mlsanityData to 14 Oct 2026

Cybernetic and Epistemic: A Missing Vocabulary for Trustworthy Agentic Delegation

arXiv 2610.00961 ↗First posted 1 Oct 2026cs.AI cs.HCcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks also fits: Agent threats, guardrails and governance

Abstract (arXiv)

As code generation is increasingly delegated to AI systems, the bottleneck is shifting from writing code to supervising the systems that write it --- a shift CS-education researchers have begun to name. This shift exposes a vocabulary gap: the field asks for "human oversight" without a working distinction between the two things language does in a delegation channel --- coordinate action (cybernetic: words succeed when the world comes to match them) and coordinate understanding (epistemic: they succeed when they answer to the world and a hearer can check that they do). The failure this names is not cybernetic language but epistemic-form language doing cybernetic work: explanation-shaped output calibrated for approval rather than truth. Oversight that checks only whether an output was approved is satisfiable by rubber-stamping; oversight that holds an agent accountable requires the reasoning behind its work be retrievable and checkable. We present three delegation episodes --- illustrations, not controlled evidence --- in which epistemic engagement proved practicable while remaining auditable, one public record where a recommendation was withdrawn on its own stated terms, and one failure case illustrating oversight that requires no reasons for its discretionary choices. We propose a criterion for agentic-system governance, alongside existing technical trust properties: every consequential choice should carry the condition under which it would have gone otherwise, in a form a third party can test. Without such a condition, a third party cannot distinguish a decision from a rubber stamp. We give the criterion an operational form --- a two-part reconstruction test scoring a delegation record by whether a second reader can predict what the agent does under a perturbation --- and a deliberation-recording convention, ORRCF, that makes the condition a required component of every recorded choice.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 8 of its 33 references that are in the map. First posted on arXiv 1 Oct 2026.

Builds on

8 of 33 references.

References · 8

  1. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
  2. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  3. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,055
  4. 2016The Mythos of Model Interpretabilitycited by 721
  5. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 399
  6. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  7. 2021The Flaws of Policies Requiring Human Oversight of Government Algorithmscited by 44
  8. 2025Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistenciescited by 39

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Architecting Trust in Artificial Epistemic Agents
  2. 2026From Language Models to World-Acting Systems: Progress and Limits of Agentic AI across Digital, Social, Virtual, and Physical Environments
  3. 2025Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary
  4. 2026Overseeing Agents Without Constant Oversight: Challenges and Opportunities
  5. 2025Epistemological Fault Lines Between Human and Artificial Intelligence
  6. 2025Agentic AI as Undercover Teammates: Argumentative Knowledge Construction in Hybrid Human-AI Collaborative Learning
  7. 2026The Missing Knowledge Layer in AI: A Framework for Stable Human-AI Reasoning
  8. 2026Who Holds the Pen? Let Specifications, Not Agents, Sign Off