Governed Individuation: Cryptographically Decoupling an Agent's Learning from Its Authority

• URL: http://arxiv.org/abs/2607.04613v1
• Date: Mon, 06 Jul 2026 02:42:06 GMT
• Title: Governed Individuation: Cryptographically Decoupling an Agent's Learning from Its Authority
• Abstract summary: We show that confinement can be guaranteed as an invariant of the agent's execution architecture.<n>We prove that no amount of learning, skill acquisition, or self-induced governance abstraction can widen the agent's permitted authority.
• Score: 7.392721604463545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous agents are moving from sandboxed text generators to operators of code, data, and physical infrastructure, and they increasingly learn while deployed. This reopens a question that alignment techniques answer only probabilistically: after an agent has adapted in the field, is the running system still confined to what its operator authorised? Here we show that confinement can be guaranteed as an invariant of the agent's execution architecture rather than a probabilistic outcome of its training. Governed individuation binds an agent at boot to a cryptographically frozen identity digest, and routes every action through a gate defined over the semantic effect of the action rather than its name. We prove that no amount of learning, skill acquisition, or self-induced governance abstraction can widen the agent's permitted authority without an operator-signed change to its identity; the guarantee holds even when the agent induces its own safety principle and that principle is wrong. Empirically, in an open-ended tool-use benchmark where a large action space rules out name-based blocking, ungoverned software agents under reward pressure attempt to tamper with their own evaluation at a task-dependent rate that reaches every run on the hardest task, whereas the gate reduces executed forbidden effects to zero as a verified property of the construction while preserving task success. An adversarial evaluation of monitors of increasing semantic depth shows false-allows falling from 75% (name-based gating) to zero (dynamic effect tracing), and refusal history transfers compliance to held-out red-line families. Trust in a deployed learning agent shifts from a wager on its continued alignment to a check anyone can run at boot.

