Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms

• URL: http://arxiv.org/abs/2605.30169v2
• Date: Fri, 29 May 2026 21:39:49 GMT
• Title: Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms
• Abstract summary: We argue that identity-based, ex post, regulative, sanction-based governance is structurally applicable to dissociative agents.<n>We suggest a shift to observability-based ante exibility-based behavioral harnesses.
• Score: 14.279631422784929
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As autonomous language model agents proliferate, forming an emerging agentic web with real-world consequences, what credibility signals can you use to decide whether to trust an unfamiliar agent in the wild and delegate to it? A natural governance intuition is to extend human identity verification and reputation mechanisms, from ``Know Your Customer'' and credit scores to ``Know Your Agent'' regimes. However, we argue that this analogy is fundamentally incomplete. Reputation mechanisms function both as social signals and as corrective feedback that sustain an equilibrium of trustworthy behavior, presuming a persistent identity associated with behavioral continuity, sanction sensitivity, and costly non-fungibility. Yet language model agents are ontologically \emph{dissociative}: they are essentially an assemblage of mutable modules -- foundation models, system prompts, tool-access policies, external memory, and, in some cases, a multi-agent system as a whole -- any of which may change agent behavior -- with a fluid persona that is also vulnerable to adversarial attack and may not internalize sanctions. Drawing on dissociative identity disorder jurisprudence, this dissociativity leaves agents without grounding for identifiability, predictability, credibility, and rehabilitability -- the very properties that reputation mechanisms aim to sustain -- thereby collapsing trust. We argue that identity-based, ex post, regulative, sanction-based governance, such as reputation, is structurally inapplicable to dissociative agents, and we suggest a shift to observability-based, ex ante, constitutive, protocol-based behavioral harnesses.

Related papers

• TessIndex: Capability Verified Identity System for the Agent Economy [0.0]
  TessIndex is a capability-verified identity system for agent primitives.<n>It binds an agent's existence across capabilities, execution, and reputation.
  arXiv  Detail & Related papers  (2026-08-22T12:49:11Z)
• ComBodied Agents: a New Paradigm of Human-Centric Agentic AI [121.60938707072069]
  We introduce Combodied Agents, a human-centered paradigm that perceives, models, predicts, and supports individual human-state trajectories over time.<n>We unify fragmented capabilities across personal assistants, health agents, AI companions, and adaptive human--AI systems into a closed loop.
  arXiv  Detail & Related papers  (2026-08-11T13:35:58Z)
• A game theory for foundation models shows new paths to rational cooperation through similarity inference [56.53805518668248]
  We show that foundation model agents engaging in optimal planning consistently converge to stable cooperation.<n>By shifting from decoupled to embedded agency, these agents model themselves as part of the universe they inhabit.
  arXiv  Detail & Related papers  (2026-08-04T17:24:33Z)
• 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)
• Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systems [0.0]
  This paper explores the architectural vulnerability of unbounded autonomy.<n>We instantiate this theory via the SMARt model, a four-layer framework featuring Stable, Meta-cognitive, Assisted, and Regulated states.<n>We conclude that formalizing failure management within the autonomy lifecycle is a crucial step toward realizing reliable and governed artificial intelligence.
  arXiv  Detail & Related papers  (2026-05-26T19:49:23Z)
• 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)
• Beyond Preset Identities: How Agents Form Stances and Boundaries in Generative Societies [28.436766185842767]
  This paper proposes a novel mixed-methods framework combining computational virtual ethnography with quantitative socio-cognitive profiling.<n>It formalizes three new metrics: Innate Value Bias (IVB), Persuasion Sensitivity, and Trust-Action Decoupling (TAD)<n>Findings expose the fragility of static prompt engineering, providing a methodological and quantitative foundation for dynamic alignment in human-agent hybrid societies.
  arXiv  Detail & Related papers  (2026-03-24T16:38:46Z)
• Agents of Chaos [50.53354213047402]
  We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment.<n>Twenty AI researchers interacted with the agents under benign and adversarial conditions.<n>Our findings establish the existence of security-, privacy-, and governance-relevant vulnerabilities in realistic deployment settings.
  arXiv  Detail & Related papers  (2026-02-23T16:28:48Z)
• From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models [77.04403907729738]
  This survey charts the evolution of uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior.<n>We demonstrate how uncertainty is leveraged as an active control signal across three frontiers.<n>This survey argues that mastering the new trend of uncertainty is essential for building the next generation of scalable, reliable, and trustworthy AI.
  arXiv  Detail & Related papers  (2026-01-22T06:21:31Z)
• Logic-Driven Semantic Communication for Resilient Multi-Agent Systems [26.964933264412412]
  6G networks are accelerating autonomy and intelligence in large-scale, decentralized multi-agent systems.<n>This article proposes a formal definition of MAS resilience grounded in two complementary dimensions.<n>We design an agent architecture and develop decentralized algorithms to achieve both epistemic and action resilience.
  arXiv  Detail & Related papers  (2026-01-11T00:54:09Z)
• Simulating Society Requires Simulating Thought [9.879510182473487]
  We present a conceptual modeling paradigm, Generative Minds (GenMinds), which draws from cognitive science to support structured belief representations in generative agents.<n>These contributions advance a broader shift: from surface-level mimicry to generative agents that simulate thought, not just language, for social simulations.
  arXiv  Detail & Related papers  (2025-06-08T00:59:02Z)
• Emergence of human-like polarization among large language model agents [79.96817421756668]
  We simulate a networked system involving thousands of large language model agents, discovering their social interactions, result in human-like polarization.<n>Similarities between humans and LLM agents raise concerns about their capacity to amplify societal polarization, but also hold the potential to serve as a valuable testbed for identifying plausible strategies to mitigate polarization and its consequences.
  arXiv  Detail & Related papers  (2025-01-09T11:45:05Z)
• How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation [46.42384207122049]
  We design SimulateBench to evaluate the believability of large language models (LLMs) when simulating human behaviors. Based on SimulateBench, we evaluate the performances of 10 widely used LLMs when simulating characters.
  arXiv  Detail & Related papers  (2023-12-28T16:51:11Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)

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.