Papers

Materials synthesis planning (MSP) connects AI-driven materials discovery to experimental realization by proposing precursors, synthesis operations, and processing conditions for a target material. Existing approaches predict these components separately or in pairs and treat conditions only as a prediction target. Consequently, MSP remains centered on recipe prediction: it leaves unexplored how conditions can serve as process context for constructing synthesis routes, and it does not address which candidate routes should be executed under limited experimental resources. We propose CondMSP, which extends MSP from recipe prediction to execution-aware planning. CondMSP predicts temperature, time, and atmosphere by combining a joint predictor with variable-specific experts, and then uses these predictions both as process context, grounding the generation and retrieval of operation sequences, and as execution requirements, specifying which thermal stages can share a furnace run and at what cost. This specification is held fixed across all compared methods; within it, CondMSP ranks routes across precursor sets by a common utility and, under furnace budgets, jointly selects routes and allocates their thermal stages by constrained optimization. CondMSP improves condition and operation prediction and, under this shared constraint, raises reference-recipe recovery from 27.9% to 32.9% at three simulated furnace runs per target and from 34.7% to 40.0% at 30 trials, while a new evaluation set of 2,344 targets supports generalization beyond oxide-dominated training chemistry.

In order to reproduce experimental photoemission spectroscopy (PES) data by using band structure calculations with linearized augmented-plane-wave method, we developed a new method to calculate the atomic-subshell photoelectron differential cross section (PE-DCS) of the valence band. PE-DCS was calculated to be the sum of the products of the partial density of states (PDOS) and the photoionization differential cross section (PI-DCS) of each atomic subshell. To construct an appropriate PDOS beyond the conventional muffin-tin sphere (MTS) PDOS, we proposed a “nearly-neutral atomic sphere", which can be defined as a natural extension of MTS. Although the calculational method for atomic-subshell PI-DCS was the standard Hartree-Fock-Slater method, we developed a new computer code in order to deal with the unoccupied electron shells in the ground state of isolated atoms up to $\sim$10 keV. Details of the PE-DCS calculations were examined by using two simple examples of Na metal and NaCl, and then, the method was applied to hard x-ray PES (HX-PES) data of CuAlO$_2$. The experimental spectra were satisfactorily reproduced, demonstrating the potential applications of our method to HX-PES spectra of many compounds.

Can quantum communication determine the size of an anonymous network when no size bound is known? Classical impossibility proofs rely on the covering lemma: an execution can be reproduced on a larger network with the same local structure. Quantum interference invalidates this argument - a measurement record possible on a network can become impossible on a cover. We show that the impossibility of exact counting nevertheless survives. Our main result is a quantum analogue of the covering lemma that preserves local possibilities rather than complete executions. Within a cyclic covering family, every local measurement outcome possible at a node remains possible at a corresponding node in every sufficiently large network. This holds even after information has travelled around the smaller network, beyond the reach of a simple locality argument. We extend the principle to the universal covering tree and finite covers with sufficiently long cycles. The proof combines tensor-network methods with graph coverings: a common representation across network sizes turns normalization into a constraint on which local outcomes can occur. For uniform synchronous protocols with independently prepared nodes, this support-lifting principle rules out zero-error counting and even the production of a guaranteed finite size bound on anonymous rings and the other cyclic covering families considered here. It also rules out exact leader election with termination in finite time with probability one on these families. Measurements, classical control, and local memory growing with time are allowed; no predetermined running-time bound is assumed. Quantum interference thus defeats execution lifting without enabling infallible certification of network size.

Wearable muscle sensing for rehabilitation, assistive robotics, and electrical-stimulation feedback needs mechanomyography sensors less sensitive to fit changes during wear. In microphone-based mechanomyography, cavity pressure depends on rim contact, a dependence housing design has rarely targeted. This study evaluates a side-vented housing, whose vent opens a pressure-relief path in parallel with the rim to lower contact dependence, and quantifies the acoustic-gain cost. Thermoviscous finite-element simulations compared twenty cavity configurations under prescribed source motion. Paired bench sweeps over 10-200 Hz on both housings compared direct contact with a Gap-ring spacer, a radially grooved spacer ring. At 100 Hz, side venting reduced the simulated acoustic-gain variation across 0.10-0.50 mm rim clearances from 25.8 to 10.0 dB and cost 5.5 dB of gain at 0.20 mm clearance. Velocity fields and branch-flow calculations identified this path. At the higher speaker drive level, the Gap-ring spacer caused median attenuations of 13.6 dB (vented) and 30.1 dB (unvented) relative to direct contact. In this front end, a 35.9-dB signal-gain increase at 100 Hz raised the calculated operational-amplifier voltage-noise gain by 1.6 dB. A single-participant demonstration of paced elbow flexion and extension compared envelope timing and spectra between housings, finding similar envelope timing (correlation 0.935) and broadly similar spectra (correlation 0.956). Vent area trades acoustic gain against contact dependence and can therefore serve as a housing design parameter.

