arXiv:2606.24943v1 Announce Type: cross Abstract: Art and science collaborations that go beyond outreach and advertisement in service of science have the potential to unlock new ways of seeing and understanding the Universe that science alone cannot reach. In this white paper for the NASA Decadal Astrobiology Research and Exploration Strategy (DARES) request for information, we outline examples and benefits of artscience and research-creation methods for astrobiology. The search for life and its origin is inherently interdisciplinary and requires novel approaches that could benefit from the training artists receive in design thinking, contextualization, speculation, and community building. We take a look at this process in action through the work of Robert Irwin during the 1970 NASA Habitability Symposium, Carl Sagan's approach to mixing art and science, and the Transition Design framework of creativity-led problem solving. Each example underscores a specific advantage of deeper art-science collaborations: Irwin's creative approach to problem-solving broke scientists from conventional thought patterns, Sagan's contextualization helped align scientific work with ethical and societal considerations, and design-led research is shown to improve planning and efficiency, even for problems as complex as searching for life. Specific implementation recommendations include specifically allowing funding for artist consultations in research grants, reviving NASA's artist-in-residence program, and supporting artscience training initiatives within the astrobiology community.
Science Journals
arXiv:2606.25072v1 Announce Type: cross Abstract: A popular view in the foundations of diffeomorphism-invariant theories is that their physical content is encoded in correlations or `observables': a set of phase space functions that have vanishing Poisson brackets with the constraints related to diffeomorphisms. In this article I study the phase space structure of models with a temporal diffeomorphism invariance and prove a series of formal results that challenge this view in a few ways. First, I show how this view is not applicable to all the phase space trajectories of every diffeomorphism-invariant theory. Second, I show how correlations can be proved to be invariant only in a way that generalizes the standard definition of invariance and in a way that does not provide smooth phase space functions. Third, I prove that spatiotemporal relations are also invariant. Fourth, I prove that spatiotemporal structures are indispensable for defining the invariant content of diffeomorphism-invariant models. Finally, I comment that these results are expected to be generalizable for models invariant under $d$-dimensional diffeomorphisms, which represents a challenge for some views in the foundations of general relativity and quantum gravity.
arXiv:2606.25360v1 Announce Type: new Abstract: While end-to-end Vision-Language-Action (VLA) models show promise in robotic manipulation, their monolithic paradigm inherently couples semantic reasoning and spatial control. This creates a severe alignment bottleneck, limiting precise target disambiguation in data-constrained imitation learning. To overcome this, we propose SVP-IL, a decoupled architecture that explicitly extracts spatial visual grounding from the action generation loop. By leveraging vision-language foundation models, we parse instructions into zero-shot geometric masks, translating language into explicit Spatial Visual Prompts (SVP). These priors are injected into a continuous action generator via a lightweight direct feature-level fusion mechanism. This integration provides explicit and uncorrupted spatial gradient guidance while ensuring highly stable optimization under low-data regimes. Extensive experiments demonstrate that SVP-IL significantly outperforms state-of-the-art VLAs and pure visuomotor baselines. Trained on as few as 50 to 100 demonstrations, SVP-IL improves average success rates on highly ambiguous language-conditioned tasks from 24.0% to 39.5%, achieving 67.8% on standard benchmarks. Real-world robotic experiments further validate its robustness and data efficiency in unstructured physical environments.
arXiv:2606.25420v1 Announce Type: new Abstract: In this work, to address the challenge of joint channel estimation and data detection (JED) for orthogonal delay-Doppler (DD) division multiplexing (ODDM) in doubly selective channels, we propose an orthogonal approximate message passing (OAMP)-aided JED (OAMP-JED) receiver. We first formulate a bilinear cross-domain JED model, which can be linearized into separate channel estimation and data detection subproblems. The proposed OAMP-JED receiver alternately executes two OAMP modules for these subproblems, effectively coupled through a variational noise term to account for model uncertainty. Leveraging OAMP's error orthogonality, we derive closed-form scalar-variance updates to enable efficient and principled soft information exchange between the modules, thereby mitigating error propagation during JED. Simulation results show that, for both uncoded and coded ODDM, OAMP-JED achieves a lower bit error rate (BER) than benchmark schemes. Moreover, its BER performance closely approaches that of OAMP with perfect CSI.
