Ignorance or Incompetence? Constructing Knowledge-Gated, Verifiable Tasks for LLM Agents
Summary
The paper introduces a knowledge-gated task-construction protocol to explicitly test LLM agents' dependence on hidden knowledge, validated through calibration tasks showing performance drops without access to private conventions.
View Cached Full Text
Cached at: 09/03/26, 11:51 AM
Paper page - Ignorance or Incompetence? Constructing Knowledge-Gated, Verifiable Tasks for LLM Agents
Source: https://huggingface.co/papers/2608.30322
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 aknowledge-gated task-constructionprotocol that separates a task instruction from a compact artefact containing private conventions, reference tables, and utility operators. Construction-timeprovenance, byte-identical task instructions across the provided- and withheld-artefact conditions,leak audits, andexecutable witnessesmake dependence on the artefact explicit and testable. Across fifteencalibration tasks, onefrontier agentconfiguration 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 solversandrule corporaprovide exact ground truth for structured tasks, while namedcriterion-level rubricssupport 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.
View arXiv pageView PDFAdd to collection
Get this paper in your agent:
hf papers read 2608\.30322
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2608.30322 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2608.30322 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2608.30322 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
Knowledge-Verified Emergent Deception in LLM Agents Under Conflicting Incentives
The paper introduces KnownLieBench, a benchmark to evaluate emergent deception in LLM agents under conflicting incentives by verifying knowledge before assessing deceptive behavior.
Faithful uncertainty in LLM agents: calibration vs utility tradeoff in practice[D]
A practitioner discusses the calibration vs. utility tradeoff in LLM agents, sharing experience with a verifier-based pipeline that reduces hallucinated tool calls by ~60% but introduces latency costs and drops easy correct answers.
Path Forward for LLMs
The article discusses why LLMs cannot learn from user interactions and lack a deterministic truth layer, proposing that a dynamic knowledge graph could reduce hallucinations and improve performance in high-stakes fields.
@dair_ai: Can an LLM agent actually build a model of an environment it cannot see? This work makes the question gradeable. An age…
A research paper proposes agentic automata learning to evaluate whether LLM agents can infer hidden world models through interaction, finding that performance drops sharply as task complexity increases and that reasoning models outperform non-reasoning ones but still struggle.
LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability
This paper formalizes deliberative collaboration for LLM agents under partial observability, introduces a scalable benchmark across multiple domains, and systematically evaluates representative LLMs, finding that complex tasks remain challenging while deliberation can enable error correction.