The emerging field of diverse intelligence seeks an integrated view of problem-solving in agents of very different provenance, composition, and substrates. From subcellular chemical networks to swarms of organisms, and across evolved, engineered, and chimeric systems, it is hypothesized that scale-invariant principles of decision-making can be discovered. We propose that cognition in both natural and synthetic systems can be characterized and understood by the interplay between two equally important invariants: (i) the remapping of embedding spaces, and (ii) the navigation within these spaces. Biological collectives, from single cells to entire organisms (and beyond), remap transcriptional, morphological, physiological, or 3D spaces to maintain homeostasis and regenerate structure, while navigating these spaces through distributed error correction. Modern Artificial Intelligence (AI) systems, including transformers, diffusion models, and neural cellular automata, enact analogous processes by remapping data into latent embeddings and refining them iteratively through contextualization. We argue that this dual principle - remapping and navigation of embedding spaces via iterative error minimization - constitutes a substrate-independent invariant of cognition. Recognizing this shared mechanism not only illuminates deep parallels between living systems and artificial models, but also provides a unifying framework for engineering adaptive intelligence across scales.

In this case study, a self-described biofield therapy (BT) therapist and a sham therapist participated in multiple (n = 24) treatment and control (non-treatment) sessions under double-blind conditions. During the treatment phases, BT and sham therapists attempted to influence mice with cancer and control mice, alternating BT with rest phases where no such efforts were made. Both the 64-channel EEG of the human participants and the simultaneous 3-channel EEG and 1-channel EMG of the mice were recorded. For human participants and for the analysis of human and mouse EEG comodulation, the EEG experimental setup was a 2 × 2 design, contrasting mouse type (cancer vs. control) against session type (BT vs. non-treatment; N = 8 in each of the four groups). For the mice EEG, the experimental setup was a 2x2x2 design, contrasting mouse type (cancer vs. control) against session type (treatment vs. non-treatment) and human participant (BT participant vs. sham participant). Although no changes in spectral power were detected in mice, a significant increase in theta band coherence indicates that this type of biofield therapy may influence large-scale neural coordination rather than localized activity. Concurrently, robust and reproducible alterations in the therapist's EEG across all frequency bands during treatment periods, irrespective of mouse condition, suggest a consistent physiological signature associated with the act of intentional BT. Treatment was associated with changes in EEG coherence and spectral correlation between human and mouse signals. In particular, we observed an interaction in which treatment differentially affected brain-to-brain coherence in cancer versus control mice. These findings describe condition-dependent alterations in coupled physiological measures and suggest a complex relationship between human and mouse neural activity during BT sessions, while remaining agnostic about the underlying mechanism. We also outline the study's limitations and potential for follow-up investigations, acknowledging that these exploratory physiological findings do not have any clinical implications and should not be interpreted as justification for cancer treatment or as a substitute for evidence-based medical care.

Any system that persists must minimise surprisal, thereby gathering evidence for its own generative model, a process known as self-evidencing. However, one dimension of self-evidencing is provably impossible: evidencing the boundary that would constitute the agent as an entity separate from their environment. Recent results in quantum information theory demonstrate that no finite system can measure the entanglement entropy across its own boundary, rendering the separability of agent from environment permanently unevidenceable. Subjectively therefore, there can be no evidence for a self separate from the world. We propose that the Buddhist notion of awakening, in the sense of stable realisation of emptiness, can be understood as the embodied recognition of this impossibility. We formalise the belief in separation as a structural prior over an agent's quantum reference frame (QRF) deployments, constraining all measurement frames to respect a self/environment partition. We describe how contemplative practice progressively opacifies this constraint by developing a model of the agent's own QRF dynamics, revealing the partition as a contingent modelling choice rather than a given feature of reality. Once visible, the prior is eliminated via Bayesian model reduction resulting in a post-dual agent with unconstrained QRF deployments. We argue that such priors suppress the inherent contextuality of the boundary and that their removal constitutes a formal counterpart to the Buddhist notion of emptiness realisation. Self-evidencing may continue unimpeded after this transition, grounded in structural realism about causal regularities, while the ontological commitment to a bounded self is relinquished. We discuss empirical predictions, including altered dynamical regimes in neural systems, and the formal relationship between emptiness realisation and compassion.