arXiv:2606.25398v1 Announce Type: new Abstract: Reward design remains a major bottleneck in reinforcement learning for robot locomotion, where successful policies often depend on carefully tuned, task-specific reward functions. Preference-based reinforcement learning offers an alternative, but existing LLM-based methods typically ask for a single overall judgment between behaviors, making it difficult to capture the multiple competing objectives that underlie high-quality locomotion. We present Multi-Objective AI-Informed Preference Learning (MAPL), a framework that learns locomotion rewards from high-level natural language objectives rather than manually engineered reward equations. MAPL prompts a large language model to compare trajectories independently along semantically meaningful criteria, using generic language descriptions that are terrain-invariant and require little domain expertise. These objective-wise preferences are used to train a multi-head preference scoring model, whose outputs are aggregated to form a scalar reward for policy optimization. Across four quadruped locomotion environments, MAPL trains policies using only LLM-generated preferences and achieves performance comparable to or better than expert-designed rewards, while eliminating task-specific reward engineering.
arXiv:2606.25229v1 Announce Type: cross Abstract: Chemical potentials are among the most important properties that can be obtained from a molecular simulation since they define many technologically relevant collective properties. The chemical potential of a species in solution is obtained by computing the free energy change of adding that species into a bulk system, a calculation typically very expensive for systems such as electrolytes, due to the lack of phase space overlap between "not-inserted" and "inserted" states. Recently, normalizing flows have been introduced as a way to accelerate free energy computations by learning a bijective function, constructed to be as expressive as possible, that maps the configuration space of one Boltzmann distribution onto another. This expressivity makes them difficult to train, limiting their ability to be generated "on-the-fly" for any new system, and in practice these mappings have shown only modest sampling improvements for liquids. We address these issues by introducing a "minimal" normalizing flow (MNF). This is a trainable bijective mapping that is intentionally limited in expressivity, and instead applies low-dimensional, physically informed transformations. Useful MNFs can be trained in 1 minute of GPU time due to their simplicity and our introduction of a novel training strategy. We show how calculations of chemical potentials of Lennard-Jones particle systems can be accelerated by at least 10 times with a simple radial mapping. We also apply a radial and orientational mapping to ion solvation in water, showing that MNFs can increase the effective sample size by 3 times for charging free energy calculations and 8 times for calculating free energy changes due to force field perturbations. This provides the foundation for the development of physically-informed mappings that can accelerate complex free energy calculations while retaining low training costs.
arXiv:2606.25447v1 Announce Type: new Abstract: Tool-integrated LLM agents are often wrapped within a harness: the scaffolding that determines which tools are exposed, how they are described, and what auxiliary information accompanies each per-step observation. While agents are routinely post-trained, this scaffolding is typically treated as a fixed engineering detail, with design effort limited to the training-free regime. Moreover, existing post-training algorithms assume a static environment, even though tool environments and tasks often shift upon deployment. To address this gap, we extend $\texttt{ALFWorld}$ (i) to treat the harness as a controllable design dimension and (ii) to support evaluation under task and tool environment shifts. Building on this, we systematically analyze how the harness design influences post-training in both in-distribution and out-of-distribution (OOD) settings. We empirically show that harness-aware post-training not only improves in-distribution performance but also enables agents to robustly adapt to OOD settings. Under a harness with minimal design effort, post-training suffers a drastic performance drop under stronger tool environment shifts, further highlighting the importance of harness-aware post-training under such shifts.
arXiv:2606.25448v1 Announce Type: new Abstract: We show that there is a simple syntactically-defined subclass of higher-order benchmarks in the termination problem database for which rewriting according to Nipkow's higher-order rewrite systems (HRSs) and rewriting according to a beta-first strategy in the semantics of TermCOMP's higher-order category coincide. This lays the formal foundation for an HRS (sub)category in TermCOMP which would allow more tools to compete against each other.
arXiv:2606.25453v1 Announce Type: new Abstract: Modern GPUs devote an increasing silicon budget to low-precision matrix-multiplication units, widening the precision-throughput gap for scientific computing workloads. Ozaki Schemes I and II offer an alternative by reconstructing high-precision general matrix multiplication (GEMM) from low-precision operations, yet existing implementations leave substantial performance untapped. In particular, intermediate results are repeatedly materialized in global memory, making data movement the dominant bottleneck. We present EmuGEMM, fused integer Tensor Core kernels for NVIDIA Hopper and Blackwell GPUs that eliminate redundant memory round-trips in both Ozaki schemes. Using Scheme I, EmuGEMM sustains up to 1,639 Top/s on Hopper (83% of INT8 peak) and 3,654 Top/s on Blackwell (81%). For large matrices, EmuGEMM surpasses cuBLAS TF32 throughput by up to 1.4x on Hopper and 1.7x on Blackwell, at comparable accuracy. Using Scheme II, EmuGEMM extends to complex arithmetic and outperforms cuBLAS ZGEMM by up to 2.3x on Hopper and 5.5x on Blackwell.
