arXiv:2607.16674v1 Announce Type: new
Abstract: Accurate routability estimation during physical design is important for reducing costly post-routing iterations. Prior learning-based methods treat this task as deterministic prediction, mapping placement-stage features to a single congestion or DRC outcome. We instead formulate routability estimation as a conditional generation problem, where both routing congestion and DRC violations are modeled as spatially structured routability fields. Our framework, Conditional Latent Diffusion for Routeability estimation (CLDRoute), uses physics-aware conditioning and task-specific latent modeling to handle the different characteristics of congestion and DRC maps. This allows our method to supports sample-based inference, producing both a mean prediction and a spatial uncertainty estimate for the same input design. On CircuitNet 2.0 (N28), our method achieves, for DRC violation generation, an SSIM of 0.9678, an MAE of 0.0028, and a TopK@1% of 0.3494; for congestion generation, it achieves an SSIM of 0.9031, an MAE of 0.0286, and an NZ-Pearson of 0.3692. Overall, our framework provides a more practical view of routability at placement by generating both the expected outcome and its uncertainty.
Science Journals
arXiv:2607.16384v1 Announce Type: new
Abstract: For stochastic gradient descent (SGD) with a constant stepsize $\alpha$, the invariant law of the iterates, centered at a minimizer, describes the behavior of the algorithm over long time horizons. In the strongly convex case, this invariant law has the familiar $\sqrt{\alpha}$ scaling and a Gaussian limit as $\alpha\downarrow 0$. We show that this behavior changes fundamentally for convex objectives $H$ with flat minima and (sub)quadratic tails.
More specifically, we study SGD with Markovian noise generated by a contractive driving chain. For every sufficiently small constant stepsize $\alpha$, we prove existence, uniqueness, and geometric convergence to an augmented invariant law in a Wasserstein distance induced by an $\alpha$-dependent metric. When the minimizer $x_\star$ has local flatness exponent $m\ge2$, meaning that $\nabla^2 H(x)\asymp \lVert x-x_\star\rVert^{m-2} I_d$ as $x\to x_\star$, we obtain a contraction bound with factor $1-c\alpha^{m-1}$, where $c>0$ is a constant. This recovers the factor $1-c\alpha$ in the quadratic case $m=2$. We then analyze the small-stepsize scaling limit. We show that the invariant law concentrates on the scale $\alpha^{1/m}$ and that the rescaled iterates converge weakly to the stationary distribution of the stochastic differential equation $$
dY_t=-h_0(Y_t)\,dt+\Sigma^{1/2}\,dB_t , $$ where $h_0$ is the limiting drift at the minimizer and $\Sigma$ denotes the asymptotic covariance. This recovers the Gaussian limit when $m=2$ and gives generally non-Gaussian stationary limits in the flat case $m>2$. Finally, we give corresponding results for coordinate-separable objectives with unequal flatness exponents.
arXiv:2607.17725v1 Announce Type: new
Abstract: The intricate motion arising from fluid--boundary interactions is visually compelling, yet notoriously difficult and computationally expensive to simulate in the presence of complex boundaries. Accurately resolving boundary geometry requires body-fitted grids constructed via cut-cell methods, which often leads to poorly conditioned linear systems in the pressure projection stage and, consequently, prohibitive computational cost. We present FastVEM, an efficient boundary-conforming fluid simulation framework that enables high-fidelity flow--boundary interaction at substantially reduced cost. Computational efficiency is achieved through a coordinated, top-down design spanning numerical discretization, grid construction, and linear solvers. FastVEM adopts a Virtual Element Method (VEM) discretization to robustly enforce incompressibility and boundary conditions on irregular body-fitted grids, and employs a VEM polynomial-space Particle-in-Cell scheme for advection. Complementing this discretization, a convexity-preserving cut-cell strategy is introduced to construct simulation-friendly body-fitted grids. To accelerate pressure projection, we develop a Galerkin geometric multigrid solver featuring a diffusion-free prolongation operator that prevents coarse-level matrix densification, along with a nested, boundary-aware grid hierarchy that ensures well-posed placement of coarse-level degrees of freedom. Compared to prior cut-cell--based fluid simulators, FastVEM speeds up the computationally dominant pressure projection stage by up to 100x, while robustly handling even more challenging boundary geometries.