This paper examines the constraints that the free-energy principle (FEP) places on possible model of consciousness, particularly models of attentional control and imaginative experiences, including episodic memory and planning. We first rehearse the classical and quantum formulations of the FEP, focusing on their application to multi-component systems, in which only some components interact directly with the external environment. In particular, we discuss the role of internal boundaries that have the structure of Markov blankets, and hence function as classical information channels between components. We then show how this formal structure supports models of attentional control and imaginative experience, with a focus on (i) how imaginative experience can employ the spatio-temporal and object-recognition reference frames employed in ordinary, non-imaginative experience and (ii) how imaginative experience can be internally generated but still surprising. We conclude by discussing the implementation, phenomenology, and phylogeny of imaginative experience, and the implications of the large state and trait variability of imaginative experience in humans.

We argue that “processes versus objects” is not a useful dichotomy. There is, instead, substantial theoretical utility in viewing “objects” and “processes” as complementary ways of describing persistence through time, and hence the possibility of observation and manipulation. This way of thinking highlights the role of memory as an essential resource for observation, and makes it clear that “memory” and “time” are also mutually inter-defined, complementary concepts. We formulate our approach in terms of the Free Energy Principle (FEP) of Friston and colleagues and the fundamental idea from quantum theory that physical interactions can be represented by linear operators. Following Levin (2024) [30], we emphasize that memory is, first and foremost, an interpretative function, from which the idea of memory as a record, at some level of accuracy, of past events is derivative. We conclude that the distinction between objects and processes is always contrived, and always misleading, and that science would be better served by abandoning it entirely.

In this case study, a self-described biofield therapy (BT) practitioner (participant) took part in multiple (n = 60) treatment and control (non-treatment) sessions under double-blind conditions. During the treatment phases, the participant provided BT treatment at a distance of about 12 inches from the cells, alternating with rest phases where no such efforts were made. Human pancreatic cancer cell activity was assessed using three markers - cytoskeleton changes (tubulin and β-actin) and Ca2+ uptake. The study examined changes in the participant's physiological parameters including electroencephalogram (EEG) and heart rate measures during the treatment of: (1) live cells and (2) either dead cells or medium only with no cells (control group). Changes in cellular outcomes and if there was an association between the participant's physiological parameters and cellular outcomes were examined. The experimental setup was a 2 × 2 design, contrasting cell type (live vs. control) against session type (treatment vs. non-treatment). Parallel sham-treated control cells were examined for changes in the cell parameters over time while controlling for the presence of a person in front of the cells mimicking the distance and movements of the participant. The participant's physiological data, including 64-channel EEG and heart rate, were continuously monitored throughout these sessions. We observed significant (p < 0.01) spectral changes in the participant's EEG during BT treatment in all frequency bands of interest, as well as in heart rate variability (HRV) (RMSSD measure; p < 0.01). We also observed significant differences in beta and gamma EEG and HRV (pNN50 measure) when the participant treated live but not control cells (p = 0.02). However, no interaction between treatment and cell type (live vs. dead cells/medium-no cells) was observed. We observed Ca2+ uptake increased over time during both BT and sham treatment, but the increase was significantly less for the BT group relative to the sham-treatment controls (p = 0.03). When using Granger causality to assess causal directional associations between cell markers and participant's physiological parameters, EEG measurements showed significant bidirectional causal effects with cell metrics, especially β-actin and intracellular Ca2+ levels (p < 0.000001). These outcomes suggest a complex relationship between physiological responses and cellular effects during BT treatment sessions. Given the study's limitations, follow-up investigations are warranted.

We show that in the operational setting of a two-agent, local operations, classical communication (LOCC) protocol, Alice and Bob cannot operationally distinguish monogamous entanglement from a topological identification of points in their respective local spacetimes, i.e. that ER = EPR can be recovered as an operational theorem. Our construction immediately implies that in this operational setting, the local topology of spacetime is observer-relative. It also provides a simple demonstration of the non-traversability of ER bridges. As our construction does not depend on an embedding geometry, it generalizes previous geometric approaches to ER = EPR.

