Ignorance or Incompetence? Constructing Knowledge-Gated, Verifiable Tasks for LLM Agents

Title:Ignorance or Incompetence? Constructing Knowledge-Gated, Verifiable Tasks for LLM Agents

Abstract:Professional agent tasks often depend on conventions that are absent from public corpora, yet benchmarks rarely control whether an agent has access to those conventions. We introduce a knowledge-gated task-construction protocol that separates a task instruction from a compact artefact containing private conventions, reference tables, and utility operators. Construction-time provenance, byte-identical task instructions across the provided- and withheld-artefact conditions, leak audits, and executable witnesses make dependence on the artefact explicit and testable. Across fifteen calibration tasks, one frontier agent configuration achieves a 68.0% pass rate with the artefact and 0% without it; on one task, a plausible but incorrect artefact also yields 0% across five trials. Deterministic solvers and rule corpora provide exact ground truth for structured tasks, while named criterion-level rubrics support outputs that cannot be checked by a single executable oracle. A configuration-relative calibration screen retains seven tasks satisfying our five-trial empirical knowledge-gating screen. These experiments validate the behavior of the construction protocol; they do not establish that the retained tasks improve post-training. We publicly release part of the task suite and supporting tooling at this https URL.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2608.30322 [cs.AI]

(or arXiv:2608.30322v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.30322 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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