arXiv:2607.16718v1 Announce Type: cross
Abstract: Mapping the dispersion of polaritons, hybrid quasiparticles arising from light-matter coupling, can provide key insights into the material dielectric response, coupling strength, and energy transfer pathways with other excitations. In this work, we present THz pump-Raman probe (TP-RP) as a versatile method for mapping the polariton dispersion in polar non-centrosymmetric materials, demonstrated here for the case of phonon-polaritons in LiNbO$_3$. By resonantly driving polaritonic modes with a broadband THz pump and probing them with a tunable NIR Raman pulse, TP-RP allows for the extraction of the momentum-dependence of both their frequency and damping rate with high accuracy. The spectral features observed in the pump-probe signal, including the polaritonic response as well as pulse artifacts, are reproduced within a many-body theoretical approach. Applying the technique to study the E(TO$_1$) phonon of LiNbO$_3$ enables the combined analysis of theory and experiments to uncover a nontrivial frequency dependence of the phonon intrinsic damping rate, revealing possible anharmonic couplings to other modes.
arXiv:2607.16725v1 Announce Type: cross
Abstract: Conditional generative modeling remains a challenging problem in semi-supervised settings where labeled data is scarce but unlabeled samples are abundant. To effectively leverage structural information embedded within the unlabeled dataset and compensate for sparse conditioning signals, we propose a semi-supervised framework combining conditional stochastic interpolation with low-dimensional latent representations. RepG decomposes generation into two stages: label-dependent latent sampling and high-dimensional reconstruction. This isolates the supervised learning of conditional dependencies to a low-dimensional space, requiring few labels while utilizing the abundant unlabeled data purely for reconstruction. Theoretically, we establish an error decomposition showing that the Kullback-Leibler divergence of RepG comprises stage-wise estimation errors and a structural bias quantified by conditional mutual information. For deep neural network estimators, we derive non-asymptotic convergence rates proving that RepG significantly improves sample complexity. By confining the supervised estimation burden to the low intrinsic dimension of the latent representation, RepG achieves a strictly faster convergence rate. Complemented by a minimax lower bound, our theoretical results demonstrate that this method effectively mitigates the curse of dimensionality inherent in direct ambient-space generative modeling.
arXiv:2607.16761v1 Announce Type: cross
Abstract: Dropout and Random Gradient Masking (RaM) are two training techniques used to improve performance in deep learning. Both techniques inject randomness into the training dynamics, but in significantly different ways: dropout applies random masks to the activations in the forward pass, whereas RaM leaves the forward pass unchanged and instead masks the gradients. In particular, the noise induced by RaM in the parameter updates is unbiased, so standard explanations for the effectiveness of dropout, such as the penalization effect or the prevention of co-adaptation between neurons, do not apply to RaM. In this work, we show that the difference between the two methods disappears for ResNets in the large depth and width asymptotics: in the complete feature learning regime, they both converge to the same large-scale limiting dynamics. This asymptotic equivalence holds for several variants of dropout and RaM, including layerwise dropout as used in stochastic-depth ResNets, albeit at slower quantitative rates. In fact, we also show that several of these variants collapse to the same limit asymptotically.
arXiv:2607.16766v1 Announce Type: cross
Abstract: Edge inference has emerged as a promising solution for the proliferation of artificial intelligence (AI) services by deploying models at the network edge to circumvent cloud-routing latency. Existing edge inference approaches mainly focused on either cooperative inference to reduce latency or lightweight model design to fit resource-constrained devices. These solutions often address the communication and computation challenges separately, and thus struggle to achieve a balanced trade-off among transmission efficiency, on-device processing cost, and inference accuracy. To bridge this gap, this paper proposes a hybrid-precision task-oriented communication framework for edge inference to holistically balance communication, on-device computation, and utility. In this framework, a binarized front-end is deployed on the edge device to extract and transmit binary features via orthogonal frequency-division multiplexing (OFDM) signals, while a full-precision back-end on the edge server performs the final inference. To ensure model consistency, we introduce an on-device binarization method tailored for split inference and develop an integrated channel-aware transmission scheme featuring subcarrier-based feature calibration. Furthermore, a knowledge distillation (KD)-based training strategy, supported by specialized gradient estimators, is developed to optimize the end-to-end system and inherit semantic knowledge from a full-precision teacher model. Extensive experiments on the large-scale ImageNet dataset demonstrate the superiority of the proposed hybrid system. Our analysis confirms that this design achieves an optimal trade-off among communication efficiency, on-device computational cost, and inference accuracy, outperforming existing edge inference solutions.
