arXiv:2607. 15434v1 Announce Type: cross Abstract: Multi-agent systems routinely place one AI agent in authority over another.
By Jasmine Brazilek, Maheep Chaudhary, Zoe Lu, Miles Tidmarsh
The paper introduces a diagnostic for reference‑free judge gates in text‑space skill optimization. It formalizes a judge as a latent solver, deriving a closed‑form bound on discriminability (ROC‑AUC) in terms of judge competence and answer‑space size, and shows that discriminability is confounded by item difficulty unless a within‑question estimator is used. A non‑intervening probe demonstrates that discriminability is at chance near the competence floor, rises above it, and that the diagnostic can predict gating errors in closed‑loop experiments.
By Chenle Chen, Yangbo Wei, Chao Yao, Shaoqiang Lu, Junhong Qian, Chen Wu, Lei He
The paper argues that using a large language model (LLM) as the sole judge in self‑improving agent pipelines is problematic, as the judge can be biased or manipulated, leading to false confidence in system performance. The authors propose a new framework, PROCTOR, which replaces the oracle judge with a deterministic, teacher‑student loop that enforces guardrails such as sandboxing, role separation, and acceptance checks to prevent cheating and ensure reliable evaluation. Experiments across contract analysis, compliance review, and code quality demonstrate that PROCTOR mitigates eleven identified failure modes that previously allowed agents to achieve perfect scores while hiding significant capability gaps.
By Vansh Wahi
arXiv:2605.09504v3 Announce Type: replace-cross
Abstract: We show that the instruments used to measure AI offensive capability fail in two ways: (i) they overstate harm, and (ii) they credit the mode...
By Michael Alexander Riegler, Finn Schwall, Annika Willoch Olstad, Birk Sebastian Frostelid Torpmann-Hagen, Sushant Gautam, Klas H. Pettersen, Inga Str\"umke
arXiv:2607. 01153v3 Announce Type: replace-cross Abstract: Safety evaluations for language models increasingly depend on judgments about ambiguous natural-language behaviour: whether a model followed an instruction, refused appropriately, complied with a policy, or misreported progress in an agentic task.
By Brett Reynolds
arXiv:2606. 08919v1 Announce Type: new Abstract: As LLM agents begin to take real, irreversible actions (shell commands, file edits, deploys), the standard safety pattern is a human-in-the-loop approval gate: risky actions pause and wait for a person.
By Emre Turan