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LLM agents can be turned into powerful verifiers by giving them a workspace and evidence tools, outperforming baselines on complex long-horizon tasks by resolving disagreements and challenging consensus.
Regularized Recursive Self-Improvement of Agent Harnesses (RRSI) is introduced, which incorporates the principles of regularizations into harness self-improvement by constraining the evolution candidate proposal and selection.
Static environments can be transformed on-the-fly to better suit agent learning, resulting in up to a 9.0-point performance boost with fewer execution steps.
FinanceHarness not only automates financial deep research but also reveals that even advanced LLMs struggle with specialized financial tasks, scoring below 40% on rigorous benchmarks.
