Foundations of a Time-Consistent Counterfactual Actuarial Runtime for Autonomous AI Agents

• URL: http://arxiv.org/abs/2605.26508v1
• Date: Tue, 26 May 2026 03:47:58 GMT
• Title: Foundations of a Time-Consistent Counterfactual Actuarial Runtime for Autonomous AI Agents
• Abstract summary: We propose a runtime actuarial layer for autonomous AI agents.<n>Every side-effect-bearing action carries a time-consistent, counterfactual risk toll against a contractually fixed safe default.<n>The paper states the primitive contract, the toll identity, the within-boundary no-arbitrage result, and the budget guarantee on which those later layers depend.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a foundational runtime actuarial layer for autonomous AI agents in which every side-effect-bearing action carries a time-consistent, counterfactual risk toll computed against a contractually fixed safe default, inside an explicit underwriting boundary. The framework treats per-action insurance as the primary unit of analysis and replaces post-hoc annual liability cover with a pre-action transaction layer. The paper establishes four structural results: (i) a well-defined counterfactual toll under a chosen safe-default mapping and continuation policy, with explicit non-uniqueness; (ii) a no-splitting property within an underwriting boundary that telescopes path-decomposed actions into a boundary potential, with a corollary tying gaming-resistance to boundary design; (iii) an irreversible-authority premium, split into a strictly positive action-level component and an if-and-only-if characterisation of the set-level robust capital increase; and (iv) a conservative runtime gating theorem that translates high-probability toll envelopes into an executed-action budget guarantee. The result is the mathematical base layer for a broader program: an empirical companion instantiates the runtime through an Actuarial Action Interface and authority-frontier experiments; a mechanism-design companion studies strategic operator incentives and cross-boundary aggregation; and a dynamic-underwriting companion studies experience rating and audit-replay calibration. The present paper states the primitive contract, the toll identity, the within-boundary no-arbitrage result, and the budget guarantee on which those later layers depend.

Related papers

• 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)
• Counterfactual Shapley Credit Assignment [49.39649767581182]
  Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents.<n>We introduce Counterfactual Shapley Credit Assignment, a novel framework that attributes credit and blame via the Counterfactual Shapley ($$-value)<n>We derive a consistent estimator that computes $$$-values, enabling a new class of policy methods, $$-PPO, combined with Prioritized Trajectory Replay (PTR)
  arXiv  Detail & Related papers  (2026-07-18T23:28:20Z)
• Gaming-Resistant Insurance Contracts for Autonomous AI Agents: Strategy-Proof Toll Mechanism Design [0.0]
  Paper A defines a time-consistent actuarial runtime that prices each side-effect against a contractually fixed safe default and gates execution against a reserve budget.<n>We characterise a five-attack space for autonomous AI-agent insurance contracts and prove when the actuarial runtime is gaming-resistant.<n>We then compose these clauses with Paper A's runtime guarantees to obtain joint incentive compatibility over the five-attack space.
  arXiv  Detail & Related papers  (2026-06-15T07:31:21Z)
• TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning [52.67756371231985]
  Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.<n>We introduce Tree Rollout Allocation for Contrastive Exploration (TRACE), a unified rollout allocation framework.<n>Technically, TRACE allocates rollout budget to both prompt roots and intermediate prefixes that are most likely to yield mixed terminal rewards.
  arXiv  Detail & Related papers  (2026-06-09T17:16:03Z)
• Provably Secure Agent Guardrail [89.79561918065122]
  Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
  arXiv  Detail & Related papers  (2026-05-28T02:12:41Z)
• Insuring Every Action: An Authority Frontier Framework for Runtime Actuarial Control of Autonomous AI Agents [0.0]
  We propose the Actuarial Action Interface (AAI), a deterministic runtime contract that prices each such action against a contractually fixed safe default.<n>We then develop the Authority Frontier, an evaluation primitive measuring how much autonomous authority the runtime releases at each level of reserve capital.<n>The contribution is a benchmark-ready evaluation framework for runtime actuarial control of autonomous-agent side effects.
  arXiv  Detail & Related papers  (2026-05-25T09:31:48Z)
• AgenticAITA: A Proof-Of-Concept About Deliberative Multi-Agent Reasoning for Autonomous Trading Systems [0.0]
  AGENTICAITA is an agentic AI framework that replaces the traditional signal then execute paradigm with a fully autonomous loop.<n>The framework demonstrates operational correctness of the deliberative pipeline, achieving 157 zero-intervention invocations across 76 assets.
  arXiv  Detail & Related papers  (2026-05-01T16:25:43Z)
• Bridging Values and Behavior: A Hierarchical Framework for Proactive Embodied Agents [26.613956143957548]
  textitValuePlanner is a hierarchical cognitive architecture that decouples high-level value scheduling from low-level action execution.<n>Our work offers a structured approach to bridging intrinsic values and grounded behavior for autonomous agents.
  arXiv  Detail & Related papers  (2026-04-30T10:43:55Z)
• Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [59.536125286960186]
  Multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing.<n>We find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry.<n>ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus.
  arXiv  Detail & Related papers  (2026-04-21T15:05:58Z)
• Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions [51.56484100374058]
  Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes.<n>This gap, driven by mutually cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector.<n>This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure.
  arXiv  Detail & Related papers  (2025-11-10T22:24:21Z)
• Co-Investigator AI: The Rise of Agentic AI for Smarter, Trustworthy AML Compliance Narratives [2.7295959384567356]
  Co-Investigator AI is an agentic framework optimized to produce Suspicious Activity Reports (SARs) significantly faster and with greater accuracy than traditional methods.<n>We demonstrate its ability to streamline SAR drafting, align narratives with regulatory expectations, and enable compliance teams to focus on higher-order analytical work.
  arXiv  Detail & Related papers  (2025-09-10T08:16:04Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)
• IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound [85.6899802468343]
  We present IBP-R, a novel verified training algorithm that is both simple effective. We also present UPB, a novel robustness based on $beta$-CROWN, that reduces the cost state-of-the-art branching algorithms.
  arXiv  Detail & Related papers  (2022-06-29T17:13:25Z)

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.