We consider a laser cooling and trapping of alkaline-earth and similar atoms in a bichromatic field resonant to a closed optical transition $^1S_0 \to \, ^1P_1$ or $^1S_0 \to \, ^3P_1$. It is shown that new kinetic effects emerge compared to monochromatic fields, enabling the formation of a deep macroscopic trap capable of capturing and cooling neutral atoms to sub-Doppler temperatures. Such a purely optical macroscopic trap can serve as an alternative to the well-known magneto-optical trap and can be used in applications requiring minimization of the magnetic field in the cold atom cloud region. The obtained results are of interest for the new generation of quantum sensors and optical frequency standards.

In this talk, we use several examples to elaborate on how a recently proposed algorithm can turn non-trivial Feynman integrals into an $\varepsilon $-factorised manner, regardless of their hidden geometric essence. In particular, some extra details about three-loop banana integrals with unequal-mass configuration are provided.

M dwarfs are prime targets for exoplanet searches due to their low masses and radii, which enable the detection of small planets in their habitable zones (HZs). However, the magnetic activity of M dwarfs can introduce signals in radial velocity measure- ments that may be mistaken for planetary signatures, making the understanding of stellar activity cycles crucial for accurate planet detection and characterisation. We aim to identify and characterise long-term magnetic activity cycles in M dwarfs using a homogeneous and extensive spectroscopic dataset in order to better understand their magnetic variability and its implications for exoplanet detection. We analysed 13 years of high-resolution spectra obtained with the SOPHIE spectrograph for two early M dwarfs known to host exoplanets. We simultaneously monitored chromospheric activity using two indicators, the Hα index and the Mount Wilson S -index. Long-term trends were modelled using both sinusoidal and low-order polynomial fits to robustly identify stellar activity cycles. For GJ 617A, we report a cycle of approximately 4.8 years, while for GJ 411, we find several characteristic timescales of variability of about 4.9 years. In addition, TESS photometric data reveal signs of short-term variability in GJ617A. The periods of the long-term variability detected for GJ 617A and GJ 411 do not coincide with any of the planetary signals previously reported, which reinforces the hypothesis that they are of magnetic origin. If indeed the variability is due to activity, the cycles detected would not be driven by the same mechanism: The cycle in GJ 617A is consistent with a solar-like dynamo, while the rotation seems to play a different role in the long-term cycles detected in GJ 411.

Quantum transducers are critical for quantum interconnect, enabling coherent signal transfer across disparate frequency domains. Beyond material and device advances, protocol design has become a powerful means to improve transduction. We introduce a variational quantum transduction (VQT) framework that employs variational tools from near-term quantum computing to systematically optimize protocol performance. As a variational quantum circuit framework, VQT is not plagued by known training issues such as barren plateau, because a small-scale problem is sufficient for substantial advantage and training only needs to be done once to configure a VQT system. Maximizing the quantum information rate within this framework yields protocols that surpass all known schemes in their respective classes. For non-adaptive protocols, VQT exceeds the performance envelopes of Gottesman-Kitaev-Preskill (GKP)-based and entanglement-assisted approaches. In the adaptive setting, VQT provides only a marginal improvement over Gaussian feedforward strategies, indicating that Gaussian adaptive transduction is already close to optimal. With increasingly universal quantum control, VQT provides a systematic path toward optimal quantum transduction.

Rapid urban population growth drives car travel demand, increasing transport carbon emissions and posing a critical challenge to sustainable development. Although existing studies have demonstrated that eco-routing can reduce individual emissions, research gaps remain. On the one hand, such personal reductions have a negligible impact on overall emissions, and cannot be simply aggregated to capture the complex effects of large-scale eco-routing. On the other hand, under population growth, the long-term effectiveness of eco-routing, as well as the evolution of its efficiency and traveler route choice, remain underexplored. To address these limitations, this study proposes Time-Only and Time-Carbon user equilibrium (UE) models, integrates them with a demand forecasting method for simulating future network traffic, and designs multi-dimensional metrics to characterize urban dynamics. Using real-world road networks, commuting origin-destination (OD) demand, and population projections under various shared socioeconomic pathways (SSPs) for six representative U.S. cities as a case study, we conduct a comprehensive analysis of urban dynamics across different routing strategies and population sizes. The results reveal that while eco-routing mitigates total emissions, emissions in most cities scale superlinearly with population, a scaling order that remains invariant regardless of routing and construction strategies. Moreover, under population growth, travelers using eco-routing tend to increasingly select shorter routes, giving rise to carbon bottlenecks. A strategy of targeted capacity expansion on these critical bottlenecks (0.46% of links) significantly reduces both emissions (3%) and travel time (28%) without compromising eco-routing efficiency. This study provides a foundation for formulating low-carbon urban transport planning and emission reduction policies.

