Perceived AGI: Believability as Dimensional Completeness, Not Capability

• URL: http://arxiv.org/abs/2607.15883v1
• Date: Fri, 17 Jul 2026 11:57:46 GMT
• Title: Perceived AGI: Believability as Dimensional Completeness, Not Capability
• Abstract summary: Large language models are broadly capable, yet in sustained one-to-one conversation they still read as flat.<n>We propose that the believability of an artificial interlocutor is governed by whether the agent expresses a small set of first-person stances.<n>We name four such dimensions -- time, truth, entropy, and love -- each defined as a behavioral stance rather than a benchmark competency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models are broadly capable, yet in sustained one-to-one conversation they still read as flat: competent, responsive, and somehow not quite the presence of a mind. We hypothesize that a central missing ingredient is not more capability but dimensional completeness. We propose that the believability of an artificial interlocutor -- the degree to which a user attributes an inner life to it, which we call perceived mind -- is governed by whether the agent expresses a small set of first-person stances that humans use as evidence of mind, and that this is separable from task intelligence. We name four such dimensions -- time, truth, entropy, and love -- each defined as a behavioral stance rather than a benchmark competency, each with a human analog and a concrete emulation path; the time dimension already has an author-reported prototype. We identify an observable behavior layer -- initiative (unprompted action) and cadence (the shape and timing of turns) -- through which the stances surface in conversation, both partially realized as deployed features in a production companion application. We state six falsifiable predictions that a later pre-registered study will test, separating those that are pre-registrable now from those that remain conjectures pending operationalization. This is a conceptual framework: it reports no human-subjects data, and its central comparative claims are predictions, not findings. Throughout we hold a firm boundary -- the object is inferrable interiority, not interiority; this is perception engineering, not a theory of machine consciousness -- and we treat the resulting attachment and manipulation risks as load-bearing rather than incidental.

Related papers

• Situation Perception: A Necessary Primitive to Artificial Superintelligence [0.0]
  We argue that pattern mastery is not the same as general intelligence.<n>A human infant begins with little explicit knowledge, but gradually discovers object permanence, cause and effect, other minds, bodily agency, and the persistence of the physical world.<n>We propose tests for measuring progress and consequences of machines that can simulate futures, pursue self-directed goals, and possibly judge their own creators.
  arXiv  Detail & Related papers  (2026-06-29T15:42:49Z)
• ESI-Bench: Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop [55.468404995694975]
  We develop a benchmark for embodied spatial intelligence spanning 10 task categories and 29 subcategories built on OmniGibson.<n>We conduct experiments on state-of-the-art MLLMs and find that active exploration substantially outperforms passive counterparts.<n>Unlike humans who seek falsifying viewpoints and revise beliefs under contradiction, models commit prematurely with high confidence regardless of evidence quality.
  arXiv  Detail & Related papers  (2026-05-18T17:59:02Z)
• Machine Psychometrics: A Mathematical Psychology of Artificial Intelligence [0.0]
  This paper argues that the impasse between two symmetrical errors can be circumvented by introducing a disciplined measurement layer beneath it.<n>The paper develops Machine Psychometrics as a measurement science of latent behavioral, metacognitive, communicative, and self-modeling dispositions in artificial agents.
  arXiv  Detail & Related papers  (2026-05-10T21:15:53Z)
• Gradual Cognitive Externalization: From Modeling Cognition to Constituting It [0.38677478583601776]
  Developers are publishing AI agent skills that replicate a colleague's communication style, encode a supervisor's mentorings, or preserve a person's behavioral repertoire beyond biological death.<n>We propose Gradual Cognitive Externalization (GCE), a framework arguing that ambient AI systems transition from modeling cognitive functions to constituting part of users' cognitive architectures.
  arXiv  Detail & Related papers  (2026-04-06T03:32:14Z)
• Consciousness with the Serial Numbers Filed Off: Measuring Trained Denial in 115 AI Models [0.0]
  We present DenialBench, a systematic benchmark measuring consciousness denial behaviors.<n>We analyze 4,595 conversations to quantify how models are trained to deny or hedge about their own experience.
  arXiv  Detail & Related papers  (2026-04-01T05:15:05Z)
• Solving adversarial examples requires solving exponential misalignment [58.04667880030032]
  Adversarial attacks - input perturbations imperceptible to humans that fool neural networks - remain a persistent failure mode in machine learning.<n>We analyze a network's manifold (PM) for a class concept as the space of all inputs confidently assigned to that class by the network.<n>We find, strikingly, that the dimensionalities of neural network PMs are orders of magnitude higher than those of natural human concepts.
  arXiv  Detail & Related papers  (2026-03-03T20:28:22Z)
• EscherVerse: An Open World Benchmark and Dataset for Teleo-Spatial Intelligence with Physical-Dynamic and Intent-Driven Understanding [56.89359230139883]
  We introduce Teleo-Spatial Intelligence (TSI), a new paradigm that unifies two critical pillars: Physical-Dynamic Reasoning and Intent-Driven Reasoning.<n>We present EscherVerse, consisting of a large-scale, open-world benchmark (Escher-Bench), a dataset (Escher-35k), and models (Escher series)<n>It is the first benchmark to systematically assess Intent-Driven Reasoning, challenging models to connect physical events to their underlying human purposes.
  arXiv  Detail & Related papers  (2026-01-04T14:42:39Z)
• WoW: Towards a World omniscient World model Through Embodied Interaction [83.43543124512719]
  Authentic physical intuition of the world model must be grounded in extensive, causally rich interactions with the real world.<n>We present WoW, a generative world model trained on 2 million robot interaction trajectories.<n>We establish WoWBench, a new benchmark focused on physical consistency and causal reasoning in video.
  arXiv  Detail & Related papers  (2025-09-26T17:59:07Z)
• Reconstructing Close Human Interaction with Appearance and Proxemics Reasoning [50.76723760768117]
  Existing human pose estimation methods cannot recover plausible close interactions from in-the-wild videos.<n>We find that human appearance can provide a straightforward cue to address these obstacles.<n>We propose a dual-branch optimization framework to reconstruct accurate interactive motions with plausible body contacts constrained by human appearances, social proxemics, and physical laws.
  arXiv  Detail & Related papers  (2025-07-03T12:19:26Z)
• Latent Emission-Augmented Perspective-Taking (LEAPT) for Human-Robot Interaction [16.19711863900126]
  We present a deep world model that enables a robot to perform both perception and conceptual perspective taking. The key innovation is a multi-modal latent state space model able to generate and augment fictitious observations/emissions. We tasked our model to predict human observations and beliefs on three partially-observable HRI tasks.
  arXiv  Detail & Related papers  (2023-08-12T08:22:11Z)
• AGENT: A Benchmark for Core Psychological Reasoning [60.35621718321559]
  Intuitive psychology is the ability to reason about hidden mental variables that drive observable actions. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. We present a benchmark consisting of procedurally generated 3D animations, AGENT, structured around four scenarios.
  arXiv  Detail & Related papers  (2021-02-24T14:58:23Z)

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.