arXiv:2606.25454v1 Announce Type: new Abstract: In this study, the learning-based inertial odometry problem is investigated using raw IMU measurements obtained from the EuRoC MAV benchmark dataset. Instead of absolute position regression-a formulation that may lead to large constant errors-the models are trained to estimate the incremental displacement ({\Delta}p) over a fixed 50 ms sliding window, and the full trajectory is reconstructed through numerical integration. A standard Multi-Layer Perceptron (MLP) is compared with a Kolmogorov-Arnold Network (KAN) equipped with learnable B-spline activations. Although KAN has 6.9 times fewer parameters than MLP (8,444 versus 57,859), it produces a 44% lower error in terms of final cumulative drift on the test trajectory (9.61 m versus 17.23 m). In addition, KAN exhibits more stable behavior in terms of long-term error accumulation, with lower P_50 and P_90 cumulative drift values. These findings indicate that learnable B-spline-based activations have the potential to reduce error accumulation in the inertial odometry problem.
arXiv:2606.25530v1 Announce Type: new Abstract: Software performance optimization is a notoriously complex and manual task. Despite the growing use of Large Language Models (LLMs) for code refinement, we still lack benchmarks that capture how optimization actually happens in real-world codebases. Existing frameworks often oversimplify the problem by focusing on isolated functions or a single performance metric, missing the critical trade-offs between execution time and memory footprint, the inherent noise of the measurement environment, and the variability introduced by different input data and execution conditions. We address this by introducing SWE-Pro, a repository-level benchmark derived from 102 expert-written optimizations from open-source projects. Unlike previous benchmarks, SWE-Pro pairs each task with parameterized tests to evaluate runtime, peak memory, and Time-Weighted Memory Usage (TWMU) across varying input data and execution conditions under noise-aware measurement conditions. Our evaluation shows that current LLMs struggle significantly: runtime gains are negligible, and memory optimizations are nearly non-existent. This stands in sharp contrast to expert implementations, which achieve an aggregate speedup of 15.5x and peak memory reduction of 171.3x over benchmark tasks. Expert-written improvements are observed in 91.2% of tasks for runtime and 65.7% for peak memory. Our findings expose a substantial gap between current LLM capabilities and the demands of expert-level engineering.
arXiv:2606.25438v1 Announce Type: cross Abstract: We prove a three-dimensional positive-cone continuation criterion for the stress-diffusion-free Oldroyd-B system on the periodic torus. Writing the positive conformation tensor as A = exp(B), we show that finite-time breakdown of a strong H^s solution, s > 5/2, can occur only through loss of the logarithmic spectral envelope of A or divergence of the endpoint vorticity clock given by the time integral of the B^0_{infty,1} norm of curl u. The proof combines compact positive-cone envelopes, endpoint Biot-Savart estimates, and high-order logarithmic conformation estimates, without using stress diffusion. We also derive a positive-cone Reynolds admissibility criterion with an exact residual-work cost. The least L^2 conformation residual needed to pay positive pressure-free residual work is determined by the entropy-dual lever G = I - A^{-1}, and this cost degenerates quantitatively near the equilibrium A = I. Together, the two criteria identify the same positive-cone obstruction in the strong and relaxed regimes: before breakdown one must control the endpoint flow clock on a compact logarithmic cone, while after passage to a relaxed description positive residual work must be paid for by an exact entropy-dual conformation defect.
arXiv:2606.25502v1 Announce Type: cross Abstract: Discovering solid-state materials with target properties remains a central challenge in computational materials science. Existing approaches -- high-throughput screening, surrogate optimization, and generative models -- require extensive evaluations or training data and extrapolate poorly to unseen compositions. Here we develop a first-principles inverse-design framework, integrating reverse-mode automatic differentiation (AD) into KKR-CPA -- the Korringa--Kohn--Rostoker method with the coherent potential approximation -- where atomic compositions are continuous variables to be optimized. Reverse-mode AD yields gradients of objective functions with respect to composition at a cost independent of the number of candidate elements, enabling gradient-based optimization to identify materials from compositional spaces spanning dozens of elements. In this framework, any computable quantity can serve as the objective. We demonstrate this generality through two contrasting applications, magnetic alloys and half-metals, yielding candidates such as (Lu$_{0.553}$Yb$_{0.447}$)(Co$_{0.759}$Fe$_{0.241}$)$_2$Fe$_3$ and FeZr(Sb$_{0.94}$Te$_{0.06}$). Our framework offers a physically grounded route from a target property to the material that realizes it.
