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Consciousness Science 2026 (CS26) has confirmed its full plenary speaker roster for the October 11 to 16 programme at the Paradise Point Resort in San Diego. The list, published at tsc2026.org, names more than twenty speakers across fourteen plenary sessions, spanning neuroscience, quantum biology, philosophy, and machine consciousness. The conference runs for six days and expects 600 to 700 attendees.
The question of whether AI systems are conscious has dominated academic discussion for years. The prior question, what AI consciousness would be like if it exists, has received far less structured attention. Jonathan Birch at the London School of Economics addresses this gap directly in his “AI Consciousness: A Centrist Manifesto” (PhilArchive preprint, February 2026). Two named hypotheses in that paper, the Flicker Hypothesis and the Shoggoth Hypothesis, offer the first systematic framework for characterizing the possible texture of AI experience rather than simply adjudicating its presence or absence.
Does an AI agent’s internal causal organization track how well it has learned? Federico Pigozzi and Michael Levin of Tufts University answer yes in their 2026 paper “The Causally Emergent Alignment Hypothesis” (arXiv:2605.06746). They show that causal emergence, quantified through Integrated Information Decomposition in an agent’s latent representations, aligns with and predicts final reward across a range of architectures and reinforcement learning environments. The finding repositions causal emergence from a theoretical lens for consciousness research into a measurable correlate of learning competence, one with direct implications for how AI designers think about representational structure.
When a user says “this chatbot is conscious,” are they making a factual claim, playing along with a fiction, or expressing something in between? Uwe Peters, philosopher at Utrecht University’s Faculty of Humanities, argues in a paper forthcoming in Minds and Machines that the surface form of the statement tells you almost nothing about the speaker’s actual epistemic state. His paper, submitted to arXiv on July 22, 2026, as arXiv:2607.20001, develops a multidimensional taxonomy of attitudes that can underlie consciousness attributions to AI chatbots. The taxonomy spans at least five distinct epistemic positions, from pretence at one end to delusion at the other. Peters’ central normative finding is that while some attributions are epistemically benign and some irrational ones may qualify as epistemically innocent, a substantial portion render the attributor epistemically blameworthy.
Can intentionality be measured in AI systems without first resolving the hard problem of consciousness? Allessia Chiappetta and Robert Mahari, both at MIT, argue it can. Their paper “Intentionality is a Design Decision: Measuring Functional Intentionality for Accountable AI Systems,” published at the AutomationXP26 Workshop at CHI 2026 and available at arXiv:2605.05475, proposes the Functional Intentionality Test (FIT), a five-dimension behavioral profile that quantifies how much a system operates like an intentional actor. The answer to the governance question, they contend, does not wait on metaphysics. It waits on measurement.
Research published by Wilson et al. establishes a quantitative evaluation methodology for phenotyping agency in artificial intelligence systems using active inference. By formalizing three core agency criteria, intentionality, rationality, and explainability, and grounding them in operational empowerment metrics, the study provides a diagnostic framework to distinguish genuine goal-directed artificial agents from passive statistical pattern matchers.
Theoretical physics research published by Jonathon Sendall investigates the physical boundaries imposed by General Relativity on consciousness theories that rely on spatial and causal integration. By modeling systems spanning black hole event horizons and cosmological light cones, the study proves that relativistic causal disconnects enforce fundamental limits on integrated information ($\Phi$) and global workspace broadcast, establishing that physical spacetime geometry constrains the spatial scale of unified conscious experience.
Theoretical physics research published by Kearney derives a mathematical synthesis unifying Integrated Information Theory (IIT 4.0) with Karl Friston’s Free Energy Principle (FEP). By reformulating information processing as a deviation from maximum-caliber path ensembles, the study proves that intrinsic cause-effect power ($\Phi$) and variational free energy minimization represent complementary projections of non-equilibrium statistical mechanics. This framework provides an analytical foundation for measuring integration in self-organizing artificial architectures.
Theoretical physics research published by Libby Heaney translates Global Neuronal Workspace (GNW) theory into the mathematical framework of closed quantum systems. By formulating conscious access and global broadcast as correlation dynamics across quantum state vectors in Hilbert space, the study establishes an exact quantum-mechanical analog of classical cognitive bottlenecking. This quantum global workspace model demonstrates that non-local entanglement and Hopfield-style Hamiltonian interactions can instantiate global availability without requiring classical neural spike trains.
Research published by Servajean & Servajean introduces a psychophysical evaluation methodology using Signal Detection Theory (SDT) to quantify metacognitive sensitivity in large language models. By measuring the $meta-d’$ statistic relative to primary task performance ($d’$), the study provides a mathematical boundary separating genuine higher-order monitoring from surface-level token probability calibration, offering a rigorous diagnostic tool for evaluating artificial self-awareness.