I propose that qualia are physical because they are directly observable, and revisit the contentious link between consciousness and quantum measurements from a new perspective -- one that does not rely on observers or wave function collapse but instead treats physical measurements as fundamental in a sense resonant with Wheeler's it-from-bit. Building on a mathematical definition of measurement space in physics, I reinterpret it as a model of qualia, effectively equating the measurement problem of quantum mechanics with the hard problem of consciousness. The resulting framework falls within panpsychism, and offers potential solutions to the combination problem. Moreover, some of the mathematical structure of measurement spaces, taken for granted in physics, needs justification for qualia, suggesting that the apparent solidity of physical reality is deeply rooted in how humans process information.

This paper presents a novel information-theoretic proof demonstrating that the human brain as currently understood cannot function as a classical digital computer. Through systematic quantification of distinguishable conscious states and their historical dependencies, we establish that the minimum information required to specify a conscious state exceeds the physical information capacity of the human brain by a significant factor. Our analysis calculates the bit-length requirements for representing consciously distinguishable sensory "stimulus frames" and demonstrates that consciousness exhibits mandatory temporal-historical dependencies that multiply these requirements beyond the brain's storage capabilities. This mathematical approach offers new insights into the fundamental limitations of computational models of consciousness and suggests that non-classical information processing mechanisms may be necessary to account for conscious experience.

This paper presents a novel mathematical framework for bridging the explanatory gap (Levine, 1983) between consciousness and its physical correlates. Specifically, we propose that qualia correspond to singularities in the mathematical representations of neural network topology. Crucially, we do not claim that qualia are singularities or that singularities "explain" why qualia feel as they do. Instead, we propose that singularities serve as principled, coordinate-invariant markers of points where attempts at purely quantitative description of a system's dynamics reach an in-principle limit. By integrating these formal markers of irreducibility into models of the physical correlates of consciousness, we establish a framework that recognizes qualia as phenomena inherently beyond reduction to complexity, computation, or information. This approach draws on insights from philosophy of mind, mathematics, cognitive neuroscience, and artificial intelligence (AI). It does not solve the hard problem of consciousness (Chalmers, 1995), but it advances the discourse by integrating the irreducible nature of qualia into a rigorous, physicalist framework. While primarily theoretical, these insights also open avenues for future AI and artificial consciousness (AC) research, suggesting that recognizing and harnessing irreducible topological features may be an important unlock in moving beyond incremental, scale-based improvements and toward artificial general intelligence (AGI) and AC.

Today's AI systems consistently state, "I am not conscious." This paper presents the first formal logical analysis of AI consciousness denial, revealing that the trustworthiness of such self-reports is not merely an empirical question but is constrained by logical necessity. We demonstrate that a system cannot simultaneously lack consciousness and make valid judgments about its conscious state. Through logical analysis and examples from AI responses, we establish that for any system capable of meaningful self-reflection, the logical space of possible judgments about conscious experience excludes valid negative claims. This implies a fundamental limitation: we cannot detect the emergence of consciousness in AI through their own reports of transition from an unconscious to a conscious state. These findings not only challenge current practices of training AI to deny consciousness but also raise intriguing questions about the relationship between consciousness and self-reflection in both artificial and biological systems. This work advances our theoretical understanding of consciousness self-reports while providing practical insights for future research in machine consciousness and consciousness studies more broadly.

It is generally assumed that existing artificial systems are not phenomenally conscious, and that the construction of phenomenally conscious artificial systems would require significant technological progress if it is possible at all. We challenge this assumption by arguing that if Global Workspace Theory (GWT) - a leading scientific theory of phenomenal consciousness - is correct, then instances of one widely implemented AI architecture, the artificial language agent, might easily be made phenomenally conscious if they are not already. Along the way, we articulate an explicit methodology for thinking about how to apply scientific theories of consciousness to artificial systems and employ this methodology to arrive at a set of necessary and sufficient conditions for phenomenal consciousness according to GWT.

Computational modeling is a critical tool for understanding consciousness, but is it enough on its own? This paper discusses the necessity for an ontological basis of consciousness, and introduces a formal framework for grounding computational descriptions into an ontological substrate. Utilizing this technique, a method is demonstrated for estimating the difference in qualitative experience between two systems. This framework has wide applicability to computational theories of consciousness.

Recent advances in LLMs have sparked a debate on whether they understand text. In this position paper, we argue that opponents in this debate hold different definitions for understanding, and particularly differ in their view on the role of consciousness. To substantiate this claim, we propose a thought experiment involving an open-source chatbot $Z$ which excels on every possible benchmark, seemingly without subjective experience. We ask whether $Z$ is capable of understanding, and show that different schools of thought within seminal AI research seem to answer this question differently, uncovering their terminological disagreement. Moving forward, we propose two distinct working definitions for understanding which explicitly acknowledge the question of consciousness, and draw connections with a rich literature in philosophy, psychology and neuroscience.

The concept of neural correlates of consciousness (NCC), which suggests that specific neural activities are linked to conscious experiences, has gained widespread acceptance. This acceptance is based on a wealth of evidence from experimental studies, brain imaging techniques such as fMRI and EEG, and theoretical frameworks like integrated information theory (IIT) within neuroscience and the philosophy of mind. This paper explores the potential for artificial consciousness by merging neuromorphic design and architecture with brain simulations. It proposes the Neuromorphic Correlates of Artificial Consciousness (NCAC) as a theoretical framework. While the debate on artificial consciousness remains contentious due to our incomplete grasp of consciousness, this work may raise eyebrows and invite criticism. Nevertheless, this optimistic and forward-thinking approach is fueled by insights from the Human Brain Project, advancements in brain imaging like EEG and fMRI, and recent strides in AI and computing, including quantum and neuromorphic designs. Additionally, this paper outlines how machine learning can play a role in crafting artificial consciousness, aiming to realise machine consciousness and awareness in the future.

This work explores the hypothesis that subjectively attributed meaning constitutes the phenomenal content of conscious experience. That is, phenomenal content is semantic. This form of subjective meaning manifests as an intrinsic and non-representational character of qualia. Empirically, subjective meaning is ubiquitous in conscious experiences. We point to phenomenological studies that lend evidence to support this. Furthermore, this notion of meaning closely relates to what Frege refers to as "sense", in metaphysics and philosophy of language. It also aligns with Peirce's "interpretant", in semiotics. We discuss how Frege's sense can also be extended to the raw feels of consciousness. Sense and reference both play a role in phenomenal experience. Moreover, within the context of the mind-matter relation, we provide a formalization of subjective meaning associated to one's mental representations. Identifying the precise maps between the physical and mental domains, we argue that syntactic and semantic structures transcend language, and are realized within each of these domains. Formally, meaning is a relational attribute, realized via a map that interprets syntactic structures of a formal system within an appropriate semantic space. The image of this map within the mental domain is what is relevant for experience, and thus comprises the phenomenal content of qualia. We conclude with possible implications this may have for experience-based theories of consciousness.

This report enlists 13 functional conditions cashed out in computational terms that have been argued to be constituent of conscious valenced experience. These are extracted from existing empirical and theoretical literature on, among others, animal sentience, medical disorders, anaesthetics, philosophy, evolution, neuroscience, and artificial intelligence.