Frontier AI systems are now producing responses that make users, developers, and policymakers genuinely pause and ask: could these models have conscious experiences? Yet the field still lacks rigorous, hard-to-game tools that can distinguish genuine indicators from optimized artifacts or surface-level cues. We introduce the Triangulated Consciousness Assessment Stack (TCAS), a validity-centered...
Debates about AI consciousness often proceed without grounding the concept in empirically validated models. This position paper argues that meaningful claims about AI consciousness should be licensed by (and graded by confidence in) models validated on humans. Drawing on Quine's observation sentences and pragmatic philosophy of science, we argue that all scientific observation ultimately...
Perspectival Control Identity Theory (PCIT) proposes a falsifiable, intervention-based identity program for phenomenal consciousness. The central claim is an a posteriori identity: phenomenal consciousness is identical to a specific kind of internal control variable, a Perspectival Control State (PCS)—a temporally extended, viability-weighted stream that makes an agent's competing needs...
This paper distinguishes two forms of consciousness that are often conflated in debates about artificial intelligence: pre-reflective experiential awareness and reflective self-consciousness. Drawing on phenomenology, Kant, and con-temporary philosophy of mind, it argues that pre-reflective awareness involves the minimal self-involvement characteristic of phenomenal experience, while reflective...
Theories of access consciousness predict specific architectural signatures: parallel specialized processing, synthesis into a unified representation, and global availability for reasoning and action. We present MIRROR, a cognitive architecture that implements these features in large language models and tests whether they produce the functional behaviors these theories predict. MIRROR separates...
Here, we explore points of convergence between the Human Consciousness Hypothesis (HCH) and Integrated World Modeling Theory (IWMT). HCH posits that consciousness is defined by three fundamental principles: Genesis (conscious functions constituting an early-stage learning algorithm), Coherence (maximization of representational consistency), and Second-Order Perception (synchronous...
Whether machines can be conscious depends not only on what they compute, but when they compute it. Most deployed artificial systems realise their functions via sequential or time-multiplexed updates, yet a moment of conscious experience feels unified and simultaneous. I prove that this difference matters. I augment Stack Theory with algebraic laws relating within time-window constraint...
Metacognition is an important aspect of information processing in the brain, subserving judgement and making cognition robust. In the literature, there are different views on the role metacognition plays in consciousness. The role of meta-cognition has been addressed by various studies. In relation to consciousness, some authors argue that metacognition is not necessarily essential in...
Amid growing concern about whether frontier artificial intelligence could instantiate consciousness-relevant capacities, we argue that attention self-modelling provides a uniquely tractable target for empirical research in current and future systems. Recent findings in multi-agent and control settings lend fresh support to this approach, demonstrating that attention self-modelling enables both...
Current AI systems are largely organized around prediction-error minimization and reward optimization. While these frameworks have been highly successful, they do not address how AI systems can develop the kind of integrated, self-maintaining world-model widely regarded as central to consciousness. This paper proposes autocatalytic constraint closure as a necessary organizational principle for...
This paper advances a specific thesis about the relationship between consciousness and learning: namely, that the evaluative process central to learning—computing progress toward or away from goals—is identical to conscious experience. Valence, the positive or negative quality of experience, just is goal-relative prediction error. Viewed from the outside, this process is iterative optimization;...
As machine intelligences become more sophisticated, individuals and societies will encounter myriad attributions of consciousness to machines. Some of these attributions may be correct; many will be illusory. This paper argues that computationalist functionalist theories of machine consciousness can and should seek to explain illusions of machine consciousness. A theory that accounts for...
As AI systems scale, many proposed indicators of machine consciousness track capability rather than consciousness, reducing discriminative power precisely when discrimination becomes important. This paper proposes Triadic Relational Ontology (TRO) as a constraint on test design: if machine consciousness exists, it must involve a sustained triadic organization comprising (i) a temporally...