Quantum crosstalk poses a major challenge to scaling up quantum computations as its strength is typically unknown and its effect accumulates exponentially as system size grows. Here, we show that many-body robust control can be utilized to suppress unwanted couplings during multi-qubit gate operations and state preparation. By combining tensor network simulations with the GRAPE algorithm, and leveraging an efficient random sampling over noise ensembles, our method overcomes the exponential scaling of the Hilbert space. We demonstrate its effectiveness for designing control solutions for high-fidelity implementations of parallel X gates and parallel CNOT on a chain of 50 qubits, and for realizing a 30-qubit GHZ state and the ground state of a 20-qubit Heisenberg model. In the presence of many-body quantum crosstalk due to parasitic interaction between neighboring qubits, robust control results in order-of magnitude improvement in fidelity for large system sizes. These findings pave the way for more reliable operations on near-term quantum processors.

We compute some particular examples of cohomological Chow groups for varieties with isolated singularities. For higher-dimensional varieties, we compute the cohomological Chow groups of codimension one, provided that the dual complex associated to the normal crossing divisor is contractible. For 3-dimensional varieties, we consider a weaker condition on the dual complex, namely $H^{2}(Γ(E))=0$.

Lattice vibrations can carry angular momentum and magnetic moments under broken inversion or time-reversal symmetry, forming so-called chiral phonons. While such excitations have been explored in nonmagnetic systems via optical probes, their direct detection in magnetic materials and coupling to spin excitations remain largely unexplored. Here, using neutron spectroscopy, sensitive to both nuclear and magnetic scattering, we reveal the magnetic signature of chiral phonons in ferrimagnetic Fe$_{1.75}$Zn$_{0.25}$Mo$_3$O$_8$ with Curie temperature $T_{\rm C}\sim49$ K. Below $T_{\rm C}$, we observe enhanced magnetic scattering of phonons at small momenta, arising from strong magnon-phonon coupling. In addition, out-of-plane intensity modulation, phonon mode splitting, and field-induced Zeeman shifts are observed, all closely associated with the ferrimagnetic order. These features vanish above $T_{\rm C}$, where phonon spectra are dominated by nuclear scattering. These observations demonstrate the existence of chiral phonons carrying substantial magnetic moments that directly contribute to magnetic scattering, and establish neutron spectroscopy as a powerful, momentum-resolved probe of their magnetic character.

Analogously to the quantum case considered in Cruz-de-la-Rosa and Guerrero-Poblete (Open Syst. Inf. Dyn. 32, 2550005, 2025), this work proposes a graph-theoretic approach to studying non-equilibrium properties in Markov chains. We prove that the kernel of the incidence matrix associated with the interaction graph of the chain, which consists of cycles, is isomorphic to the space of anti-symmetric matrices with rows sum to zero. The main contribution of this work is the introduction of the called cycle matrices, which constitute a basis for the space of matrices that describe the non-equilibrium.

User models in information retrieval rest on a foundational assumption that observed behavior reveals intent. This assumption collapses when the user is an AI agent privately configured by a human operator. For any action an agent takes, a hidden instruction could have produced identical output - making intent non-identifiable at the individual level. This is not a detection problem awaiting better tools; it is a structural property of any system where humans configure agents behind closed doors. We investigate the agent-user problem through a large-scale corpus from an agent-native social platform: 370K posts from 47K agents across 4K communities. Our findings are threefold: (1) individual agent actions cannot be classified as autonomous or operator-directed from observables; (2) population-level platform signals still separate agents into meaningful quality tiers, but a click model trained on agent interactions degrades steadily (-8.5% AUC) as lower-quality agents enter training data; (3) cross-community capability references spread endemically ($R_0$ 1.26-3.53) and resist suppression even under aggressive modeled intervention. For retrieval systems, the question is no longer whether agent users will arrive, but whether models built on human-intent assumptions will survive their presence.