arXiv:2606.25511v1 Announce Type: cross Abstract: The cavity-magnon-qubit system has recently been demonstrated as a new platform for preparing macroscopic quantum states in magnonic systems. Here, we propose to prepare a two-mode magnonic cat state, which is also a non-Gaussian entangled state, based on this practical system involving two yttrium-iron-garnet (YIG) spheres and a superconducting qubit coupled to a common microwave cavity. By adiabatically eliminating the cavity and resonantly driving the qubit, an effective magnon-qubit conditional-displacement interaction is achieved. Further working in the magnon-magnon strong-coupling regime and considering two identical magnon frequencies and coupling strengths to the cavity, two hybridized magnon modes are formed, of which the bright mode is prepared in a cat state after a projective measurement on the qubit, while the dark mode remains in its initial vacuum state. Such a state corresponds to a two-mode cat state of two original magnon modes, which share strong non-Gaussian entanglement. We also discuss practical dissipation and dephasing effects on the cat state. The results indicate that strong nonclassicality and non-Gaussian entanglement are present in the two-mode cat state using fully feasible parameters.
arXiv:2606.25615v1 Announce Type: cross Abstract: In this paper, we consider a parabolic problem with constraint written as a differential inclusion, driven by a multiplicative colored noise and involving a p-Laplace operator (for $p \geq 2$), nonlinear random source terms and subject to Neumann boundary conditions on a bounded Lipschitz domain of $R^d$ with $d \geq 1$. This contribution aims at proving existence and uniqueness of a solution for such a multivalued problem. On one hand, the existence result is proved by the analysis of a semi-implicit time discretization scheme constructed on a smoother version of our problem, itself obtained by a regularization "\`a la Moreau-Yosida" of the subdifferential term. The key point of our approach consists in finding a clever relation between the time step denoted $\tau$ and the Moreau-Yosida regularization parameter denoted $\epsilon$ in view to pass simultaneously to the limit with respect to $\tau$ and $\epsilon$. On the other hand, the uniqueness of the solution is proved by standard arguments.
arXiv:2512.01759v3 Announce Type: replace Abstract: We investigate the potential of weights to serve as effective representations, focusing on neural fields. Our key insight is that constraining the optimization space through a pre-trained base model and low-rank adaptation (LoRA) can induce structure in weight space. Across reconstruction, generation, and analysis tasks on 2D and 3D data, we find that multiplicative LoRA weights achieve high representation quality while exhibiting distinctiveness and semantic structure. When used with latent diffusion models, multiplicative LoRA weights enable higher-quality generation than existing weight-space methods.
arXiv:2606.25666v1 Announce Type: cross Abstract: Classical invisibility cloaks are designed to suppress selected scattering signatures and thereby make an object appear absent to external electromagnetic probes. However, the suppression of a classical scattering observable does not, by itself, establish that all information about the concealed object has been removed from the detected quantum state of light. Here we formulate the detectability of classically cloaked objects as a quantum-state distinguishability problem. Treating a linear passive cloak as an effective Gaussian quantum channel acting on the accessible detected modes, we show that local quantum undetectability requires the detected first and second moments to be independent of the hidden-object parameter. In this framework, quantum Fisher information provides an operational criterion for whether the concealed parameter remains estimable from the detected output state. We derive displacement- and covariance-level detectability conditions and show that a nonzero parameter imprint surviving in the detected Gaussian state leads to a nonzero accessible quantum Fisher information. To connect the criterion with a physical cloaking model, we analyze a regularized cylindrical transformation-optical cloak in the Born limit and compare the scaling of the classical scattering response with the derivative-based quantum sensitivity. The analysis shows that reducing a scattering amplitude is not equivalent to eliminating local quantum-state sensitivity. Loss, environmental noise, and finite numerical aperture degrade the accessible information, but quantum undetectability is reached only when the parameter imprint is removed from the detected state or projected entirely outside the accessible subspace. These results provide a Gaussian-channel framework for assessing when classical cloaking does, and does not, imply quantum-state undetectability.