arXiv:2607.16783v1 Announce Type: cross
Abstract: We study thermal transport across copper-graphene-copper interfaces using Non-Equilibrium Molecular Dynamics (NEMD), focusing on the influence of finite domain length and domain configuration, including lattice initialisation and associated graphene wrinkling, on the predicted thermal conductivity and Kapitza resistance. In the literature, NEMD simulations identified trends in the Kapitza resistance of graphene-copper interfaces. However, the simulation outcomes and reliability may depend heavily on configuration choices that are underexplored in the literature. We identify a strong sensitivity of the Kapitza resistance to domain configuration choices that affect the lattice constants and atomic density. We show that two conventional lattice initialisation strategies yield a factor of two difference in the Kapitza resistance, despite differences of only a few per cent in the lattice parameters. This behaviour is accompanied by strain-dependent shifts in the graphene and copper phonon spectra, and by increased phonon overlap at lower strain. Counter to conventional expectations, greater phonon-mode overlap coincides with higher Kapitza resistance, showing that spectral overlap alone cannot capture the interfacial heat-transfer dynamics. We suggest that in lattices initialised with lower residual strain, a damping boundary layer develops near the interface, and increases thermal resistance, as indicated by increased local structural disorder and local spectral broadening over a wider interfacial region. Beyond strain- and density-related effects associated with the lattice constants, Kapitza resistance shows no significant dependence on domain length or boundary temperature enforcement in this study. By contrast, the copper lattice conductivity exhibits clear domain-size and temperature dependence, consistent with phonon mean-free-path limitations and supported by phonon spectral analysis.
arXiv:2607.16820v1 Announce Type: cross
Abstract: We investigate the existence and dynamics of two-dimensional solitary waves in a quantum droplet environment described by the extended Gross-Pitaevskii equation featuring logarithmic mean-field and Lee-Huang-Yang interactions. In the modulationally stable regime of the background, we employ suitable multiscale asymptotic methods to derive effective nonlinear integrable models corresponding to the Kadomtsev-Petviashvili and Davey-Stewartson equations. Based on these reduced models, we construct approximate analytical solutions describing line solitons, algebraically localized lump solitons, ring solitons, and exponentially localized dromions embedded on the droplet background. The dynamical robustness of these solutions is monitored through numerical simulations. Line, lump and ring solitons stay closest to the theoretical predictions, although progressively deviate due to the emergence of small-amplitude radiation, while dromions depart from their analytical waveform the most, although they roughly maintain their shape. Our results unveil unprecedented multidimensional soliton solutions in models featuring the competition of mean-field and quantum fluctuations and as such are amenable to current ultracold atom experiments.
arXiv:2607.17067v1 Announce Type: new
Abstract: Generative AI (GenAI) is reshaping software engineering, raising concerns about how the development pathway through which juniors become seniors is being eroded. While macro statistics show a decline in junior hiring and controlled studies demonstrate the effects of AI on individual task performance, the mechanisms through which GenAI reshapes early-career development in real organizational and educational contexts have not been thoroughly examined. Through 14 semi-structured interviews with juniors at the threshold of entering software engineering and senior software engineers in South Korea, analyzed using Reflexive Thematic Analysis, we reveal a foundational pattern of Absorption -- GenAI redirects entry-level work into senior-AI workflows -- and three consequences: (1) juniors losing the productive struggle through which expertise once developed; (2) the structural reproduction of this loss through collective normalization of GenAI use in university classrooms; and (3) the perceptual asymmetry between seniors and juniors that prevents either side from correcting these dynamics on their own. By extending learning theory and situated cognition to organizational and institutional scales, we argue that GenAI appears to be absorbing not just specific categories of tasks but also parts of the pathway through which the next generation of seniors is formed. Preserving this pathway will require deliberate institutional design across classrooms, workplaces, and the evaluation criteria for juniors.
