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

• URL: http://arxiv.org/abs/2608.30322v1
• Date: Mon, 31 Aug 2026 06:39:27 GMT
• Title: Ignorance or Incompetence? Constructing Knowledge-Gated, Verifiable Tasks for LLM Agents
• Abstract summary: We introduce a knowledge-gated task-construction protocol that separates a task instruction from a compact artefact.<n>One frontier agent configuration achieves a 68.0% pass rate with the artefact and 0% without it.<n>We do not establish that the retained tasks improve post-training.
• Score: 16.70932968825709
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://github.com/DatagridsAI/Knowledge-Gated-Task-Construction.

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