arXiv:2607. 26159v1 Announce Type: cross Abstract: An AI benchmark result rarely reaches a consequential claim in one step.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 31273v1 Announce Type: new Abstract: AI-assisted research has entered a stage in which the central question is not only whether systems can generate hypotheses, run experiments, or produce manuscripts, but whether their scientific claims are calibrated to the evidence that supports them.
arXiv:2609.16302v1 Announce Type: cross Abstract: When a coding agent returns to existing software, it inherits evidence from earlier engineering work: tests, type checks, proofs, static analyses, an...
The paper examines how agentic AI systems intended for military command and control are tested and evaluated. It reviews 240 testing practices across eight dimensions and three lifecycle stages, uncovering eight assumptions—grouped into system specifiability, stability, composability, and supervisability—whose validity is weakened by agentic properties. Consequently, test results may meet procedural standards but do not guarantee that fielded behavior matches tested behavior, leading the authors to propose ten assurance claims and suggest that uncertainty be managed through deployment‑time monitoring and defined expiry conditions.
AI tools that help people judge online claims are usually evaluated while the tool is present. This paper asks a different question: after using such a tool, what can the user still do on their own?
arXiv:2608. 05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions.
VeriHarness is a method that enhances verification for large language model agents tackling long‑horizon tasks without needing reference answers at test time. It transforms the base LLM into an agentic verifier by providing a workspace, evidence tools, and reusable verification skills, using disagreement resolution and consensus challenge to evaluate competing claims. Across five benchmarks and two frontier models, VeriHarness outperforms baselines, achieving significant performance gains and demonstrating self‑improvement of verification skills from failure feedback.