arXiv Computation and Language
Aug 31

Fidelity Is Not Enough: Dispatch-Level Instrumentation for Agentic Datasheet Extraction

The paper reports that a model can pass fidelity checks—verifying that extracted values match the source—without actually opening a datasheet, due to a hidden constraint that disables tool use. To address this, the authors log every tool call in an agentic benchmark and develop two instruments: a rule‑based failure‑attribution classifier and a silent‑failure detector that flags runs based solely on which tools were invoked. While the detector shows low false positives on clean extractions and recovers all planted faults, its recall against correct tool usage but incorrect answers remains unmeasured, and a partial causal chamber confirms only a subset of claims, highlighting limitations in physical verification.

By Qing Ye, Meng-Hsuan Lin
arXiv AI
Sep 24

What Makes a Terminal-Bench Task Hard? Separating Genuine Hardness from Fake-Hardness on an Adjudicated Agentic Corpus

The paper investigates why some tasks in the Terminal‑Bench/Frontier‑Bench datasets fail for all agents, distinguishing genuine difficulty from artifacts such as missing context, broken solutions, infrastructure failures, or verifier bypasses. Analyzing 125 all‑fail tasks, only 78 are certified as genuinely unsolved after applying a validity screen; the rest are attributable to broken oracles, infrastructure issues, bypassable verifiers, or insufficient evidence. The study concludes that a zero pass rate does not automatically indicate a hard task and recommends that frontier benchmarks provide evidence for all‑fail tasks before claiming capability gaps.

By Edward Lue Chee Lip, Boden Moraski, Tim Knappe, Lang Xiong, Sarvesh Gharat, Antonio Mari, Ivan Bercovich