Open Deep Search

Researchers from Sentient, UW, Princeton, and UC Berkeley introduce Open Deep Search (ODS), an open-source search AI framework that rivals top proprietary systems like GPT-4o Search Preview and Perplexity Sonar. Key insights: ● Two open components: search + reasoning – ODS has two modular parts: (1) Open Search Tool, which retrieves and refines high-quality web results using query rephrasing, snippet reranking, and site-specific logic; and (2) Open Reasoning Agent, a controller that orchestrates tool usage (search, calculator, etc.) to answer queries. Two variants are offered: ODS-v1 (ReAct) and ODS-v2 (CodeAct). ● SOTA open-source performance – With DeepSeek-R1 as the base LLM, ODS-v2 scores 88.3% on SimpleQA and 75.3% on FRAMES, beating GPT-4o Search Preview by +9.7% on the latter. ODS adapts the number of searches per query (avg. 3.39 on FRAMES), balancing cost and accuracy more efficiently than fixed-query baselines. ● Better than Perplexity Sonar – On both FRAMES and SimpleQA, ODS+DeepSeek-R1 outperforms Perplexity’s flagship search models, even in complex reasoning tasks involving multi-hop questions, time/date calculations, and name disambiguation. ● Code-based agents enhance reasoning – ODS-v2 builds on CodeAct, allowing it to write and run Python code to perform symbolic reasoning and tool calls. This results in sharper numerical precision and task flexibility compared to CoT-based ReAct in ODS-v1.