Harness-G: Graph-Structured Retrieval Framework Cuts Retrieval Aliasing, Beats Graph-R1 by 10.7 F1
Yanning Hou · hf · 2026-07-31
Harness-G is a graph-structured retrieval framework for RL search agents. It addresses retrieval aliasing (retrieval-equivalence collapse) by reformulating query generation as finite action selection: policy picks evidence sentence/entity or answer, environment builds menu and validates. This reduces aliasing and enables comparable alternatives. Structured Non-myopic Credit (SNC) uses a frozen scorer to assign downstream gains. On six QA benchmarks, Harness-G achieves highest average F1, outperforming Graph-R1 by 10.74 points at 1.5B and 3.98 at 3B.
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