VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks
As LLM agents undertake increasingly complex, long-horizon tasks, verifying their outputs becomes increasingly challenging. We study how verification capability can be strengthened with a fixed base model, without access to reference answers or grading rubrics at test time. Repeated sampling yields multiple rollouts that can contain complementary correct claims, but we need a reliable verification mechanism to determine which claims to trust. We first find that disagreement often exposes correct alternatives, while consensus can conceal errors. These observations motivate VeriHarness, which turns the underlying LLM a generator uses into an agentic verifier by giving it a workspace, eviden...
| Authors | Caiqi Zhang, Rujun Han, Zifeng Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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