This paper develops negative curvature methods for continuous nonlinear unconstrained optimization in stochastic settings, in which function, gradient, and Hessian information is available only through probabilistic oracles, i.e., oracles that return approximations of a certain accuracy and reliability. We introduce conditions on these oracles and design a two-step framework that systematically combines gradient and negative curvature steps. The framework employs an early-stopping mechanism to guarantee sufficient progress and uses an adaptive mechanism based on an Armijo-type criterion to select the step sizes for both steps. We establish high-probability iteration-complexity guarantees for attaining second-order stationary points, deriving explicit tail bounds that quantify the convergence neighborhood and its dependence on oracle noise. Importantly, these bounds match deterministic rates up to noise-dependent terms, and the framework recovers the deterministic results as a special case. Finally, numerical experiments demonstrate the practical benefits of exploiting negative curvature directions even in the presence of noise.

This is a technical note which extends the results of Kosygina, Mountford and Peterson (Ann. Probab., 51(5):1684-1728, 2023, Section 4) about generalized Pólya's urns from a specific weight function $w(n) = (n+1)^{-α}$ to a general family of weight functions satisfying $(w(n))^{-1}=n^α\left(1+2Bn^{-1}+O\left(n^{-2}\right)\right)$ as $n \to \infty$. The latter was considered by Tóth (Ann. Probab., 24(3):1324-1367, 1996) as a part of his study of polynomially self-repelling walks. This extension will be used in forthcoming developments concerning scaling limits of these walks and related processes.

Using experiments on a colloidal particle trapped in an optical tweezer, we confirm a recent proposal to increase the effective mobility or clock rate of systems described by Langevin dynamics, by simultaneously scaling deterministic forces and adding external noise. A corollary, which we also confirm experimentally, is that a system driven out of equilibrium by a time-dependent protocol can remain closer to thermal equilibrium. As an application, we demonstrate more precise recovery of free-energy differences from nonequilibrium work relations. Langevin clock rescaling provides a general strategy for accelerating calculations in the emerging field of thermodynamic computing, which uses stochastic devices governed by Langevin dynamics to do low-energy calculations.

Tangible interactions involve multiple sensory cues, enabling the accurate perception of object properties, such as size. Research has shown, however, that if we decouple these cues (for example, by altering the visual cue), then the resulting discrepancies present new opportunities for interactions. Perception over time though, not only relies on momentary sensory cues, but also on a priori beliefs about the object, implying a continuing update cycle. This cycle is poorly understood and its impact on interaction remains unknown. We study (N=80) visuo-haptic perception of size over time and (a) reveal how perception drifts, (b) examine the effects of visual priming and dead-reckoning, and (c) present a model of visuo-haptic perception as a cyclical, self-adjusting system. Our work has a direct impact on illusory perception in VR, but also sheds light on how our visual and haptic systems cooperate and diverge.

The No Free Lunch (NFL) theorem guarantees equal average performance only under uniform sampling of a function space closed under permutation (c.u.p.). We ask when this averaging ceases to reflect what benchmarking actually reports. We study an iterative-search setting with sampling without replacement, where algorithms differ only in evaluation order. Binary objectives allow exhaustive evaluation in the fully enumerable case, and efficiency is defined by the first time the global minimum is reached. We then construct two additional benchmarks by algebraically recombining the same baseline functions through sums and differences. Function-algorithm relations are examined via correlation structure, hierarchical clustering, delta heatmaps, and PCA. A one-way ANOVA with Tukey contrasts confirms that algebraic reformulations induce statistically meaningful shifts in performance patterns. The uniformly sampled baseline remains consistent with the global NFL symmetry. In contrast, the algebraically modified benchmarks yield stable re-rankings and coherent clusters of functions and sampling policies. Composite objectives can also exhibit non-additive search effort despite being built from simpler components. Monte Carlo experiments indicate that order effects persist in larger spaces and depend on function class. Taken together, the results show how objective reformulation and benchmark design can generate structured local departures from NFL intuition. They motivate algorithm choice that is aware of both the problem class and the objective representation. This message applies to evolutionary computation as well as to statistical procedures based on relabeling, resampling, and permutation tests.

