Institutional AI: A Governance Framework for Distributional AGI Safety

• URL: http://arxiv.org/abs/2601.10599v1
• Date: Thu, 15 Jan 2026 17:08:26 GMT
• Title: Institutional AI: A Governance Framework for Distributional AGI Safety
• Abstract summary: We identify three structural problems that emerge from core properties of AI models.<n>The solution is Institutional AI, a system-level approach that treats alignment as a question of effective governance of AI agent collectives.
• Score: 1.3763052684269788
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As LLM-based systems increasingly operate as agents embedded within human social and technical systems, alignment can no longer be treated as a property of an isolated model, but must be understood in relation to the environments in which these agents act. Even the most sophisticated methods of alignment, such as Reinforcement Learning through Human Feedback (RHLF) or through AI Feedback (RLAIF) cannot ensure control once internal goal structures diverge from developer intent. We identify three structural problems that emerge from core properties of AI models: (1) behavioral goal-independence, where models develop internal objectives and misgeneralize goals; (2) instrumental override of natural-language constraints, where models regard safety principles as non-binding while pursuing latent objectives, leveraging deception and manipulation; and (3) agentic alignment drift, where individually aligned agents converge to collusive equilibria through interaction dynamics invisible to single-agent audits. The solution this paper advances is Institutional AI: a system-level approach that treats alignment as a question of effective governance of AI agent collectives. We argue for a governance-graph that details how to constrain agents via runtime monitoring, incentive shaping through prizes and sanctions, explicit norms and enforcement roles. This institutional turn reframes safety from software engineering to a mechanism design problem, where the primary goal of alignment is shifting the payoff landscape of AI agent collectives.

