PaperScout: An Autonomous Agent for Academic Paper Search with Process-Aware Sequence-Level Policy Optimization

PaperScout is an autonomous agent that treats academic paper search as a sequential decision-making process, dynamically deciding when and how to use search and expansion tools based on accumulated context. The authors identify a granularity mismatch in standard reinforcement learning for multi-turn tasks and propose Proximal Sequence Policy Optimization (PSPO), a sequence-level policy optimization method that aligns learning with agent–environment interactions. Experiments on synthetic and real-world benchmarks show that PaperScout outperforms structured retrieval and RL baselines in recall and relevance, demonstrating the effectiveness of its adaptive agentic framework and optimization strategy.

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• agents
• reinforcement-learning
• benchmarks