We construct smooth plane curves whose logarithmic contours are smooth and embedded but strictly larger than their amoeba boundaries. The examples include connected irreducible curves, disconnected unions of nonlinear components with two-dimensional amoebas, nested families with arbitrarily many components, and a uniform construction realizing every positive genus. Exact critical-locus calculations distinguish the boundary-producing folds from compact or noncompact contour branches lying in the amoeba interior. These constructions show that smoothness of the complete contour does not force contour–boundary equality and that internal critical-value branches persist under monomial transformations, controlled perturbations, and finite monomial coverings.21 pages, 14 figures

What makes natural languages diversify, in their syntactic structure, even when sharing a largely common origin? We refine our simple model, which focuses on the glassy dynamics induced by a network of interactions between syntactic parameters. Distinguishing between symmetric interaction weights that can be inferred from the observed statistics of syntax across many languages, and additional asymmetric weights assigned at random, we show that both components are needed, with similar overall strength, to account for plausible long-term syntactic dynamics. An approximate balance can combine the glassy metastability provided by the inferred weights with the chaotic diversification induced by the residual asymmetric weights.

This paper is a continuation of Part~I \cite{QTWPartI}, where blow-up analysis and compactness results for the CR Nirenberg problem were established. We prove some existence and degree results for the CR Nirenberg problem on the standard sphere. More precisely, we first prove existence results under antipodal and, more generally, finite-group symmetries. Without symmetry, we derive a Leray–Schauder degree formula under natural flatness conditions, yielding existence results for positive prescribed curvatures with certain homogeneous expansions at critical points. We also obtain Aubin-type inequalities under a vanishing first-moment condition, uniformly near the critical exponent.

We study constant-round LOCAL approximation for additive local vertex-subset minimization problems. Given a $k$-uniform $\alpha$-approximation on a hereditary source class closed under disjoint union, Bonamy, Das, Gavoille, Picavet, Suomela, and Wesolek obtained ratio $\alpha(d+1)+1$ on locally nice target classes of asymptotic dimension $d$. We prove two transfers with ratios $\alpha+1+\eta$ and $\alpha+\eta$ for every $\eta>0$. These bounds require the stronger local niceness assumption at a larger scale that depends on $\eta$. The first algorithm keeps their repair construction and increases the radius used to detect errors. We average over partitions of the graph to bound the retained cost without the factor $d+1$. These partitions are used only in the analysis. The second algorithm tests several deletion widths around the errors. In each component of the repair zone, it chooses a smallest union of retained choices and an exact repair. Averaging over the widths bounds the cost counted twice and yields ratio $\alpha+\eta$. Both transfers are deterministic and computable, use arbitrary distinct numerical identifiers, and need no knowledge of the graph size. Their outputs also satisfy local uniformity guarantees. For 2-domination on graphs of bounded Euler genus, the same planar source gives ratio $19$ through the earlier transfer and $6+\eta$ through ours. For minimum dominating set, our transfer gives ratio $7+\varepsilon$ on graphs formed by attaching a clique of arbitrary size at one vertex of a planar graph, using the source algorithm of Amiri and Siebertz. This class satisfies our target assumptions and allows nonempty error sets. Its Euler genus is unbounded, so their guarantee for graphs of bounded Euler genus does not cover the whole class.