arXiv:2606.25808v1 Announce Type: cross Abstract: We propose a predict-optimize-explain framework that uses gradient-based sample generation to interpret various portfolio models by identifying macroeconomic conditions that induce specified portfolio outcomes. Unlike traditional feature-importance methods, this approach directly probes decision pipelines (predictive models coupled with portfolio optimization) by constructing economically meaningful what-if questions. We focus on four such questions: under what macroeconomic conditions a predict-then-optimize pipeline closes or reverses its return gap with a predict-and-optimize pipeline; what conditions lead a pipeline to diversify rather than concentrate its allocation; when a pipeline trained on calm markets overtakes one trained through crises; and what conditions would let a pipeline match a benchmark return. These examples illustrate how our framework uncovers key behavioral differences between various decision pipelines. Beyond these cases, the proposed framework is flexible and can support a wide range of probing questions tailored to specific portfolio objectives. Our findings highlight the value of integrating prediction, optimization, and explanation to produce more robust and transparent portfolio strategies.
arXiv:2606.25545v1 Announce Type: new Abstract: Reconstructing diffusion tensors from sparse DWIs is critical for accelerating Diffusion Tensor Imaging (DTI) in clinical settings, yet current deep learning approaches frequently yield anatomically inconsistent or physically implausible tensors. We introduce TensorLDM, a component-wise latent diffusion model that processes the six tensor components through two group-specific encoders (for diagonal and off-diagonal elements) while maintaining anatomical consistency via shared DWI conditioning. TensorLDM uses an Anatomy-Conditioned Autoencoder that encourages the latent to focus on tensor properties rather than re-encoding structural information. A shared Cross-Component Attention (CCA) mechanism, applied in both autoencoder refinement and diffusion fine-tuning, models inter-component dependencies, while a Mixture-of-Experts (MoE) DWI conditioner provides component-adaptive conditioning. On the Human Connectome Project (HCP) dataset under a single-shell, four-volume sparse acquisition, TensorLDM produces the most accurate downstream tractography and tensors with near-ground-truth physical validity (SPD-violation rate 1.54% vs. 1.40%), with the best or comparable voxel-wise reconstruction accuracy. Geodesic tensor error measured by the Log-Euclidean Metric (LEM) corroborates these gains.
arXiv:2606.25575v1 Announce Type: new Abstract: Assistive robotic systems face a fundamental trade-off: fully autonomous systems lack user agency, while fully user-controlled systems demand continuous cognitive effort. Existing shared autonomy approaches blend human and robot commands but are mostly deployed in separate physical bodies. We introduce co-embodiment with variable autonomy, where human and robot share a single physical body and operate at different autonomy levels across task phases, from mutual autonomy during object search and grasping to human-dominant control during actuation. We present a co-embodied, wearable robotic hand that has its own ``mind'' and operates with variable autonomy levels. A learning-from-demonstration visuomotor diffusion policy enables autonomous grasping when the user positions the hand near known objects. Once grasped, the system signals completion and the human can actuate the grasped tool (drill, spray bottle, infrared thermometer, lighter, and ice-cream scoop) via hands-free head gestures. The human retains veto authority at all times through a release gesture that returns the system to the initial phase. Unlike blended autonomy, where control is continuously negotiated, our co-embodied approach consists of variable autonomy from full human control to full independent actions while maintaining physical coupling, realizing a one body, two minds paradigm. In a user study with 44 participants performing five bimanual tasks, users rapidly adapted to this ``two minds'' paradigm: completion times improved by 23.3% across trials ($p < 0.001$, Cohen's $d = 0.94$), the best-performing policy variant reached a 93.6% task success rate, and acceptance ratings were high (5.70/7 overall impression, 5.52/7 daily use willingness). This work establishes co-embodiment with variable autonomy as a viable approach for assistive robotics, enabling human-robot collaboration through co-embodiment.
arXiv:2606.25817v1 Announce Type: cross Abstract: The IC4Stars (Intensity Correlation for Stars) project aims to measure the diameter of the white dwarf star Sirius B, using Intensity Interferometry. In this work we present our latest efforts and the milestones achieved in the last year. We report laboratory characterization of the single-photon detectors, TDC and synchronization electronics. We describe an observation campaign where we demonstrated on-sky time resolution below~35~ps~RMS, synchronizing two TDCs using the White Rabbit protocol and a~30~m telecom fiber. We developed the data acquisition of the raw time tags, and an algorithm to compute the second-order correlation function.
