand deployment across relevant departments. Implement continuous learning mechanisms, leveraging new research data and user interactions to further enhance PaSa's recall, precision, and overall efficiency, staying ahead in academic discovery.
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