In real-world indoor fire environments, visual degradation severely impairs instruction grounding, waypoint prediction, and long-horizon exploration in vision-language navigation (VLN). Although image restoration offers a natural means to mitigate such visual degradation, existing methods primarily optimize reconstruction fidelity, which does not necessarily translate into better navigation decisions and may even corrupt task-critical cues when applied indiscriminately. In this paper, we present FireNav, a decision-oriented reconstruction evidence routing framework that aligns visual restoration with navigation utility in VLN. To advance this task, we first contribute FireVLN-Bench, a paired indoor fire navigation benchmark with clean references preserving task semantics, safety supervision, and matched settings for continuous navigation and robot execution with simulated dynamics. Our FireNav retains native visual tokens and treats latent reconstruction features as auxiliary evidence, where a lightweight router leverages visual features, task context, and prior execution states to determine whether such evidence should be admitted and how strongly it should contribute to the visual tokens. To learn when reconstruction is beneficial, we further introduce decision utility calibration, which compares action prediction losses with and without candidate evidence under the same state, thereby supervising evidence selection according to its contribution to waypoint, heading, and arrival prediction. Experiments demonstrate that FireNav consistently outperforms state-of-the-art baselines in both navigation success and path efficiency across evaluation settings.

We establish global well-posedness and sharp higher-order decay estimates for the fractional compressible Navier–Stokes–Poisson system in $\R^{3}$ with fractional Lamé–Navier dissipation $\Lame^\alpha u$ with $\alpha\in[1/2,1]$, and Riesz coupling $\Lambda^{2\beta}\phi=\rho-\bar{\rho}$ with $\beta\in[0,1]$, where $\Lambda:=(-\Delta)^{1/2}$ and $\bar{\rho}>0$ denotes the constant background density. For small perturbations of a constant equilibrium in $H^{N\alpha}$ with $N\geq3$ and $N\alpha>5/2$, we prove the global existence and uniqueness of classical solutions. Furthermore, we propagate the negative Sobolev norms with uniform-in-time bounds and derive the higher-order decay estimates, provided that the initial perturbation additionally belongs to $\dot{H}^{-\vartheta}$ for some $\vartheta\in[0,3/2)$. Moreover, we establish the optimality of these decay rates within the prescribed initial data class for the velocity and the Riesz potential in their natural energy norms. The density decay exponents are also optimal whenever the available regularity permits the full Riesz-induced gain. The analysis relies on a nonlinear energy structure adapted to both fractional dissipation and Riesz interaction, together with Sobolev interpolation. The optimality of the decay is proved by constructing smooth initial data localized at low frequencies and proving matching upper and lower decay bounds for the resulting solutions. These solutions show that the low-frequency linear dynamics including the Riesz-induced density gain, persists under nonlinear evolution.

An elementary space is a pair $(X, \mathcal A)$, where $\mathcal A$ is an algebra of subsets of $X$. Every elementary space $(X, \mathcal A)$ is naturally a topological space when equipped with the topology $\tau_{\mathcal A}$ generated by $\mathcal A$. Elementary mappings on elementary spaces are defined in a manner analogous to measurable mappings on measurable spaces. In addition, the notion of an extension of an elementary space is introduced. Let $M(X, \mathcal A)$, denote the ring of all real-valued elementary functions on $(X, \mathcal A)$. In this article, we study the ring $M(X, \mathcal A)$ and extensions of an elementary space. We show that, for any elementary space $(X, \mathcal A)$, there exists a unique compact elementary extension $(\beta_{\mathcal A}X, \bar{\mathcal A})$ of $X$, analogous to the Stone-$\check C$ech compactification, which contains every other extension of $X$. This extension has the property that every elementary map from $X$ into any compact topological space extends continuously to $\beta_{\mathcal A}X$. Next, we show that $\beta_{\mathcal A}X$ contains a realcompact extension $(\upsilon_{\mathcal A}X, \bar{\mathcal A}_R)$ of $X$, such that every elementary map from $X$ into any realcompact topological space extends continuously to $\upsilon_{\mathcal A}X$. Moreover, $\upsilon_{\mathcal A}X$ is the smallest realcompact extension of $X$ and $(X, \mathcal A)$ is realcompact if and only if $X=\upsilon_{\mathcal A}X$. Also, we see that, every metric space equipped with the $\sigma$-algebra of all Borel sets is realcompact. Finally, we observe that the maximal ring of quotients of $M(X, \mathcal A)$ is the direct limit of locally constant (continuous) functions on dense open subsets of $(X, \tau_{\mathcal A})$. Some questions raised in the literature are also answered.

