Patrick Jiang's Harness-1 externalizes memory for a 20B search agent
The paper reports 0.730 average curated recall across eight retrieval benchmarks, with code and model weights now public.
By Ryan Merket · Published
Primary source: X
Why it matters
Harness-1 points to a live question in agent design: whether better long-horizon behavior comes from larger models alone, or from training smaller models to use explicit memory, verification and search interfaces.
Patrick Jiang (@patpcj) introduced Harness-1, a 20B-parameter search agent that moves search state outside the model and into a structured harness, according to a 13-post thread on X and the accompanying paper. The paper lists authors from the University of Illinois at Urbana-Champaign, UC Berkeley and Chroma, and describes Harness-1 as a retrieval subagent trained with reinforcement learning inside a stateful search harness.…
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