Researchers trained an open source AI search agent, Harness-1, that outperforms GPT-5.4 on recalling relevant information
A joint research collaboration between researchers at the University of Illinois at Urbana-Champaign (UIUC), UC Berkeley, and the open source AI-native vector database platform Chroma unveiled Harness-1, a 20-billion parameter open-source search agent built atop OpenAI's gpt-oss-20B open source model that fundamentally redesigns how AI executes complex retrieval tasks.
Harness-1 achieves a massive leap in performance, scoring 73% average on its ability to recall relevant information correctly from a curated dataset, outperforming even GPT-5.4 (70.9%) and the next, most accurate open source search agent, Tongyi DeepResearch 30B, by 11.4 percentage points. (While GPT-5.5 has also been out for more than a month, the researchers didn't test against this model as it wasn't available when they were building theirs.)
Crucially for developers, the model and its environment are available immediately under the highly permissive Apache 2.0 license and model code/weights on Hugging Face.
Harness-1 also serves as proof-of-efficacy...
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