We provide a simplified and more human-friendly version of the proof of the Deligne–Drinfeld conjecture recently given by OpenAI.

Accurate, robust, and efficient interatomic potentials are highly desirable for large-scale molecular dynamics simulations. Although machine-learning interatomic potentials can reproduce first-principles potential-energy surfaces with high accuracy, they typically incur higher computational costs than conventional empirical potentials and may be less reliable outside their training domains. We developed Tersoff-NN, a hybrid machine-learning potential for Si, which retains the analytical radial interactions and cutoff structure of the Tersoff potential while representing its environment-dependent bond-order factor using compact neural networks (NNs). Benchmarks for 32,768-atom systems show that Tersoff-NN is 4-12 times faster than the Behler-Parrinello-type NN potentials tested in this study. Tersoff-NN improves the short-range structure of liquid Si relative to the conventional Tersoff potential, closely reproduces the ab initio radial and angular distributions, and reproduces the equilibrium structure of diamond Si and qualitative energetic trends among several crystalline phases. Furthermore, melt-quench simulations reveal a disordered solid with predominantly four-coordinated, tetrahedral-like local environments. Additionally, characteristic reconstruction motifs are retained on Si surfaces despite their exclusion from the training dataset. A direct comparison with an alternative atom-centered bond-order NN highlights the critical role of physical constraints in ensuring dynamic stability. Despite exhibiting comparable validation errors, the selected triplet-based architecture remains stable, while an alternative model exhibits unphysical bond-order growth and atomic collapse. These results confirm that learning only the bond-order term improves the description of diverse Si environments while retaining substantial computational efficiency and enhancing dynamical stability.

In practical engineering and scientific computing, a real physical system can be reduced to a boundary value problem, and the adequate boundary information to determine the interior spatial distribution and evolutionary state of the physical field can be solved with linear equations, alternatively, the the real physical problem can be treated with boundary problems with in adequate information problems in field of machine learning. This paper proposes a deep learning framework for the recovery of missing boundary information using an attention-guided graph neural network (GNN) surrogate. The framework extracts features from the physical information on the discretized boundary into a latent space, and the missing boundary quantities are then predicted by the network. Thus, the nonlinear mapping from known boundary quantities to missing boundary quantities is learned using a variational autoencoder (VAE) architecture, while an attention mechanism is incorporated to better model the correlations among different types of boundary information. Furthermore, the interior physical field can be reconstructed using the conventional boundary element method (BEM) integral formulation. The effectiveness of the GNN-BEM framework is evaluated using numerical examples of the two-dimensional Laplace and Poisson equations, which demonstrate good performance in reconstruction accuracy, local detail preservation, and model stability. The results show that GNN-BEM provides a feasible computational framework for boundary information recovery and fast interior physical field reconstruction for partial differential equations under complex boundary conditions.

Many deployed distillation pipelines show the student a truncated view of the teacher: keep the top-k probabilities, renormalize, and minimize a divergence on what remains. Renormalization is assumed to account for the missing mass. We prove that it removes the visibility of tail mass, not the freedom to move it. The renormalized objective depends only on log-odds among the retained tokens, so at each position its gradient along the directions that control tail mass is exactly zero; on a real teacher that component measures $2$-$3\times10^{-15}$ of the gradient. The full-vocabulary gradient equals the truncated gradient plus a residual weighted by the tail masses, and training acts only on the first term. Without the cross-entropy term, a top-k student's tail mass drifts away from the teacher's while a full-vocabulary control moves slightly toward it; with it, the gap shrinks about fourfold but persists. On seven open-weight teachers, the retained set determines the teacher's distribution only from $k=d+1$ ($d$ the hidden dimension): recovery is exact from there on all seven, and commonly cached sizes sit below it. In our off-policy runs the practical effect is real but smaller than the choice of teacher-student pair. Renormalization is a normalization, not a safeguard. The code is available at https://github.com/KurbanIntelligenceLab/top-k-distillation.