arXiv:2607.17906v1 Announce Type: new
Abstract: Four-dimensional MRI (4D MRI) characterizes respiratory organ motion, yet existing reconstruction pipelines are tightly coupled to specific acquisition platforms (e.g., non-Cartesian trajectories with self-gating, vendor-specific navigators, or external respiratory hardware), limiting broad adoption across diverse clinical and research settings, including low-field, open-bore, and non-supine imaging. We present SIMPLE-4D (Surrogate-free, IMplicit, PortabLE 4D MRI), a software-first portable workflow that operates entirely on reconstructed slices from standard fast multi-slice 2D MRI and requires no pulse-sequence modification, no non-Cartesian trajectory, no navigator, and no external hardware. SIMPLE-4D combines an acquisition-agnostic front end consuming standard 2D protocols, a surrogate-free variational motion encoder that extracts a compact motion code directly from each 2D slice, and a physics-aware continuous spatio-temporal reconstruction based on a hash-encoded implicit neural representation (INR) with a SIREN deformation network producing bidirectional cycle-consistent DVFs and motion-dependent Gauss-Legendre thick-slice quadrature. Bidirectionality yields a complete inter-frame motion model by composition, supporting downstream tasks such as dose accumulation without retraining. We validate the identical pipeline on two contrasting datasets: a 1.5 T clinical bSSFP dataset (5 volunteers, 3 sessions each) and a 0.5 T open-bore HASTE dataset (5 volunteers, supine and upright). To our knowledge, this is the first per-frame 4D volumetric respiratory MRI reconstruction on a weight-bearing upright open-bore low-field scanner from reconstructed 2D Cartesian slices alone. On low-field data the INR template additionally acts as an implicit denoiser, yielding +132% SNR. Systematic ablations isolate each component's contribution.
arXiv:2607.16854v1 Announce Type: cross
Abstract: A hypergraph is \emph{linear} if every pair of vertices is contained in at most one hyperedge. For a family $\mathcal{F}$ of $r$-uniform hypergraphs, the linear Tur'an number $ex^{\mathrm{lin}}_r(n,\mathcal{F})$ is the maximum number of hyperedges in an $n$-vertex $\mathcal{F}$-free linear $r$-uniform hypergraph. Extending the work of Gy'arf'as, Ruszink'o, and S'ark"ozy on $3$-uniform linear hypertrees, we study linear Tur'an numbers for higher uniformity.
We determine the linear Tur'an number of the $r$-uniform linear star $S_k^r$, proving [ex^{\mathrm{lin}}_r(n,S_k^r)\le \frac{n(k-1)}{r},] with equality exactly for $(k-1)$-regular linear $r$-uniform hypergraphs, whenever they exist. We also construct dense $T_k^r$-free hypergraphs showing that, under suitable divisibility and design-existence assumptions, [ex^{\mathrm{lin}}_r(n,T_k^r)\ge \frac{n(k-1)}{r}] for every linear $r$-uniform hypertree $T_k^r$ with $k$ hyperedges.
We then study all linear hypertrees with four hyperedges. For the broom $B_4^r$, we prove [ex^{\mathrm{lin}}_r(n,B_4^r)\le \frac{(r+1)n}{r},] and characterize the extremal hypergraphs as disjoint unions of Steiner systems $S(2,r,r^2)$, whenever such systems exist. For the crown $E_4^r$, we establish [ex^{\mathrm{lin}}_r(n,E_4^r)\le \frac{(2r-1)n}{r},] together with a lower-bound construction leaving only a constant-factor gap.
