We characterize observability in representation learning through Platonic Projection Structures (PPS), an operator-theoretic framework for analyzing representation accessibility under partial observation. Rather than treating observable outputs as direct reflections of latent representations, PPS models observation through a self-adjoint positive semidefinite operator acting on a latent representation space.
arXiv:2607. 05175v1 Announce Type: new Abstract: We characterize observability in representation learning through Platonic Projection Structures (PPS), an operator-theoretic framework for analyzing representation accessibility under partial observation.
arXiv:2609.00372v1 Announce Type: cross Abstract: With quantum sensors, simulators and networks emerging, a future of quantum technology may produce quantum states as data---that is, coherently rathe...
The Platonic Representation Hypothesis (PRH) claims that independently trained models converge on a shared statistical model of reality, yet recent work finds only weak pointwise similarity between mo...
arXiv:2609.24209v1 Announce Type: new Abstract: The Platonic Representation Hypothesis (PRH) claims that independently trained models converge on a shared statistical model of reality, yet recent wor...
The paper introduces the Lattice Representation Hypothesis (LRH) to explain why different alignment criteria—such as neighbor overlap, spanning trees, and distances—yield varying views on model representation convergence. It shows that the Platonic Representation Hypothesis (PRH) reflects agreement at the top of a lattice, while evidence comes from criteria near the bottom, and that the Aristotelian Representation Hypothesis (ARH) limits convergence to local neighborhoods due to calibration effects. Experiments on numerous pretrained vision and language models, along with theoretical analyses of linear autoencoders and contrastive Gibbs models, support the LRH’s claim that training procedures and objectives determine which criteria are reliably met across runs.
arXiv:2601. 18930v4 Announce Type: replace-cross Abstract: We are interested in enabling autonomous agents to learn and reason about systems with hidden states, such as locking mechanisms.
The paper develops a finite‑measurement framework for inferring the symmetry group that a quantum learning model should respect, based on candidate transformations and limited data. It shows that observable‑invisible transformations correspond to the stabilizer of a projected state when the probe span is invariant, and that recovered generators form a valid subgroup with a continuous invisible space identified via its Lie algebra. The authors introduce an unbiased shadow statistic that improves estimation rates, establish optimal gap dependence through a commuting‑qubit lower bound, and provide tools for task validation, bias quantification, and capacity analysis, all illustrated with Ising‑chain calculations.
The paper derives an information‑theoretic bound for a shared‑state cognitive architecture that uses an auxiliary variable to mediate context. It shows that the residual dependence of observable behavior on context, given the shared state, is bounded by the information carried by the auxiliary variable and its conditional entropy. A recognition‑memory example illustrates how to compute and compare this bound across different representational choices, providing a framework for analyzing context‑memory‑control trade‑offs in cognitive models and artificial agents.
arXiv:2607. 17327v1 Announce Type: cross Abstract: Quantum machine learning models define probabilistic input--output maps through coherent quantum evolution and measurement.
arXiv:2610.07196v1 Announce Type: cross Abstract: Variational autoencoders are powerful representation learning models that map complex data into low-dimensional latent spaces, enabling the discovery...
The paper introduces the Bayesian Mirror Architecture (BMA), a self-referential generative model where sensory abstractions, meta-abstractions, and a self-latent interact through a closed update loop, binding self-representations to abstract world models. Consciousness is defined as an architectural property of this circular structure rather than an optimization goal, with inference occurring over probability measures in an optimal-transport space. Stability, coherence, and causal learning are formalized using the 2‑Wasserstein metric, leading to concepts such as self-manifolds, Wasserstein epsilon-necks, and a Causal Learning Regime that diagnoses learnable causal structure without implying consciousness.
The paper introduces QXymb, a framework for building observational declarative twins of quantum circuits, and presents its first complete order‑0 specialization, QILP‑0. QILP‑0 transforms observed circuit behavior into a finite multi‑valued propositional logic program by incrementally traversing a declared family of quantum observables, quantifying progress via reference‑relative coverage, and preserving observational semantics through deterministic mapping back to original observable columns. Validation on Bars & Stripes and MNIST quantum machine learning settings shows that the induced QILP‑0 theory achieves perfect, conflict‑free reconstruction of the discrete relations, with logical exactness separated from numerical and discretization uncertainties. whyItMatters":"The work demonstrates a method to construct exact observational declarative twins of quantum circuits, enabling precise logical reconstruction of quantum behavior independent of numerical uncertainties."