We study the initial boundary value problem for the compressible Navier-Stokes-Poisson equations without damping in the three-dimensional half-space. The velocity satisfies the no-slip boundary condition, and the electric potential satisfies the homogeneous Neumann boundary condition. For small perturbations of a constant state in $H^3$, with a small initial electric field in $L^2$, we establish global existence and uniqueness of strong solutions. The uniform estimates combine density dissipation, tangential and normal derivative estimates, and a modified energy that accounts for the electric boundary contribution through the Helmholtz projection. We also prove algebraic decay under an additional low-frequency assumption on the initial data. If the density perturbation, velocity and electric field have finite tangential negative Sobolev norms of order $-s$, where $1/2\leq s<1$, then the solution decays at the rate $(1+t)^{-s/2}$ in the full energy norm. The decay estimate follows from the formulation in terms of the momentum $\rho u$, uniform negative-order estimates and interpolation with the dissipation.

We construct a positive-definite symmetric integral $12\times12$ matrix $A$ such that, for every solution $Q$ of Nahm's equations $1-Q=Q^A$ with $Q_i\ne0,1$ for all $i$, the associated element $\sum_i[Q_i]$ is torsion in the Bloch group of $\mathbb{Q}(Q_1,\ldots,Q_{12})$. However, the Nahm sum $F_{A,B,C}(\tau)$ is nonmodular for every $B\in\mathbb{Q}^{12}$ and $C\in\mathbb{Q}$. Thus the torsion condition for all solutions in Nahm's conjecture is not sufficient for the existence of a modular Nahm sum.

Sample-based quantum linear solvers measure quantum Krylov states $e^{-iAT}|b\rangle$ and reconstruct the solution of $Ax=b$ classically on the observed basis states. We develop this approach for positive-definite systems that arise from elliptic partial differential equations. We show that the sampling guarantees hold in any fixed basis with an efficient transform. We then replace the variational reconstruction by a Galerkin solve on the sampled set. For this reconstruction we derive expected and high-probability energy-norm error bounds, a sample complexity in terms of the smallest sampling probability on an essential set, a computable residual certificate, and a support-completeness theorem for the dephased outcome law. We formulate the method for elliptic problems on Riemannian cross sections. In two dimensions the stiffness matrix is metric-independent in conformal coordinates, and curved vessels lead to the same matrix class. Killing symmetries of the cross section give selection rules for the measurement outcomes, and the Lie derivative of the metric gives first-order bounds for geometric perturbations. We separate operator approximations, whose effect we bound, from sampler approximations, which affect coverage but not the target. As examples we treat the implicit step of pulsatile flow in elliptic, lens-shaped, and curved ducts. In simulations of up to 12 qubits, the number of Fourier modes required for 1% error grows from 14 to 60 while the number of unknowns grows from 64 to 4096. A classical residual-greedy selection is nevertheless more efficient than quantum sampling in all cases studied, consistent with the low measured sampling efficiency. On a six-qubit trapped-ion processor, the measured outcome laws follow a replacement-noise model whose strength grows by about 0.6% per two-qubit gate, and a parity selection rule rejects shots at the predicted rate.

World Action Models (WAMs), often built upon pretrained video-generation world models, have emerged as a promising framework for general-purpose and open-ended robot control. However, their real-world deployment often suffers from discontinuous and jerky execution. Existing methods typically address this issue by either improving inference efficiency or enhancing action continuity across prediction chunks, without jointly addressing both challenges. To bridge this gap, we propose StreamingWAM, a streaming-diffusion framework for WAM-based robot control. StreamingWAM combines a noise ladder for chunk-wise action prediction, a prefix rollout module for cross-chunk continuity, and dense video-history memory for temporally consistent conditioning. Experiments on LIBERO, LIBERO-Plus, RoboTwin 2.0, and real-world robotic tasks demonstrate that StreamingWAM improves action continuity across static and dynamic settings, achieving competitive performance while reducing steady-state replanning latency to 72.9 ms in simulation and 57.3 ms on the real robot. The method also produces smoother action transitions, while the streaming staircase reduces sensitivity to the execution horizon K.

