- Who it helps
- Researchers and developers deciding when an agent action needs review.
- Availability
- The October 2 preprint abstract is public. Code availability and a deployable integration were not established in the inspected evidence.
What the authors report
The method combines signals about individual arguments with a judgment of the complete call. The abstract reports better error discrimination than the acting model’s stated confidence, and explores routing uncertain calls to review or feeding the score back into the agent.
Decide what a gate must accomplish
A gate that rejects more actions can appear more accurate while completing less work. For an evaluation, record accepted-action accuracy together with the share of actions accepted and the cost of review. Keep a labelled test set of silent errors, where the tool returns successfully but the requested outcome is wrong.
What you can do
- Read the full experimental setup before adapting the approach. Define acceptable error and review rates on a held-out task set, including errors that produce no tool exception. Open reference ↗
Where the evidence stops
This is an abstract-level research brief. The reported gains were not reproduced and do not establish an enforceable permission boundary.
Sources read. Sources checked October 19, 2026 UTC. No external product workflow, model inference or third-party project was executed.
Sources & further reading
Paper · paper
Proxy Confidence paper abstract ↗
Read the abstract and submission date. Full experimental procedures, artifacts and reported results were not independently audited.
Yikai Zhao and coauthors · page checked 2026-10-19 · Published 2026-10-02
Coverage note: The cited primary material was read on October 19. A matched original X post, a watched video comparison and independent execution remain unavailable.
The record over time (1)
Dated source checks and published explanations. An observation date can differ from the original release date.
- · Added to Current
Proxy Confidence studies whether a separate open-weight model can identify risky agent actions before a tool runs. It scores the proposed call using the same context and tool schema.