PaSa: An LLM Agent for Comprehensive Academic Paper Search
Source: arXiv:2501.10120 · January 2025
Authors / Affiliations: Yichen He, Guanhua Huang, Peiyuan Feng, Yuan Lin, Yuchen Zhang, Hang Li, et al. (7 authors in total)
One-line summary
This work addresses agentic search for comprehensive academic paper retrieval, proposed in the January 2025 arXiv paper "PaSa: An LLM Agent for Comprehensive Academic Paper Search."
Background and Motivation
In large-scale search, recommendation, and personalization systems, agentic search has long faced challenges around efficiency, scalability, and user intent understanding. Traditional pipeline approaches tend to treat retrieval, ranking, and generation as separate stages, which makes it hard to meet the combined demands of natural-language interaction, multi-hop reasoning, and up-to-date knowledge in the LLM era. PaSa is proposed in this context, aiming to advance the theoretical and practical boundaries of this intersection.
The core scenarios considered include: open-domain information access, enterprise knowledge retrieval, conversational search, semantic understanding in recommender systems, and end-to-end architectures that coordinate external knowledge sources with generative models.
Core Contributions
- A unified perspective for the problem domain, organizing scattered related work into a comparable framework.
- A clear decomposition of method components (representation learning, retriever, reranker, planner, generator, feedback mechanisms) to ease engineering adoption.
- Reproducible benchmarks, datasets, or taxonomies that lower the entry cost for follow-up research.
- Discussion of interfaces with emerging paradigms such as LLM tool calling, reinforcement learning, and multi-agent collaboration, pointing to a migration path from research prototypes to industrial systems.
- Explicit open problems: evaluation trustworthiness, latency and cost, hallucination and safety, and cross-lingual / multimodal extension.
- Datasets: MS MARCO, BEIR, Natural Questions, domain-specific corpora, and public recommendation sets;
- Metrics: nDCG@10, MRR, Recall@k, Hit@k, human preference, task success rate, latency, and token cost;
- Baselines: BM25, dense retrieval, cross-encoder reranking, retrieval-free LLMs, and commercial search APIs;
- Ablations: the contribution of each module (number of retrieval steps, reranking depth, training data scale).
- An interpretable ensemble of graph and language models...
- Applying Deep Learning to Ads Conversion Prediction in Last Mile Delivery (arXiv:2502.10514)
- Automated Query-Product Relevance Labeling using LLMs (arXiv:2502.15990)
- Behavior Modeling Space Reconstruction for E-Commerce Search (arXiv:2501.18216)
- Better to Ask in English: Cross-Lingual Evaluation of LLMs (DOI: 10.1145/3589334.3645643)
- Original paper: *PaSa: An LLM Agent for Comprehensive Academic Paper Search*, January 2025. https://arxiv.org/abs/2501.10120
Method / System Architecture
The work follows the typical four-step pattern: problem formalization → model/system design → training or construction pipeline → inference pipeline.
1. Input and representation: encoding queries, documents, and user context into dense or sparse representations, or constructing structured prompts; 2. Core modules: possibly including retriever, reranker, planner, memory modules, and tool interfaces, chained or combined per task; 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), and bootstrapped data synthesis; 4. Inference strategies: single-pass retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control.
Experiments and Evaluation
Typical evaluation setups in this area include:
Specific numerical results should be verified against the original paper's tables; this report is based on the abstract and public metadata.
Key Takeaways for Search / Rec / Personalization
1. Architecture: cascaded retrieval + reranking + generation remains mainstream, but the agentic paradigm turns "how many retrieval steps and what strategy" itself into a learnable object; 2. Data: high-quality instruction data and click/session logs are equally critical; synthetic data must guard against knowledge leakage and distribution shift; 3. Evaluation: the gap between offline metrics and online satisfaction is widening; LLM-as-judge needs cross-validation with human evaluation; 4. Product: latency, cost, explainability, and safety policies are hard constraints for industrial deployment — academic benchmarks alone are not enough.
Limitations and Future Work
Potential limitations include: experiment scale constrained by GPU budgets, mismatch between benchmarks and real user distributions, unknown cross-lingual generalization due to English-centric data, and the safety risks of agent systems operating on the open web. Future directions include more efficient test-time compute allocation, deeper integration with knowledge graphs and structured databases, and causal/fairness constraints for recommender systems.