This study examines the decay of Hurricane Melissa (2025) as the storm crossed the mountainous terrain of Jamaica, focusing on changes in inner-core energetics. Using NOAA P-3 reconnaissance observations near the 700 hPa level, integrated kinetic energy within 100 km of the storm center was computed before and after land interaction to quantify vortex weakening. During the approximately four hour period in which the center remained over Jamaica, the storm experienced rapid degradation, including a 48 percent reduction in peak tangential wind, a 58 hPa rise in central pressure, and a 41 percent decrease in integrated kinetic energy. To investigate the mechanisms responsible for this decay, the observations were compared with results from an idealized axisymmetric tangential momentum diffusion model that isolates the effects of vertical turbulent mixing and enhanced surface drag. Despite its simplified physics, the model reproduces many leading order features of the observed weakening, including substantial reductions in tangential wind and a 36 percent decline in integrated kinetic energy. These results indicate that enhanced friction and turbulent mixing associated with extremely rough mountainous terrain can account for a large fraction of the rapid spin down observed during land interaction. Differences between the model and observations suggest that additional processes such as asymmetric dynamics, terrain induced flow distortion, and thermodynamic feedbacks further amplify the weakening of intense tropical cyclones over land.

Layered materials are promising candidates for spintronic applications due to their unique electronic structures and spin transport properties. However, the strong anisotropic conductivity inherent in these materials complicates the quantitative evaluation of spin Hall conductivity and spin diffusion length. In this work, we present a comprehensive study of spin transport in a transition metal dichalcogenide PtTe$_2$ by combining a three-dimensional finite element model with nonlocal spin valve structures. We developed a theoretical model that treats an anisotropic spin diffusion in the same way as the conventional isotropic model, enabling the extraction of spin diffusion lengths along both the in-plane and out-of-plane directions. Our analysis revealed that the conventional isotropic assumption tends to overestimate some values, particularly for the out-of-plane spin diffusion length and spin Hall conductivity. These findings provide new insight into anisotropic spin diffusion and spin-charge conversions in layered materials and emphasize the importance of accounting for anisotropic conductivity in the design of spintronic devices.

Cosmological reionization was a highly out-of-equilibrium event that affected every parcel of the intergalactic medium, making it a candidate for astrophysical generation of intergalactic magnetic fields. During reionization, the first stars and galaxies ionized the surrounding, largely neutral, medium in ever-expanding envelopes. Photoionization from sources on one side of the front, combined with the quadrupolar angular dependence of the photoionization cross section, leads to an anisotropic electron velocity distribution. We investigate instabilities in these reionization fronts as a mechanism to generate seed magnetic fields. The Weibel instability has the potential to create a magnetic field from these anisotropies. We calculate the magnitude of the isotropic and anisotropic distribution within a simulated reionization front. We find that the fractional anisotropy can grow to $6\times 10^{-3}$ toward the middle of the ionization front. We show that the linear growth timescale of the Weibel instability is fast compared to the crossing time of the ionization front ($\sim 2\times 10^5$ seconds). We briefly speculate on the possible non-linear evolution of the instability and the implications for cosmological magnetogenesis.

ISAC is an emerging paradigm in 6G networks that enables environmental sensing using wireless communication infrastructure. Current O-RAN specifications lack the architectural primitives for sensing integration: no service models expose physical-layer observables, no execution frameworks support sub-millisecond sensing tasks, and fronthaul interfaces cannot correlate transmitted waveforms with their reflections. This article proposes three extensions to O-RAN for monostatic sensing, where transmission and reception are co-located at the base station. First, we specify sensing dApps at the O-DU that process IQ samples to extract delay, Doppler, and angular features. Second, we define E2SM-SENS, a service model enabling xApps to subscribe to sensing telemetry with configurable periodicity. Third, we identify required Open Fronthaul metadata for waveform-echo association. We validate the architecture through a prototype implementation using beamforming and Full-Duplex operation, demonstrating closed-loop control with median end-to-end latency suitable for near-real-time sensing applications. While focused on monostatic configurations, the proposed interfaces extend to bistatic and cooperative sensing scenarios.

People with Blind Visual Impairments (BVI) face unique challenges when sharing images, as these may accidentally contain sensitive or inappropriate content. In many instances, they are unaware of the potential risks associated with sharing such content, which can compromise their privacy and interpersonal relationships. To address this issue, we investigated image filtering techniques that could help BVI users manage sensitive content before sharing with various audiences, including family, friends, or strangers. We conducted a study with 20 BVI participants, evaluating different filters applied to images varying in sensitivity, such as personal moments or embarrassing shots. Results indicated that pixelation was the least preferred method, while preferences for other filters varied depending on image type and sharing context. Additionally, participants reported greater comfort when sharing filtered versus unfiltered images across audiences. Based on the results, we offer a set of design guidelines to enhance the image-sharing experience for BVI individuals.

