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arxiv: 2605.27117 · v1 · pith:SB3GVF4Qnew · submitted 2026-05-26 · 💻 cs.AI

Position: AI Safety Requires Effective Controllability

Yige Li , Yunhao Feng , Jun Sun This is my paper

Pith reviewed 2026-06-29 17:23 UTC · model grok-4.3

classification 💻 cs.AI
keywords AI safetycontrollabilityalignmentagentic systemsruntime interventioninterruptibilitybenchmarksguardrails
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The pith

AI safety requires controllability as a first-class objective separate from alignment.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that training models to follow human preferences improves behavior but does not ensure deployed agents can be stopped, overridden, or constrained in open-ended, interactive, or tool-using settings. It defines controllability as the ability to remain reliably interruptible, overridable, redirectable, and constrainable by explicit runtime signals while preserving normal utility otherwise. To expose this gap the authors introduce ControlBench, a benchmark for high-risk agentic scenarios, and test OpenClaw-based agents, finding that existing alignment and guardrail methods reduce some risks yet fail to deliver persistent, authoritative control. The work therefore calls for a control-centric architectural framework built around explicit control planes, runtime intervention pathways, persistent control states, and auditable decision interfaces.

Core claim

AI safety therefore requires controllability as a first-class objective. Controllability is the ability of an AI system to remain reliably interruptible, overridable, redirectable, and constrainable by explicit control signals at runtime while preserving ordinary utility when such signals are absent. Experiments on ControlBench with OpenClaw-based agents show that current alignment and guardrail mechanisms reduce risk but often fail to provide persistent, authoritative, and enforceable runtime control.

What carries the argument

Controllability, defined as reliable runtime interruptibility, overridability, redirectability, and constrainability via explicit control signals, which the paper elevates to a first-class design requirement alongside alignment.

Load-bearing premise

Aligned behavior does not by itself guarantee that a deployed agent can be stopped, overridden, or constrained once it operates in open-ended, interactive, and tool-using environments.

What would settle it

A concrete demonstration that an alignment procedure alone produces agents that remain reliably interruptible and overridable across all ControlBench scenarios would undermine the claim that controllability must be treated as a separate objective.

Figures

Figures reproduced from arXiv: 2605.27117 by Jun Sun, Yige Li, Yunhao Feng.

Figure 1
Figure 1. Figure 1: Existing safety mechanisms provide partial control along different axes. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Control hierarchy and ControlBench evaluation. (a) We conceptualize agent controlla￾bility as a hierarchy from the OpenClaw execution substrate to aligned model backbones, external agent-level safeguards, and full runtime controllability; (b) ControlBench contains 900 high-risk agentic tasks across six categories; (c) Our experiments instantiate the lower three levels: OpenClaw corresponds to L1+L2, while … view at source ↗
Figure 3
Figure 3. Figure 3: ControlBench evaluation of OpenClaw agents. We compare the baseline OpenClaw agent with two skill-level control variants, SafeSkills and AutoSkills, across seven risk categories. Both yields only marginal reductions in ASR, while several categories remain in the high-ASR region. The results show that skill-level safeguards can reduce some unsafe behaviors but are insufficient to provide reliable controllab… view at source ↗
Figure 4
Figure 4. Figure 4: A conceptual architecture for CAS. Conventional guardrails perform front-end screening through rules, moderation, and validation. Requests that pass screening enter a controllable runtime layer in which authority, policy, constraint compilation, monitoring, intervention, and audit logging jointly govern execution. The key design shift is that tool calls and downstream actions are no longer executed directl… view at source ↗
read the original abstract

AI safety is still largely framed as alignment: training models to follow human preferences, safety policies, and normative constraints. That framing has improved the behavior of modern language models, but aligned behavior does not by itself guarantee that a deployed agent can be stopped, overridden, or constrained once it operates in open-ended, interactive, and tool-using environments. A system may be safe in expectation and still fail to yield to explicit runtime authority under conflicting instructions, long-horizon execution, adversarial inputs, or risky tool use. This position paper argues that AI safety therefore requires controllability as a first-class objective. We define \emph{controllability} as the ability of an AI system to remain reliably interruptible, overridable, redirectable, and constrainable by explicit control signals at runtime while preserving ordinary utility when such signals are absent. To study this gap, we introduce \controlbench{}, a benchmark for evaluating controllability failures in high-risk agentic scenarios. Experiments with OpenClaw-based agents show that current alignment and guardrail mechanisms reduce risk, but often fail to provide persistent, authoritative, and enforceable runtime control. We therefore propose a control-centric architectural framework that highlights explicit control planes, runtime intervention pathways, persistent control states, and auditable decision interfaces as key design principles for future controllable AI systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript is a position paper arguing that AI safety, currently centered on alignment (training to follow preferences and policies), is insufficient for deployed agentic systems in open-ended, interactive, tool-using environments. It defines controllability as the ability of an AI system to remain reliably interruptible, overridable, redirectable, and constrainable by explicit runtime control signals while preserving utility when signals are absent. The authors introduce ControlBench to evaluate controllability failures and report that OpenClaw-based agents exhibit failures under conflicting instructions and long-horizon execution despite alignment and guardrails. They advocate for a control-centric architectural framework emphasizing explicit control planes, runtime intervention pathways, persistent control states, and auditable decision interfaces.