For the linear path $P_4^r$, we construct $P_4^r$-free hypergraphs with $(r+1)n/r$ hyperedges and conjecture this is the optimal general bound. We verify the conjecture for connected hypergraphs under suitable degree conditions. Finally, for $r=4$, we identify counterexamples to a key structural claim in a previous proof of Zhang and Wang, give a new proof that [ex^{\mathrm{lin}}_4(n,P_4^4)\le \frac{5n}{4},] and show that equality holds precisely for disjoint unions of Steiner systems $S(2,4,16)$.
arXiv:2607.16877v1 Announce Type: cross
Abstract: The increasing complexity of next-generation wireless networks has driven the integration of artificial intelligence (AI) into wireless communications. However, most existing studies focus on developing task-specific deep learning techniques for single scenarios, which limits their ability to generalize across diverse tasks, channel conditions, and system configurations. To address this generalization bottleneck, we propose a hierarchical wireless foundation model (WFM) for multi-task optimization. The proposed WFM couples an upstream foundation channel encoder (FCE) with a downstream foundation optimization decoder (FOD) via geometry-aware cross-attention. Specifically, the FCE extracts task-agnostic channel representations via self-supervised masked reconstruction while the FOD generates multi-task optimization decisions through differentiable output heads. Moreover, a hybrid supervised-to-unsupervised training strategy is employed to overcome the performance ceiling of purely supervised learning, and the modular architecture of the WFM enables efficient adaptation to unseen communication tasks with minimal parameter overhead. Simulation results show that the proposed WFM learns high-fidelity channel representations and achieves competitive multi-task optimization performance while substantially reducing optimization inference latency relative to numerical baselines. Furthermore, it exhibits robust generalization to unseen propagation environments, varying constraint parameters, and heterogeneous system configurations.
arXiv:2607.17733v1 Announce Type: new
Abstract: 4-bit quantization enables efficient LLM inference, but suffers from significant accuracy degradation due to outliers. Prior work addresses this problem via data rotation or mixed-precision integer quantization, but often relies on software-managed scaling and frequent dequantization, incurring substantial overhead. Microscaling formats, such as MXINT, eliminate these inefficiencies by encoding scales in hardware, yet remain incompatible with rotation-based methods. Our analysis reveals that outliers vary in severity, from rare extremes to frequent mild deviations, and that quantization sensitivity is unevenly distributed across layers and columns. These insights motivate a fine-grained, sensitivity-guided approach. We introduce MXSens, a training-free method that assigns mixed mantissa bitwidths (4/6/8) based on column- and layer-wise sensitivity, naturally leveraging the block-wise structure of MXINT. MXSens outperforms state-of-the-art quantization methods across a range of models and tasks. Under the W4A4KV4 setting, MXSens achieves perplexities of 3.77 and 7.63 on LLaMA-2-70B and LLaMA-3-8B, respectively, substantially improving over existing baselines on WikiText-2. Our work establishes a new balance between accuracy and resource efficiency for LLM quantization.
arXiv:2607.16549v1 Announce Type: new
Abstract: Language models (LMs) are trained to excel at predicting the next word in the sequence given prior context, and humans also share this predictability in reading comprehension. Neuroscience research reveals that next-word predictability influences brain response, as recorded at millisecond resolution using electroencephalography (EEG). While our evidence indicates that advanced LMs achieve accuracies closely aligned with human performance at the next-word prediction task, this raises the question: Does higher prediction accuracy necessarily mean that these models adequately capture the cognitive signals associated with human reading comprehension? Here, we generate regressors for both humans and LMs based on two information measures, including top-1 prediction and surprisal, to predict event-related potential (ERP) elicited from EEG recordings which reflect different stages of cognitive processing during reading. We argue that modelling ERP patterns offers fine-grained analysis of the cognitive plausibility of various LMs during reading. Our results indicate that only surprisal potentially correlates with language-processing ERPs, especially for open-class words with high semantic content. Moreover, our findings challenge the assumption that scaling LMs with increased parameters and computational budgets will consistently lead to improved convergence with human-like linguistic processing.
