Recently researching @SentientAGI's Open Deep Search, after all, it is an open-source alternative to Perplexity and SearchGPT. 🎯 Performance Data The ODS + DeepSeek-R1 combination achieved a 75.3% accuracy rate in the FRAMES benchmark test, directly surpassing OpenAI GPT-4o Search Preview's 65.6%, leading by nearly 10 percentage points. In the SimpleQA test, it reached 88.3%, almost matching GPT-4o Search Preview's 90.0%. For the first time, an open-source solution has surpassed closed-source competitors in complex reasoning searches. ⚡️ Architectural Innovation Traditional open-source search tools simply hand over raw search results to LLMs for processing. ODS redesigns the entire process: 1️⃣ Intelligent Query Reconstruction — Understanding users' implicit intentions and automatically optimizing search terms. 2️⃣ Advanced Content Chunking and Reordering — Filtering out low-relevance content to ensure LLMs receive high-quality context. 3️⃣ Customized Website Processing — Optimizing crawls for major information sources like Wikipedia, ArXiv, and PubMed. 4️⃣ Dynamic Search Decision Making — Intelligently determining whether multiple searches are needed based on query complexity. 💡 Reasoning Engine ODS-v1 is based on the Chain-of-Thought and ReAct frameworks, enhancing reasoning capabilities through a think/action/observe loop. ODS-v2 is based on the Chain-of-Code and CodeAct frameworks, generating executable Python code for precise reasoning. Both versions can adaptively adjust search strategies based on query complexity, rather than using the same number of searches each time like fixed solutions. ————————————————————————— Developers are no longer forced to rely on black-box algorithms and can customize search logic according to their needs. Startups gain search AI capabilities on par with tech giants. @SentientAGI ODS provides a plug-and-play framework, allowing seamless integration of new open-source reasoning models after their release, ensuring that open-source search AI always maintains a competitive edge.