We study the homogenization of an optimal control problem governed by a Navier-Stokes-Cahn-Hilliard system in a periodically perforated three-dimensional domain. The obstacles are Lipschitz sets of size $\varepsilon^\alpha$, $\alpha>3$, where $\varepsilon$ is the period. The fluid satisfies the no-slip condition on the outer boundary and the free-slip condition on the obstacles, the phase-field equation contains a source term, and the convective term carries a pore-scale Reynolds number that tends to zero as $\varepsilon\to0$. The control is a distributed body force, and the cost functional tracks the velocity in $L^2$ and the order parameter in $H^1$. Since uniqueness of weak solutions of the state system is not known for fixed $\varepsilon$, the control-to-state relation is multivalued, and we pose the microscopic problems over control-state pairs. We prove the existence of microscopic optimal pairs and the sequential $\Gamma$-convergence of the cost functionals to the cost functional of a control problem governed by the unsteady Stokes-Cahn-Hilliard system on the full domain. As a consequence, the optimal values converge, every cluster point of microscopic optimal pairs is optimal for the homogenized problem, and the optimal controls converge strongly along the corresponding subsequences. The $\liminf$ inequality follows from the homogenization of the state system, including the strong convergence of the gradients of the order parameter. Only the recovery sequence requires uniqueness of weak solutions of the homogenized system, which we prove when the outer boundary is of class $C^{2,1}$.

We prove an explicit lower spectral bound for independent random vectors with prescribed group multiplicities. Let $v_{ij}\in\mathbb C^d$, $1\le i\le l$, $1\le j\le k_i$, be independent random vectors with finite support and $\mathbb E v_{ij}v_{ij}^*=A_i$, where $\sum_{i=1}^l A_i=I_d$ and $\operatorname{Tr}(A_i)\ge\epsilon$. Set $k=\max_i k_i$, $n=\max_i\operatorname{rank}(A_i)$, and $K=\sum_{i=1}^l k_i$. If $n>(6+4\sqrt2)k$ and $2\sqrt{nk}-k<\epsilon\le n/2$, then, with positive probability, \[ \lambda_K\left(\sum_{i=1}^l\sum_{j=1}^{k_i}v_{ij}v_{ij}^*\right) \ge n-(\sqrt{k}+\sqrt{n-\epsilon})^2. \] As a consequence, we obtain a restricted invertibility result selecting exactly $k_i$ vectors from the $i$th group of a Parseval frame. The proof combines interlacing families, a reciprocal transformation of mixed characteristic polynomials, and a multivariate barrier estimate that accommodates repeated differentiation in the same variable.

We study the problem of inverse garment design, reconstructing simulation-ready 3D outfits with arbitrary layers from a single image, with visual agents. Unlike differentiable optimization that is time-consuming, or feed-forward methods limited by training data, we introduce DressAgent, a hierarchical agentic system that recovers garment sewing patterns through planning and closed-loop refinement grounded in physical simulation. To this end, we first present an enhanced representation based on GarmentCode for diverse styles and arbitrary dressing layers, and build the first multi-layer sewing pattern dataset with over 15K outfits for supervision and benchmarking. Inverse design then proceeds coarse to fine: In layer planning and coarse estimation, agents infer layer order and relations, peel each layer into its own view, and estimate coarse patterns. In fine-grained refinement, an agentic trio of Tailor, Critic, and Orchestrator refines patterns in a closed loop of editing, simulating, and judging against the reference with visual and measured evidence. Carefully designed tools and an evolving memory make this refinement converge on the target geometry. We also design an agentic generation method for high-fidelity textures. DressAgent outperforms feed-forward methods by a large margin, surpasses or matches differentiable optimization in a fraction of the time, and generalizes to complex layering, diverse poses, and body shapes.

Immersive extended reality (XR) applications are key enablers of human-centric connectivity in next-generation networks. However, meeting their high-throughput, ultra-low-latency, and high-reliability demands while preventing cyber-sickness remains a critical challenge, especially in environments with non-deterministic wireless connectivity. Fiber-To-The-Room (FTTR), extending widely deployed Fiber-To-The-Home (FTTH) infrastructure, offers a promising solution. Yet, satisfying the stringent Quality-of-Experience (QoE) requirements of XR collaborators demands robust resource allocation across the multi-tier cascade of FTTH, FTTR, and WiFi segments. In this paper, we propose a coarse-grained traffic forecasting-empowered proactive resource allocation and intelligent in-premise roaming framework. Here, artificial intelligence (AI) model complexities are scaled to the computational capabilities of each network segment. At the FTTR segment, we employ a hybrid temporal convolutional and bi-directional long short-term memory (TCN-BiLSTM) network for multi-modal XR traffic forecasting, integrating passive optical network (PON) and WiFi access points with multi-link operation (MLO) and orthogonal frequency-division multiple access (OFDMA). The resulting long-horizon forecasts reduce inference frequency, enabling coordinated FTTR-WiFi resource allocation and seamless network-orchestrated mobility across rooms. At the FTTH segment, we deploy Transformer-based time-series models (e.g., Informer, Autoformer, PatchTST, and TFT) to predict the complex XR traffic aggregated in FTTR segments for transmission over the FTTH backhaul. Evaluations show Transformer-based models are up to $\sim$75% more accurate in long-horizon forecasting than the hybrid model. Extensive simulations confirm the proposed framework significantly outperforms traditional methods in satisfying XR QoE requirements.

