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Search-o1
🔍 Search-o1: Agentic Search-Enhanced Large Reasoning Models [EMNLP 2025]
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Search-o1 is a Python toolchain from RUC NLPIR that adds agentic search enhancement to large reasoning models, published at EMNLP 2025. It combines retrieval-augmented generation with reasoning models like QwQ and R1-style models.
You want to pair retrieval with step-by-step reasoning models instead of a plain LLM pipeline.
Use it to
- Enhance reasoning models with agentic search
- Run retrieval-augmented generation over reasoning traces
- Evaluate on math and QA benchmarks like GPQA, AMC, AIMO
- Test code reasoning with LiveCode tasks
For Researchers and engineers working on RAG with reasoning models
- Role
- rag
- Language
- Python
- Licence
- MIT
- Forks
- 110
- Open issues
- 2
- Last push
- 2025-11-17
topicsragreasoningsearchpythonbenchmarksllm