arXiv:2606.25945v1 Announce Type: cross Abstract: The increasing number of orbital and sub-orbital launches makes it necessary to investigate the environmental impacts of launch vehicles and incorporate eco-design considerations into their development. In response, the European Space Agency has promoted Life Cycle Assessment (LCA) as a standardization methodology to mitigate environmental impacts of present and future space missions. This need is further amplified in the NewSpace, where numerous configurations and innovative technologies are explored, reinforcing the importance of integrating environmental considerations. At early design stages, launch vehicle architecture can be formalized through a multi-physics optimization problem based on Multidisciplinary Design Analysis and Optimization (MDAO) methods, where disciplines such as propulsion, aerodynamics, structure, and trajectory are coupled to obtain trade-offs among candidate configurations. This paper proposes a methodology to integrate an LCA discipline within an MDAO framework for launch vehicle design. The approach relies on parametric life-cycle inventories depending on design and coupling variables, covering component and propellant production as well as transport to the launch site. Launch emissions are evaluated from optimized trajectory profiles and characterized in terms of climate change impact. The methodology is illustrated on a representative expendable launch vehicle, where multi-objective optimizations assess trade-offs between performance and environmental indicators. Results highlight antagonistic behaviors among environmental impact categories, emphasizing the importance of carefully defining environmental objectives in eco-design studies. The generic nature of the methodology lays the foundation for integrating LCA into early-stage launch vehicle design, enabling exploration of trade-offs between performance, cost, and environmental considerations.
arXiv:2606.25946v1 Announce Type: cross Abstract: Van der Waals materials are ideally suited for the implementation of high-frequency nanophononic resonators with atomically flat interfaces. Here, we present two versatile van der Waals-based nanophononic architectures: First, we introduce self-supporting nano-domes of WSe$_2$ as a scalable platform for the simultaneous generation of hundreds of high-quality nanoacoustic resonators with resonance frequencies in the 100 GHz range. Second, we engineer self-supporting nano-drums that reach record-high working frequencies for 2D-semiconductor transducers beyond 1 THz. Through optical pump-probe spectroscopy experiments and photoelastic linear chain model calculations, we gain a detailed understanding of the intricate interplay between phononic mode hybridization across heterostructures, the differences between modes close to the center and edge of the acoustic Brillouin zone, and the temporal structure of the photoelastic response. Both architectures have potential applications in low-cost nanoacoustic probing and the ultrafast modulation of quantum emitters in two-dimensional semiconductors. While nano-drums surpass the THz frequency barrier, nano-domes appear as an accessible, low-cost alternative for developing scalable nanophononic technologies.
arXiv:2606.25954v1 Announce Type: cross Abstract: This theoretical note studies the finite axiomatizability of strict majority reasoning in finite social decision frames. Moss and Pedersen (2026) introduce a coherence criterion that characterizes exactly when qualitative majority judgments are representable by a finitely additive measure. The question addressed here is whether that coherence criterion can be replaced, in the finite setting, by any bounded finite fragment. We prove that it cannot. For every $k\ge 1$, we construct a maximal standard frame whose shortest coherence violation has length exactly $2k+2$. Hence there is no uniform finite bound on the incoherence index of social decision frames, resolving Conjecture 5.7 stated by Moss and Pedersen (2026). The construction is geometric, in the sense that it proceeds via orthogonality and dimension in rational vector spaces, and self-contained: it isolates a symmetric family of half-sized voting blocs and extends it to a maximal frame in which every shorter balanced obstruction is excluded. Along the explicit infinite sequence of universe sizes obtained in the construction, this also establishes the middle-layer family predicted by Conjecture B.25 by Moss and Pedersen (2026). Together with the soundness and completeness theorem for the Moss-Pedersen minimal logic for strict majorities, this establishes that measurable social decision frames are not finitely axiomatizable in that language.
arXiv:2606.26000v1 Announce Type: cross Abstract: The coherent, periodic energy transfer between light- and matter excitations characterizes the strong coupling regime of cavity exciton-polaritons, resulting, in the simplest case, in a Rabi-doublet in the spectral domain. We demonstrate a peculiar regime of strong light-matter coupling, which arises when photonic cavity modes couple to an ultra-thin excitonic mirror. We embed a 12 nm J-aggregated thin film in an open microcavity and tune the coupling strength from weak to the onset of ultrastrong coupling. At resonance, the excitonic mirror selectively changes dielectric to metallic field boundary conditions adding a 2{\pi} phase, which links optical cavity modes of different order. Our work gives an exciting perspective to ultra-fast cavity switches and photonic devices based on excitonic optical elements.