This paper presents an algorithm for detecting and fitting multiple ellipsoids in 2D, 3D and higher-dimensional data. The representation is based on tangents to the ellipsoid together with their contact points, rather than on points alone. A tangent contributes d independent equations where a point contributes one, so each hypothesis needs fewer samples, which in turn reduces the number of iterations required to recover every ellipsoid. The problem is posed as sets of linear equations whose solutions – the ellipsoid parameters – are recovered robustly by clustering in parameter space. The algorithm works in all dimensions and outperforms the state of the art in 2D. The approach applies in general to the robust solution of linear equations, and so has utility beyond ellipsoid fitting.

Disaggregated LLM inference executes the prefill and decode phases on separate resource pools, reducing interference between the two phases. However, this architecture introduces a coupled scheduling problem because a request that completes prefill retains its prompt KV cache in prefill memory until it is admitted to the decode pool. Prefill and decode admission decisions therefore jointly determine memory occupancy and request completion times. To the best of our knowledge, this problem lacks a formal formulation, and no approximation guarantees are known for request scheduling in disaggregated LLM inference. We formalize this setting as the dual-memory request scheduling (DMRS) problem, whose objective is to minimize the sum of request completion times, and prove that it is strongly NP-hard. We develop two deterministic polynomial-time approximation algorithms. The first applies to instances satisfying $\alpha<1$ and $\beta<1$ and achieves an approximation ratio of $8+\frac{4}{1-\alpha}+\frac{8}{1-\beta}$, where $\alpha$ and $\beta$ are the maximum normalized prefill and peak decode memory demands among all requests, respectively. In particular, it yields a $32$-approximation when $\alpha,\beta\leq 1/2$. The second algorithm applies to all feasible instances and achieves a uniform $36$-approximation. Finally, evaluations on two H100 GPUs using trace-derived offline workloads show that the proposed algorithms reduce the sum of request completion times relative to multiple vLLM deployment baselines.

Post-training expert pruning is a promising deployment technique for Mixture-of-Experts (MoE) models. The calibration set is an integral part of the pruning process, however, its correlation with evaluation performance is largely misunderstood and unintuitive. In this work, we present an empirical study of the robustness of calibration perturbations, specifically in MoE models. Showing how calibration size, language, and data distribution affect layer-wise expert pruning for gpt-oss-20b. Our findings reveal that (1) domain-general pruning quality receives minimal benefit from scaling calibration data beyond small sample sizes, (2) cross-lingual calibration transfers effectively, with English-only calibration maintaining strong performance across seven languages of varying typologies and scripts, and (3) a diverse calibration data resembling the pretraining distribution can yield comparable or superior performance to test-distribution data. Ultimately, we provide practical insights as a recipe for better understanding calibration for more effective domain-general expert pruning.