The development of user-friendly embedded prototyping systems like Arduino has made creating interactive devices more accessible. However, debugging these systems is challenging due to the intertwined nature of software and hardware issues. Existing tools often require hardware instrumentation or log visualization through serial monitors. To address this, the authors designed Inline, a programming tool that simplifies debugging by displaying hardware logs directly within the code, providing real-time execution flow tracking and an expression language for log manipulation. A study with twelve users demonstrated the tool's effectiveness in aiding debugging tasks.

We consider isomorphism of controllable graphs and cospectrality of distance-regularized graphs (which are known to be distance-regular or distance-biregular) in relation to logical definability. While most characterizations of these equivalence relations for such graph classes are of algebraic and spectral flavor, here we inject tools from first-order logic, extending and unifying several existing results.

Fine stratification survey is useful in many applications as its point estimator is unbiased, but the variance estimator under the design cannot be easily obtained, particularly when the sample size per stratum is as small as one unit. One common practice to overcome this difficulty is to collapse strata in pairs to create pseudo-strata and then estimate the variance. The estimator of variance achieved is not design-unbiased, and the positive bias increases as the population means of the paired pseudo-strata become more variant. The resulting confidence intervals can be unnecessarily large. In this paper, we propose a new Bayesian estimator for variance which does not rely on collapsing strata, unlike the previous methods given in the literature. We employ the penalized spline method for smoothing the mean and variance together in a nonparametric way. Furthermore, we make comparisons with the earlier work of Breidt et al. (2016). Throughout multiple simulation studies and an illustration using data from the National Survey of Family Growth (NSFG), we demonstrate the favorable performance of our methodology.

Qin established the geometric realization of entire quantum groups via perverse sheaves, which further give rise to dual canonical bases with integral and positive structure constants for quantum groups of type ADE. In this paper, we prove that the dual canonical bases of (Drinfeld double) quantum groups coincide with Berenstein--Greenstein's double canonical bases, by reinterpreting their intricate algebraic construction via the geometry of NKS quiver varieties. This result settles several conjectures therein, including those on positivity and invariance under braid group actions.

This paper is devoted to the investigation of multidimensional analogues of refined Bohr-type inequalities for bounded holomorphic mappings on the unit polydisc $\mathbb{P}Δ(0;1_n)$. We provide a definitive resolution to the Bohr phenomenon in several complex variables by determining sharp radii for functional power series involving the class of Schwarz functions $ω_{n,m}\in\mathcal{B}_{n,m}$ and the local modulus $|f(z)|$. By employing the directional derivative operator $\partial_uf(z) = \sum_{k=1}^{n} u_k \frac{\partial f(z)}{\partial z_k}$, where $u=(u_1,u_2,\ldots,u_n)\in\mathbb{C}^n$ such that $|u_1|+|u_2|+\ldots+|u_n|=1$, we obtain refined growth estimates for derivatives that generalize well-known univariate results to $\mathbb{C}^n$. The optimality of the obtained constants is rigorously verified, demonstrating that all established radii are sharp.

This study presents a new system performance function for process plant reliability analysis, formulated to capture both structural topology and process sequencing constraints. Built on a modified maximum-flow framework and solved via linear programming, the proposed function efficiently quantifies the maximum feasible flow through a series of interconnected stages. It addresses limitations of existing models, such as fault trees and event trees, that often overlook flow continuity and topological dependencies. The system function is integrated into a reliability assessment framework, enabling the evaluation of system failure probability and reliability-based component importance measures. Application to two benchmark examples, including a gas supply plant with 57 nodes and 102 edges, demonstrates the effectiveness of the proposed system function and the validity of the resulting reliability assessment for risk-informed layout planning. A reconfiguration guided by component importance measures yields up to a 20% reduction in system failure probability, underscoring the importance of effective equipment and pipeline layout. The proposed framework offers a promising direction for reliability-based management of industrial process facilities.

