Researchers trained an open source AI search agent, Harness-1, that outperforms GPT-5.4 on recalling relevant information
VentureBeat / 6/9/2026
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Key Points
- Researchers from UIUC, UC Berkeley, and Chroma unveiled Harness-1, a 20B-parameter open-source AI search agent built on OpenAI’s gpt-oss-20B that rethinks how complex retrieval tasks are executed.
- Harness-1 reportedly scores 73% average on correctly recalling relevant information from a curated dataset, outperforming GPT-5.4 (70.9%) and the next-best open-source search agent Tongyi DeepResearch 30B by 11.4 percentage points.
- The team evaluated the model across eight complex, real-research-style retrieval benchmarks spanning open web search, SEC filings, USPTO patents, and multi-hop question answering.
- For developers, Harness-1 (model environment and code/weights) is available immediately under an Apache 2.0 license via Hugging Face, enabling rapid adoption and experimentation.
- Harness-1 also demonstrates the practical impact of Thinking Machines’ distributed, web-based training and fine-tuning API Tinker, which was used to train and run inference for the agent.
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