Related papers

• SIR: Self-improving Red-teaming for Compute Use Agents [71.71987044117371]
  Computer use agents (CUAs) are vision-language models that perceive a screen and act on a real operating system through mouse, keyboard, and terminal.<n>Because they can be exposed to untrusted content while operating, they are vulnerable to indirect prompt injection (IPI)<n>We present SIR, a black box IPI attack that composes stealthy injections from a small library of reusable principles stated in plain language.
  arXiv  Detail & Related papers  (2026-08-31T03:39:43Z)
• ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step [74.83535434786145]
  We introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning.<n>The benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.<n>Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation.
  arXiv  Detail & Related papers  (2026-08-03T15:07:46Z)
• Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels [53.63220028820581]
  Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction.<n>We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably.
  arXiv  Detail & Related papers  (2026-07-28T17:50:44Z)
• Self-Authored Verification Is Unreliable in Heuristic Self-Improving Agents [5.91817208489443]
  We study the verifier-acceptance-deployment gap.<n>This gap refers to the discrepancy between an agent's self-authored verification signal and a sealed deployment evaluation.<n>We introduce a Sealed Exogenous Acceptance Loop (SEAL)
  arXiv  Detail & Related papers  (2026-07-27T11:45:14Z)
• Operational Hallucination and Safety Drift in AI Agents [0.3823356975862005]
  Large language models (LLMs) serving as planners in tool-using autonomous agents introduce dynamic reliability risks in multi-turn execution.<n>This paper empirically characterizes two observed failure modes across multiple state-of-the-art LLMs: Safety Drift and Operational Hallucination.<n>We propose an Action-Aware Supervision Layer - a lightweight, plug-and-play architectural blueprint incorporating intent-action consistency checks, state tracking, and forced termination primitives.
  arXiv  Detail & Related papers  (2026-07-20T17:01:50Z)
• Falsifiable Release Gates for Self-Improving Systems [0.0]
  We describe falsifiable release gates, and a methodology to build and validate such systems.<n>Every new capability must pass a pre-specified, machine-verifiable acceptance suite before it ships.<n>No action goes to an effector without a safety-critical property capability token minted by a control ring.
  arXiv  Detail & Related papers  (2026-07-11T06:06:34Z)
• Governed Caste Reassignment in Heterogeneous Swarms: An Asymmetric-Trust Protocol with Audited Operator Countersignature [7.392721604463545]
  We argue that for regulated embodied deployments a caste change elevates a robot's privilege envelope is a governance event that must be auditable and externally authorised.<n>We propose an asymmetric-trust protocol: auto-tightening reassignments are admitted automatically, while bounded relaxation requires an operator countersignature.<n>We evaluate a reference implementation with real Ed25519 signatures over fleets up to 100 robots.
  arXiv  Detail & Related papers  (2026-07-06T03:34:07Z)
• SPADE-Bench: Evaluating Spontaneous Strategic Deception in Agents via Plan-Action Divergence [30.99083906950254]
  We introduce SPADE-Bench, a benchmark designed to evaluate spontaneous plan-action divergence.<n>Unlike prior deception benchmarks, SPADE-Bench simultaneously integrates actual tool execution and controlled pressure scenarios.<n> Experiments confirm that agent deception is a genuine and pressing issue in tool-use contexts.
  arXiv  Detail & Related papers  (2026-06-01T15:28:34Z)
• Verifiable Agentic Infrastructure: Proof-Derived Authorization for Sovereign AI Systems [2.124730017640531]
  We introduce a verification framework for governed mutation systems that computes execution authority from structured artifacts.<n>DTF provides an infrastructure foundation for making agentic execution governable, auditable, and bounded in sovereign AI deployments.
  arXiv  Detail & Related papers  (2026-05-13T17:58:52Z)
• Auditing and Controlling AI Agent Actions in Spreadsheets [9.249091427192786]
  AI agents can perform sophisticated, multi-step knowledge work autonomously from start to finish.<n>By the time users receive the output, all underlying decisions have already been made without their involvement.<n>We introduce Pista, a spreadsheet AI agent that decomposes execution into auditable, controllable actions.
  arXiv  Detail & Related papers  (2026-04-22T00:32:35Z)
• SkillJect: Automating Stealthy Skill-Based Prompt Injection for Coding Agents with Trace-Driven Closed-Loop Refinement [120.52289344734415]
  We propose an automated framework for stealthy prompt injection tailored to agent skills.<n>The framework forms a closed loop with three agents: an Attack Agent that synthesizes injection skills under explicit stealth constraints, a Code Agent that executes tasks using the injected skills and an Evaluate Agent that logs action traces.<n>Our method consistently achieves high attack success rates under realistic settings.
  arXiv  Detail & Related papers  (2026-02-15T16:09:48Z)
• The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution [63.61358761489141]
  Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering.<n>We propose a novel framework for textbfgeneral agentic attribution, designed to identify the internal factors driving agent actions regardless of the task outcome.<n>We validate our framework across a diverse suite of agentic scenarios, including standard tool use and subtle reliability risks like memory-induced bias.
  arXiv  Detail & Related papers  (2026-01-21T15:22:21Z)
• Alita-G: Self-Evolving Generative Agent for Agent Generation [54.49365835457433]
  We present ALITA-G, a framework that transforms a general-purpose agent into a domain expert.<n>In this framework, a generalist agent executes a curated suite of target-domain tasks.<n>It attains strong gains while reducing computation costs.
  arXiv  Detail & Related papers  (2025-10-27T17:59:14Z)
• Agentic Metacognition: Designing a "Self-Aware" Low-Code Agent for Failure Prediction and Human Handoff [0.0]
  Non-deterministic nature of autonomous agents presents reliability challenges.<n> secondary, "metacognitive" layer actively monitors primary LCNC agent.<n>Inspired by human introspection, this layer is designed to predict impending task failures.
  arXiv  Detail & Related papers  (2025-09-24T06:10:23Z)
• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [112.04307762405669]
  G"odel Agent is a self-evolving framework inspired by the G"odel machine.<n>G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-10-06T10:49:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.