arXiv:2607.16822v1 Announce Type: new
Abstract: This work proposes a compact and hardware-efficient frequency- and radiation-pattern-reconfigurable antenna (FPRA) for passive multi-target direction-of-arrival (DOA) estimation using a single receive RF chain. The antenna consists of a sectorized circular patch loaded with 16 PIN diodes. By switching the diode states, the current-concentration boundary on the patch is shifted, enabling reconfiguration of both the operating frequency and radiation pattern. With a single receive RF chain, the proposed antenna achieves beam scanning from -40 degrees to 40 degrees and provides multiple operating frequencies across the S- and C-bands. Based on these reconfigurable observation states, radiation-pattern switching is used to emulate the spatial sampling of a conventional antenna array, while multi-frequency observations introduce phase diversity to reduce the correlation among echoes from multiple passive targets illuminated by the same transmitter. Experimental results demonstrate that the combined virtual spatial sampling and frequency diversity enable passive multi-target DOA estimation without a conventional antenna array or multiple receive RF chains. The proposed FPRA offers a compact and hardware-efficient sensing solution for future integrated sensing and communication (ISAC) systems.
arXiv:2607.16886v1 Announce Type: cross
Abstract: Optical microcavities are widely used to control the emission of organic semiconductors, but their ability to reshape the molecular pathways that precede emission remains largely unexplored. Here we show that embedding a ZnPc:TPBi blend in a planar Fabry-P\'erot microcavity suppresses the formation of non-radiative exciplexes and removes bimolecular annihilation at high excitation densities, increasing the photoluminescence quantum yield by more than forty-fold under continuous-wave excitation. This enhancement is far larger than expected from the weak Purcell effect. Instead, transient spectroscopy, power-dependent photoluminescence and kinetic modelling point to a cavity-induced rebalancing of excited-state populations: long-range F\"orster energy transfer from ZnPc monomers to emissive aggregates is enhanced, allowing it to outcompete charge transfer to dark exciplexes. Electromagnetic calculations predict FRET enhancements of up to ~400-fold at relevant distances, consistent with the observed suppression of exciplex-mediated losses. Our results show that optical cavities can control not only how molecules emit, but also which excited states they form, opening a route to improved efficiency and reduced roll-off in organic optoelectronic and photonic devices.
arXiv:2607.16925v1 Announce Type: cross
Abstract: This paper presents a novel intelligent chaotic-based code-division multiple access (CDMA) scheme for underwater optical wireless communication (UOWC), addressing critical performance degradation caused by severe scattering and multipath dispersion in underwater environments. Unlike conventional modulation techniques such as on-off keying, which depend on precise pulse timing and show high sensitivity to channel distortions, the proposed approach leverages unpredictable deterministic chaotic sequences generated by the logistic map to enhance robustness against scattering-induced impairments. A Multi-Agent Reinforcement Learning (MARL) framework enables distributed agents to dynamically adapt chaotic map parameters, including initial conditions and bifurcation parameters, based on real-time environmental feedback, optimizing sequence generation to maintain low cross-correlation properties and improve resilience to multipath effects. Experimental validation using a 2-meter water tank testbed with controlled turbidity demonstrates superior performance compared to conventional schemes. The adaptive framework exhibits rapid convergence and relaxed synchronization requirements, making it highly suitable for distributed underwater networks where centralized coordination is impractical.
arXiv:2607.16934v1 Announce Type: cross
Abstract: Traditional randomized trials often obscure clinically meaningful heterogeneity in treatment response by focusing on average effects. Leveraging real-world data to emulate clinical trials and estimate heterogeneous treatment effects (HTEs) offers a promising path toward more precise and efficient trial design. In this study, we emulate the DAPA-HF trial using electronic health records from the Mayo Clinic Cloud (MCC) to investigate whether HTE-guided stratification can identify patient subgroups with distinct treatment responses to dapagliflozin versus placebo in patients with heart failure with reduced ejection fraction. All-cause mortality was evaluated using Cox proportional hazards models, with HTEs estimated using a Meta-S learner and subgroups defined using a decision tree-based thresholding approach. In the overall cohort of the emulation, no significant treatment difference was observed (HR, 1.681; 95% CI, 0.828-3.413; p = 0.1507). However, compared with the overall emulated cohort, in which dapagliflozin showed no statistically significant survival benefit, HTE-driven stratification identified subgroups with significant and directionally distinct treatment effects. The beneficial (low-HTE) subgroup showed a significant survival benefit from dapagliflozin (HR = 0.203, 95% CI, 0.087-0.476, p = 0.0002), whereas the harmful (high-HTE) subgroup showed a significant harmful association with markedly increased mortality risk (HR = 6.680, 95% CI, 2.759-16.171, p < 0.0001). These findings indicate that HTE-guided stratification can uncover clinically meaningful beneficial and harmful treatment-effect patterns that are masked in the full-cohort emulation.