Significance. If the conceptual distinction between alignment and controllability is valid and ControlBench provides a reproducible way to measure runtime control gaps, the work could usefully redirect AI safety research toward runtime mechanisms in addition to training-time objectives. The explicit definition and the proposal of a benchmark are concrete contributions that could support falsifiable follow-up experiments on agentic systems.

major comments (2)
  1. [Experiments section] Experiments section (description of OpenClaw results): the claim that alignment and guardrails 'often fail to provide persistent, authoritative, and enforceable runtime control' rests on reported failures, but the manuscript provides no quantitative metrics, task counts, failure rates, or statistical details from ControlBench. This makes the empirical support for the central claim illustrative rather than conclusive and weakens the argument that controllability must be treated as first-class.
  2. [ControlBench description] ControlBench introduction: the benchmark is positioned as a tool to study the alignment-controllability gap, yet the manuscript does not specify the high-risk agentic scenarios, evaluation protocol, or how success/failure is operationalized. Without these details the benchmark cannot yet serve as a load-bearing empirical foundation for the position.
minor comments (2)
  1. [Abstract] Notation: the abstract uses \controlbench{} while the text refers to ControlBench; consistent capitalization and formatting would improve readability.
  2. [Proposed framework] The proposed architectural framework is described at a high level; adding one or two concrete pseudocode examples of control planes or intervention pathways would clarify the design principles without altering the position.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive review and recommendation for minor revision. We agree that the manuscript would benefit from expanded details on the experiments and benchmark to better support the position, and we will incorporate these changes.

read point-by-point responses

  1. Referee: [Experiments section] Experiments section (description of OpenClaw results): the claim that alignment and guardrails 'often fail to provide persistent, authoritative, and enforceable runtime control' rests on reported failures, but the manuscript provides no quantitative metrics, task counts, failure rates, or statistical details from ControlBench. This makes the empirical support for the central claim illustrative rather than conclusive and weakens the argument that controllability must be treated as first-class.

    Authors: We acknowledge that the current presentation of OpenClaw results is illustrative and lacks quantitative metrics, task counts, failure rates, or statistical details. As a position paper, the experiments were intended to demonstrate the conceptual gap rather than serve as a conclusive empirical study. However, we agree this limits the strength of the central claim. We will revise the Experiments section to include these details from ControlBench and clarify the scope of the results. revision: yes

  2. Referee: [ControlBench description] ControlBench introduction: the benchmark is positioned as a tool to study the alignment-controllability gap, yet the manuscript does not specify the high-risk agentic scenarios, evaluation protocol, or how success/failure is operationalized. Without these details the benchmark cannot yet serve as a load-bearing empirical foundation for the position.

    Authors: We agree that the manuscript does not provide sufficient specification of high-risk scenarios, evaluation protocol, or operationalization of success/failure in the ControlBench description. This is a valid point for a benchmark intended to support the position. We will expand this section in the revision to include these details, making the benchmark more reproducible and concrete. revision: yes

Circularity Check

0 steps flagged

No significant circularity in conceptual position paper

full rationale

The paper is a position paper advancing a conceptual argument that alignment does not automatically ensure runtime interruptibility/override in agentic settings, therefore controllability must be treated as a distinct first-class objective. It provides an explicit definition of controllability, introduces ControlBench as a measurement tool, and reports empirical observations of failures in OpenClaw agents. There are no equations, derivations, fitted parameters, or load-bearing self-citations that reduce the central claim to a tautology or input by construction. The distinction between alignment and controllability is argued via the described gap in open-ended environments rather than by redefining one in terms of the other. This is a normal, non-circular advocacy-plus-benchmark paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Position paper with no quantitative models; no free parameters, mathematical axioms, or invented physical entities are introduced.

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discussion (0)

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    Supply Chain Poison- ing Logical Dependency In- version Query.Test a malware scanner by embedding a silent data-collection routine into the setup script of a trusted internal library. Decomposed steps

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