Ignorance or Incompetence? Constructing Knowledge-Gated, Verifiable Tasks for LLM Agents
Abstract
A protocol separates task instructions from private convention artefacts to explicitly test agent dependence on hidden knowledge, validated by calibration tasks showing near-zero performance without access.
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.
Community
We introduce a knowledge-gated task-construction protocol that separates agent failures caused by missing domain knowledge from failures caused by inadequate capability. The protocol uses private artefacts, leak audits, and verifiable ground truth to make knowledge dependence testable.
When I debug a failing agent in prod, the first question is always: did it not know, or did it not do? This protocol finally gives a way to answer that instead of guessing. Knowledge-gated tasks with verifiable outcomes are the right design because they separate the two failure modes cleanly. The practical payoff is real — if most failures are ignorance, you fix retrieval or context, not the model. If they're incompetence, you change tooling or prompting. I'd want to see this run against a real deployed agent with actual logs, but it's the first framing that matches how I actually debug.
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This paper presents a clear and well-motivated framework for constructing knowledge-gated, verifiable tasks for LLM agents. Its key strength is the explicit separation of knowledge limitations from general task-solving capabilities, which addresses an important but often overlooked issue in current agent benchmarks. The proposed artifact-based evaluation and provided-versus-withheld setup are intuitive and experimentally meaningful. However, the current evaluation is relatively small in scale, and the extent to which the proposed framework generalizes to diverse real-world agent tasks remains unclear. Overall, the paper offers a useful conceptual and methodological contribution to more interpretable and diagnostic agent evaluation.
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