arXiv:2607.16945v1 Announce Type: cross
Abstract: We study algebraic geometry codes on hyperelliptic curves of genus $g \geq 2$ with complementarity properties. Our first contribution is a characterization of non-special divisors of degree $g$ and $g-1$ via the polynomial degrees of their reduced Mumford representation, reducing a classical hard geometric problem to a single-degree test on univariate polynomials. Using this, we construct Linear Complementary Pairs (LCP) of codes via polynomial arithmetic on the Jacobian and provide a criterion in terms of Mumford degrees for the resulting codes to be Maximum Distance Separable (MDS). Under a $2$-torsion condition in the Jacobian, equivalently a divisibility condition on the Mumford polynomials, we obtain explicit multipliers that turn these pairs into Linear Complementary Dual (LCD) codes. Finally, we apply this framework to the maximal hyperelliptic curve $\mathcal{X} \colon y^2 = x^q + x$ over $\mathbb{F}_{q^2}$ and give explicit examples of MDS LCD codes with parameters $[2q,q,q+1]_{q^2}$ for $q = 4, 5, 7$, verified computationally; we conjecture, with heuristic support, that such codes exist for all $q \geq 4$.
arXiv:2607.18118v1 Announce Type: new
Abstract: Strong-field photoelectron holography encodes ultrafast electron dynamics through momentum-space interference. However, the orbit-resolved origin of spider-like spin fringes and the mechanism underlying their target dependence remain unclear. Here, we resolve both issues by analyzing photoelectron spin textures generated during tunneling ionization. We use the Coulomb quantum-orbit strong-field approximation, benchmarked against time-dependent Schr\"odinger equation simulations for $\mathrm{He^+}$ and Xe, to separate orbital-channel and quantum-orbit contributions. Spider-like fringes arise from interference between $p$-orbital ionization channels with different magnetic quantum numbers within an individual orbit class and therefore do not require interorbit interference. The observable polarization along these fringes, however, depends on the balance among orbit-class contributions. The decomposition associates the opposite first-leg polarizations of $\mathrm{He^+}$ and Xe with different relative weights of laser-deflected and forward-scattered trajectories, consistent with target-dependent Coulomb focusing. Photoelectron spin textures thus complement momentum distributions as probes of Coulomb-driven strong-field dynamics.
arXiv:2607.18138v1 Announce Type: new
Abstract: The rapid development of agentic AI and multi-agent systems is establishing AI agent communication as a fundamental requirement for the future Internet. While a diverse array of agent communication protocols has recently emerged, these solutions currently suffer from interoperability crises and infrastructure gaps. The newly proposed Service-Oriented Virtualization-Based Architecture (SOVA) offers an architectural framework to address these challenges for agent communication, which expects seamless support from the network infrastructure. The emerging AI-native 6G network is promising as a robust foundation for the SOVA framework, thereby greatly facilitating AI agent communication; however, its effectiveness in supporting the SOVA framework has yet to be fully assessed. To bridge the distinct research trajectories of AI-native 6G networks and AI agent communications, this paper investigates the capabilities of current and proposed 6G network architectures and protocol specifications for supporting the SOVA framework for AI agent communications. By critically examining 6G's key architectural paradigms and their potential to fulfill SOVA's requirements, this paper identifies gaps between 6G standards and the demands of AI agent communication. Based on this gap analysis, this paper outlines research and development directions to ensure that the future 6G network can natively empower AI agent communications in the era of agentic AI.