Cooperative driving at mixed-traffic unsignalized intersections is challenged by imperfect vehicle-to-everything (V2X) communication, human-driven vehicle (HV) intentions that cannot be directly communicated, and the potential incompatibility of independently generated crossing decisions. This paper proposes State-Intent-Aware Graph Multi-Agent Reinforcement Learning for Cooperative Driving under Imperfect V2X (SIGN-MAPPO) to address these challenges through three coordinated mechanisms. Each decision-eligible connected and automated vehicle (CAV) first reconstructs a receiver-specific traffic graph from its ego state and the peer-state records available to it, from which a shared graph-attention policy generates a raw Stop/Go intention augmented with predicted HV-intent features. CAV intentions are then exchanged through timestamped vehicle-to-vehicle (V2V) messages, and a local conflict-aware arbitration layer converts each CAV's intention into a safe executable action despite potentially inconsistent peer information. In the principal Simulation of Urban MObility (SUMO) evaluation, SIGN-MAPPO achieves a 97.53% completion rate, a 0.10% collision rate, and a mean intersection traversal time of 35.28 s. Ablation studies further confirm the complementary contributions of HV-intent prediction and CAV-intent exchange to this performance. The trained policy also remains robust under the evaluated communication-delay and packet-loss ranges, and cross-scenario tests show complete traffic clearance across the tested settings, though safety margins narrow under compound unpredictable HV behavior.

When can combining source and target data yield much better performance than using either alone? From a minimax viewpoint, writing $R(n,n_T)$ for the minimax risk from $n$ source and $n_T$ target observations, we ask when $R(n,n_T)$ decays strictly faster than both $R(n,0)$ and $R(0,n_T)$. We call this the Synergistic Learning Phenomenon (SLP) and quantify it by the Synergistic Advantage Ratio $\mathrm{SAR}=\min(R(n,0),R(0,n_T))/R(n,n_T)$. We study domain adaptation with a known link: source regression $f$, target regression $g=\mathcal{A}f$ for a known operator $\mathcal{A}$, and possibly different designs. Three links—identity, integration, and differentiation—and the density ratio $\rho=h_T/h_S$ determine whether synergy is possible, at what rate, and in which sample-size window. For the identity link (covariate shift) with a Beta$(a,1)$ source and uniform target, a known necessary condition for SLP is $a\geq2+1/(2\beta)$, where $\beta$ is the smoothness of $f$ (Result I). An integral link $g(x)=\int_0^xf(t)\,dt$ can yield the joint rate $(nn_T)^{-1/2}$ even for a bounded ratio (Result II), and under a Beta$(a,1)$ source has a phase diagram with cuts at $a=2$ and $a=3$: integral SLP for $1<a<2$, an extra level-split rate $n_T^{-1}$ for $2<a<3$, and covariate-shift-type spatial SLP for $a>3$ (Result III). A derivative link $g=\dot f$ synergizes only under a stronger singularity (Result IV), giving a sharp integration–differentiation asymmetry under a bounded ratio. Across all three links, synergy arises from role separation: each dataset resolves a distinct bottleneck associated with the link and designs. We characterize the sample-size windows for synergy and how SAR varies with $n$ and $n_T$. Simulations support the predicted phase structure, and a mirrored integration–differentiation analysis of real vehicle and smartphone signals illustrates the asymmetry.

Diffusion models have been widely used for various generative tasks. However, recent studies have demonstrated that the internal representations learned in the diffusion models' denoising process are insufficiently meaningful, which can hinder the generation performance. To address this, we propose a novel diffusion U-Net architecture enhanced by explicitly aligning the weaker representation from the earlier layer conditioned on higher noise with the better representation from the later layer conditioned on lower noise during training, which we use for the challenging multimodal task of video background music generation. Our diffusion U-Net Self-Representation Alignment (U-SRA) employs an alignment loss to effectively guide our diffusion model to learn good internal representations, thereby enabling it to produce higher-quality symbolic music conditioned on video features. We employ a cross-attention mechanism to synchronize music with video segments. Experiments on a benchmark video-music dataset demonstrate that the proposed U-SRA approach for video background music generation surpasses the state-of-the-art across key evaluation metrics, including video-music alignment and overall music quality.

Structural pruning followed by a recovery stage is the standard recipe for compressing large language models (LLMs), yet the two stages are typically designed and tuned in isolation. In this work, we study how their interaction shapes the compressed model, sweeping ordering, the data that drives each stage, and the alignment of their training objectives. We find that instruction-following is the first capability to collapse under pruning, well before knowledge, reasoning, or core language modeling, and that much of the pruning in-domain loss is likely not knowledge loss but a domain-agnostic failure to respond coherently to the prompt. We find in our setting that in-domain data is the strongest pruning signal, whereas instruction-style data recovers the most. Stage order matters little, but only marginally. We further analyze how the importance ranking drifts before and after pruning. Together these results give practitioners a concrete recipe for composing pruning and recovery.