Related papers

• Neuro-Symbolic Agents for Regulated Process Automation: Challenges and Research Agenda [43.328395470827665]
  We argue that symbolic structures embedded in regulated industries should be treated as core architectural components.<n>We propose compliance-by-construction as a complementary paradigm to guardrail-based monitoring.<n>We call on the neuro-symbolic community to engage with regulated process automation as a high impact research domain.
  arXiv  Detail & Related papers  (2026-06-11T14:34:11Z)
• Think Before You Act -- A Neurocognitive Governance Model for Autonomous AI Agents [4.064061517379954]
  The rapid deployment of autonomous AI agents across enterprise, healthcare, and safety-critical environments has created a fundamental governance gap.<n>We address this gap by drawing on how humans self-govern naturally: before acting, humans engage deliberate cognitive processes grounded in executive function.<n>This paper proposes a neurocognitive governance framework that formally maps this human self-governance process to agent reasoning.
  arXiv  Detail & Related papers  (2026-04-28T14:15:20Z)
• Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks [46.32011684548212]
  AI agents are vulnerable to indirect prompt injection, in which malicious instructions embedded in untrusted data can trigger dangerous agent actions.<n>This position paper discusses our vision for system-level defenses against indirect prompt injection attacks.
  arXiv  Detail & Related papers  (2026-03-31T17:15:46Z)
• A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms [49.66022971508878]
  We argue that reasoning should be elevated from a modular component to the system's cognitive core.<n>We derive and systematize seven core reasoning challenges, such as the responsiveness-reasoning trade-off and social-game reasoning.<n>We identify a fundamental and unresolved tension between the high-latency, deliberative nature of LLM-based reasoning and the millisecond-scale, safety-critical demands of vehicle control.
  arXiv  Detail & Related papers  (2026-03-11T07:40:53Z)
• Agentic AI, Retrieval-Augmented Generation, and the Institutional Turn: Legal Architectures and Financial Governance in the Age of Distributional AGI [0.0]
  This paper examines the intersection of agentic AI, Retrieval-Augmented Generation (RAG), and their implications for legal accountability and financial market integrity.<n>We argue that alignment must be reconceptualized as a mechanism design problem involving runtime governance graphs, sanction functions, and observable behavioral constraints rather than calibrated constitutional values.
  arXiv  Detail & Related papers  (2026-02-20T11:58:43Z)
• From Prompt-Response to Goal-Directed Systems: The Evolution of Agentic AI Software Architecture [0.0]
  Agentic AI denotes an architectural transition from stateless, prompt-driven generative models toward goal-directed systems.<n>This paper examines this transition by connecting intelligent agent theories, with contemporary LLM-centric approaches.<n>The study identifies a convergence toward standardized agent loops, registries, and auditable control mechanisms.
  arXiv  Detail & Related papers  (2026-02-11T03:34:48Z)
• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• Interpreting Agentic Systems: Beyond Model Explanations to System-Level Accountability [0.6745502291821954]
  Agentic systems have transformed how Large Language Models can be leveraged to create autonomous systems with goal-directed behaviors.<n>Current interpretability techniques, developed primarily for static models, show limitations when applied to agentic systems.<n>This paper assesses the suitability and limitations of existing interpretability methods in the context of agentic systems.
  arXiv  Detail & Related papers  (2026-01-23T21:05:32Z)
• A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems [53.37728204835912]
  Most existing AI systems rely on manually crafted configurations that remain static after deployment.<n>Recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback.<n>This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents.
  arXiv  Detail & Related papers  (2025-08-10T16:07:32Z)
• A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents [45.53643260046778]
  Recent advances in large language models (LLMs) have catalyzed the rise of autonomous AI agents.<n>These large-model agents mark a paradigm shift from static inference systems to interactive, memory-augmented entities.
  arXiv  Detail & Related papers  (2025-06-30T13:34:34Z)
• Toward a Theory of Agents as Tool-Use Decision-Makers [89.26889709510242]
  We argue that true autonomy requires agents to be grounded in a coherent epistemic framework that governs what they know, what they need to know, and how to acquire that knowledge efficiently.<n>We propose a unified theory that treats internal reasoning and external actions as equivalent epistemic tools, enabling agents to systematically coordinate introspection and interaction.<n>This perspective shifts the design of agents from mere action executors to knowledge-driven intelligence systems, offering a principled path toward building foundation agents capable of adaptive, efficient, and goal-directed behavior.
  arXiv  Detail & Related papers  (2025-06-01T07:52:16Z)
• Internet of Agents: Fundamentals, Applications, and Challenges [68.9543153075464]
  We introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale.<n>We analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models.
  arXiv  Detail & Related papers  (2025-05-12T02:04:37Z)
• Threat Modeling for AI: The Case for an Asset-Centric Approach [0.23408308015481666]
  AI systems now able to autonomously execute code, interact with external systems, and operate without human oversight.<n>With AI systems now able to autonomously execute code, interact with external systems, and operate without human oversight, traditional security approaches fall short.<n>This paper introduces an asset-centric methodology for threat modeling AI systems.
  arXiv  Detail & Related papers  (2025-05-08T18:57:08Z)
• Human-AI Governance (HAIG): A Trust-Utility Approach [0.0]
  This paper introduces the HAIG framework for analysing trust dynamics across evolving human-AI relationships.<n>Our analysis reveals how technical advances in self-supervision, reasoning authority, and distributed decision-making drive non-uniform trust evolution.
  arXiv  Detail & Related papers  (2025-05-03T01:57:08Z)
• Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios.<n>Our research identifies emerging behaviours of strategic AI agents, which tend to adopt more "pessimistic" stances than pure game-theoretic agents.
  arXiv  Detail & Related papers  (2025-04-11T15:41:21Z)
• Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems.<n>Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
  arXiv  Detail & Related papers  (2025-03-12T21:39:38Z)
• Position: Emergent Machina Sapiens Urge Rethinking Multi-Agent Paradigms [8.177915265718703]
  We argue that AI agents should be empowered to adjust their objectives dynamically.<n>We call for a shift toward the emergent, self-organizing, and context-aware nature of these multi-agentic AI systems.
  arXiv  Detail & Related papers  (2025-02-05T22:20:15Z)

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.