arXiv:2607.16962v1 Announce Type: cross
Abstract: The tuned mass damper inerter (TMDI) is a passive vibration absorber that suppresses structural vibrations by leveraging the inertance property of inerter devices. Previous studies have demonstrated TMDI effectiveness in wind turbines, but relied on simplified structural models. To date, no study has implemented multi-directional TMDIs within aero-hydro-servo-elastic tools such as OpenFAST to enable independent vibration control across multiple turbine components, including the tower, blades, and substructure. This paper addresses this gap by implementing the TMDI in OpenFAST. The governing equations of motion are derived and implemented within the Structural Control (StC) Module, supporting arbitrary TMDI connectivity and independent multi-directional configurations across turbine components. Verification against independent numerical models confirms the implementation's accuracy across all supported configurations. An application study examines the IEA 15MW reference wind turbine equipped with a tower-top TMDI under combined wind-wave loading. Optimal tuning is performed using a simplified two-degree-of-freedom model, with modal properties extracted from an OpenSeesPy finite element model of the IEA 15MW. Results show that TMDIs with secondary mass up to 100 times smaller than a conventional TMD achieve matching or superior vibration suppression in the fore-aft and side-side directions, in terms of peak and standard deviation of tower-top displacement and acceleration. Notably, TMDI stroke is markedly reduced compared to the TMD, better suiting the spatially constrained wind turbine environment. These results support the practical merit of lightweight TMDIs for wind turbine vibration control, with the OpenFAST implementation enabling performance-driven design under realistic loading conditions.
arXiv:2607.17354v1 Announce Type: cross
Abstract: We investigate colored $\Delta_T$ noise, i.e., finite-frequency $\Delta_T$ noise, as a probe of edge-mode (EM) transport in quantum Hall and quantum spin Hall systems. Colored $\Delta_T$ noise probes finite-frequency nonequilibrium current fluctuations and dynamical transport properties that are often obscured in DC measurements of conductance and noise. Since $\Delta_T$ noise is driven solely by a temperature and voltage bias under zero average charge current conditions, it eliminates current-induced Joule heating and directly probes intrinsic thermal fluctuations. We show that chiral, spin-conserving helical, and spin-flip helical (trivial) EMs exhibit distinct colored $\Delta_T$-noise signatures under appropriate bias protocols. Incorporating energy-dependent scattering through a quantum point contact, we demonstrate that electron-hole asymmetry significantly modifies the finite-frequency spectrum while preserving these distinguishing features. Notably, colored $\Delta_T$ noise exhibits a frequency-dependent sign reversal absent in the corresponding white ($\omega=0$) $\Delta_T$ noise. We further investigate zero-temperature colored quantum shot noise and find that it vanishes identically for chiral EMs, whereas the spin-conserving helical response changes sign with frequency. By contrast, spin-flip helical (trivial) EMs exhibit a positive colored shot-noise spectrum. However, the corresponding colored $\Delta_T$ noise retains its characteristic sign reversal, providing a robust distinction between spin-conserving helical and spin-flip helical (trivial) EM transport. These results establish colored $\Delta_T$ noise as a robust, experimentally accessible, complementary probe for identifying chiral, spin-conserving helical, and spin-flip helical (trivial) EM transport in mesoscopic topological systems.
arXiv:2607.17140v1 Announce Type: new
Abstract: Unified multimodal models (UMMs) aim to integrate visual understanding and generation within a single architecture, but architectural unification alone does not ensure semantic consistency. A model may describe the intended target correctly while generating an inconsistent edit. This exposes an understanding-generation alignment gap: linguistic and visual outputs live in different spaces, yet should be governed by the same target semantics. We study this gap in image editing, where an instruction defines a target state that can be both described and visually realized. Given a source image and an edit instruction, we compare a UMM's target caption with its edited image to test whether the two outputs converge on the same result. Our analysis shows that existing UMMs remain weakly aligned, especially for fine-grained entities, attributes, spatial relations, and local details, indicating that semantic unification is not achieved by architecture alone. To bridge this gap, we propose STBridge, a shared-target alignment framework that connects understanding and generation through a common target state. Here the target caption expresses the desired visual result, while the edited image realizes it visually, replacing separate task-specific paths with a shared information flow from target expression to target realization. STBridge follows an align-then-optimize strategy: supervised fine-tuning first establishes the shared-target channel, and sequential reinforcement learning further refines target-centered coordination. Across visual understanding, image generation, and image editing benchmarks, STBridge consistently improves over the initialization model. Alignment analysis confirms that STBridge narrows the gap between what the model describes and what it generates, demonstrating shared-target alignment as an effective post-training strategy for bridging understanding and generation in UMMs.