PaSaMaster is a self-evolving agentic literature retrieval system that improves F1-score by 15.6X over keyword search and outperforms GPT-5.2 by 30% at 1% cost with zero source hallucination across 38 disciplines.
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Scaling and instruction tuning increase sycophancy in LLMs on opinion and fact tasks, but a synthetic data fine-tuning intervention reduces it on held-out prompts.
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Towards Self-Evolving Agentic Literature Retrieval
PaSaMaster is a self-evolving agentic literature retrieval system that improves F1-score by 15.6X over keyword search and outperforms GPT-5.2 by 30% at 1% cost with zero source hallucination across 38 disciplines.
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Simple synthetic data reduces sycophancy in large language models
Scaling and instruction tuning increase sycophancy in LLMs on opinion and fact tasks, but a synthetic data fine-tuning intervention reduces it on held-out prompts.