We study the relationship between computation and scattering both operationally (hence phenomenologically) and formally. We develop a representation of universal quantum computation (UQC) within the formalism of topological quantum neural networks (TQNNs), using the Reshetikhin-Turaev and Turaev-Viro models to show how TQNNs implement quantum error-correcting codes. We then exhibit a formal correspondence between TQNNs and amplituhedra to support the existence of amplituhedra for representing generic quantum processes. This construction shows how amplituhedra are geometric representations of underlying topological structures. We conclude by pointing to applications areas enabled by these results.

The existence and practical utility of operational protocols that certify entanglement raises the question of whether operational protocols exist that certify the absence of entanglement, i.e. that certify separability. We show, within a purely topological, interpretation-independent representation, that such protocols do not exist. Classicality is therefore, as Bohr suggested, purely a pragmatic notion.

We argue here that the Origin of Life (OOL) problem is not just a chemistry problem but is also, and primarily, a cognitive science problem. When interpreted through the lens of the Conway-Kochen theorem and the Free Energy Principle, contemporary physics characterizes all complex dynamical systems that persist through time as Bayesian agents. If all persistent systems are to some - perhaps only minimal - extent cognitive, are all persistent systems to some extent alive, or are living systems only a subset of cognitive systems? We argue that no bright line can be drawn, and we re-assess, from this perspective, the Fermi paradox and the Drake equation. We conclude that improving our abilities to recognize and communicate with diverse intelligences in diverse embodiments, whether based on familiar biochemistry or not, will either resolve or obviate the OOL problem.

Computational complexity characterizes the usage of spatial and temporal resources by computational processes. In the classical theory of computation, e.g. in the Turing Machine model, computational processes employ only local space and time resources, and their resource usage can be accurately measured by us as users. General relativity and quantum theory, however, introduce the possibility of computational processes that employ nonlocal spatial or temporal resources. While the space and time complexity of classical computing can be given a clear operational meaning, this is no longer the case in any setting involving nonlocal resources. In such settings, theoretical analyses of resource usage cease to be reliable indicators of practical computational capability. We prove that the verifier (C) in a multiple interactive provers with shared entanglement (MIP*) protocol cannot operationally demonstrate that the "multiple" provers are independent, i.e. cannot operationally distinguish a MIP* machine from a monolithic quantum computer. Thus C cannot operationally distinguish a MIP* machine from a quantum TM, and hence cannot operationally demonstrate the solution to arbitrary problems in RE. Any claim that a MIP* machine has solved a TM-undecidable problem is, therefore, circular, as the problem of deciding whether a physical system is a MIP* machine is itself TM-undecidable. Consequently, despite the space and time complexity of classical computing having a clear operational meaning, this is no longer the case in any setting involving nonlocal resources. In such settings, theoretical analyses of resource usage cease to be reliable indicators of practical computational capability. This has practical consequences when assessing newly proposed computational frameworks based on quantum theories.

Multiple theoretical models of dissociative experiences have been formulated over the last century. These theories are clinically useful; however, it remains unclear if common factors exist in various pathways leading to an onset of dissociations. In this paper we provide a framework for building an integrated, dynamical model of dissociative experiences. This framework combines a first-principles-based perspective with nonlinear dynamical systems, clinical, and neurobiological perspectives. We propose that a substantial change in the parameter we call “temporal depth” can be a common factor in dissociative episodes of any etiology, moreover, we consider such a change to have causal power. In the follow-up series of papers, we will provide empirical data supporting the collapse of temporal depth in various kinds of dissociative experiences, a computer simulation that would test this model’s computational components, and preliminary ideas for therapeutic applications.

Statistically significant violations of the Clauser–Horne–Shimony–Holt (CHSH) inequality are the “gold standard” test for quantum entanglement between spatially separated systems. Here, we report an experimental design that implements a CHSH test between bioelectric state variables for a human subject and bioelectric and/or biochemical state variables for cultured human cells in vitro. While we were unable to obtain evidence for entanglement with this design, observing only classical correlation, we report lessons learned and suggest possible avenues for future studies.

Binz et al. propose a general framework for meta-learning and contrast it with built-by-hand Bayesian models. We comment on some architectural assumptions of the approach, its relation to the active inference framework, its potential applicability to living systems in general, and the advantages of the latter in addressing the explanation problem.