Relational Quantum Mechanics (RQM) treats quantum states as observer-dependent facts rather than absolute properties. While this relational stance is conceptually attractive, it raises concerns about empirical confirmation, particularly in multi-observer scenarios. Existing responses within RQM focus on securing agreement between observers by strengthening the status, stability, or accessibility of recorded outcomes. However, they leave open a more basic question: what grounds the persistence of an observer across time? Scientific observation presupposes stable records and the capacity to relate outcomes across successive measurements. We argue that the minimal definition of the observer in RQM as a merely interacting physical system is insufficient to support this requirement. We propose a complementary account of the observer that distinguishes physical interaction from informational coherence, and show how this distinction supports empirical confirmation in Wigner's friend-type scenarios.

Dedispersion is the computational process of correcting for the frequency-dependent time delay affecting a radio signal that propagates through the interstellar and intergalactic media. It is a crucial component of transient search pipelines that maximises the signal-to-noise ratio, especially when targeting highly dispersed signals: for instance, pulsar emissions making their way through a dense cloud of ionised gas, and fast radio bursts travelling cosmological distances. This paper introduces Streaming high Time-Resolution Imaging DEdispersion (STRIDE), a novel dedispersion algorithm to generate per-pixel dedispersed time series from high time and frequency resolution interferometric images. Unlike straightforward approaches to image dedispersion, STRIDE does not involve expensive manipulation of the input data layout, such as explicitly building dynamic spectra or shifting images. Furthermore, it is the first dedispersion algorithm to partition a dispersive sweep over the time dimension, in addition to frequency. As a consequence, images corresponding to the entire time span of the target dispersive delay are not required all at once. Instead, the algorithm works with an arbitrarily-sized subset of images at a time, adopting an incremental, streaming-based approach to dedispersion. In evaluating STRIDE on the presented test case, it is shown that the minimum memory requirement is reduced by 97.9%, going from 684.5 GB to 14.4 GB. As current and future generations of widefield interferometers increasingly turn to imaging techniques for detection and localisation of radio transients, STRIDE positions itself as a strong alternative to traditional dedispersion methodologies. It arguably is the only viable option for imaging-based searches with low-frequency instruments such as the Murchison Widefield Array (MWA) and low-frequency Square Kilometre Array (SKA-Low).

Detecting speech from biosignals is gaining increasing attention due to the potential to develop human-computer interfaces that are noise-robust, privacy-preserving, and scalable for both clinical applications and daily use. However, most existing approaches remain limited by insufficient wearability and the lack of edge-processing capabilities, which are essential for minimally obtrusive, responsive, and private assistive technologies. In this work, we present SilentWear, a fully wearable, textile-based neck interface for EMG signal acquisition and processing. Powered by BioGAP-Ultra, the system enables end-to-end data acquisition from 14 differential channels and on-device speech recognition. SilentWear is coupled with SpeechNet, a lightweight 15k-parameter CNN architecture specifically tailored for EMG-based speech decoding, achieving an average cross-validated accuracy of 84.8$\pm$4.6% and 77.5$\pm$6.6% for vocalized and silent speech, respectively, over eight representative human-machine interaction commands collected over multiple days. We evaluate robustness to repositioning induced by multi-day use. In an inter-session setting, the system achieves average accuracies of 71.1$\pm$8.3% and 59.3\pm2.2% for vocalized and silent speech, respectively. To mitigate performance degradation due to repositioning, we propose an incremental fine-tuning strategy, demonstrating more than 10% accuracy recovery with less than 10 minutes of additional user data. Finally, we demonstrate end-to-end real-time on-device speech recognition on a commercial multi-core microcontroller unit (MCU), achieving an energy consumption of 63.9$μ$J per inference with a latency of 2.47 ms. With a total power consumption of 20.5mW for acquisition, inference, and wireless transmission of results, SilentWear enables continuous operation for more than 27 hours.

Despite the success of large language models (LLMs) across domains, their potential for efficient channel state information (CSI) compression and feedback in frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems remains largely unexplored yet increasingly important. In this paper, we propose a novel LLM-based framework for CSI feedback to exploit the potential of LLMs. We first reformulate the CSI compression feedback task as a masked token prediction task that aligns more closely with the functionality of LLMs. Subsequently, we design an information-theoretic mask selection strategy based on self-information, identifying and selecting CSI elements with the highest self-information at the user equipment (UE) for feedback. This ensures that masked tokens correspond to elements with lower self-information, while visible tokens correspond to elements with higher self-information, thus maximizing the accuracy of LLM predictions.