arXiv:2607.13423v1 Announce Type: new
Abstract: Temperature scaling is the dominant post-hoc calibration method in modern deep learning. Its theoretical justification rests on an assumption that is rarely stated explicitly: that ground-truth labels are one-hot and deterministic. In practice, labels are frequently soft, crowd-sourced, or genuinely distributional, reflecting real disagreement among human annotators rather than annotation noise. We study whether temperature scaling retains its calibration properties when this assumption is violated, and whether any resulting degradation depends on model scale. Using CIFAR-10H and ChaosNLI, two publicly available datasets with human-annotated soft label distributions, we evaluate three model scales per modality under both hard one-hot and soft distributional label targets. Across all nine configurations we find a positive soft-label calibration gap: temperature scaling calibrated on hard labels consistently underperforms an oracle calibrated directly on soft labels, with Brier Score gaps ranging from 0.002 to 0.134. The gap grows monotonically with model scale in the vision domain and on the SNLI-derived split of ChaosNLI, and is substantially larger in the language domain (mean gap 0.079) than in vision (mean gap 0.003). A scale-ordering reversal on the MNLI-derived split remains after matched-domain training; we treat it as inconclusive for the scale hypothesis and attribute it primarily to near-chance accuracy on that split. As a second post-hoc baseline, multiclass isotonic regression yields the same qualitative conclusion: positive soft-label gaps in all nine configurations, and larger gaps in language than in vision. These findings suggest that calibration protocols built on majority-vote labels systematically misstate model reliability wherever label ambiguity is structural, with direct consequences for deployment in safety-critical settings.
Science Journals
arXiv:2607.13288v1 Announce Type: new
Abstract: We demonstrate, experimentally and theoretically, a universal mechanism for combining nanophotonic modes relying on radiative-loss-mediated couplings. For the case of two modes this mechanism leads to a BIC-type phenomenon characterized by the emergence of a high-quality factor subradiant mode. This mode is experimentally observed in the mid-infrared range, in arrays of double-metal patch antennas where the radiation loss rates are controlled by the geometry of the system. As the mechanism described here is independent of the specific nature or number of the interacting modes, it can be used to combine physically different resonating structures, without requiring fine symmetry tuning or specific modal configurations, opening new opportunities for resonance-based nanophotonic devices.
arXiv:2607.13104v1 Announce Type: new
Abstract: Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or even no human input. This survey frames modern self-improving agents as adaptive systems that convert experience into accumulated capability gains. We offer a system-level framework that represents a modern agent as a configuration coupling a foundation model with an operational scaffold of prompts, memory, tools, and control logic. Within this framework, self-improvement is formalized as a self-induced update operator that obtains and commits updates to model parameters or scaffold components. We organize prior work by update target and by the signals that drive change, then review applications and discuss evaluation, before closing with open problems and future directions. For convenience, we track technical updates on https://github.com/selfimproving-agent/awesome-Self-Improving-Agents.
arXiv:2607.13186v1 Announce Type: cross
Abstract: We give an algebraic branching program of polynomial size which computes Cayley determinant of right quantum matrices. This is a rare example of an efficient computation of a noncommutative determinant, and the first such example for quantum groups. We extend the results to the $q$-Cayley determinant of $q$-right quantum matrices, as well as to their multiparameter generalization. The proofs are entirely combinatorial, as we relate Cayley, Moore and Valiant determinants using bijections/involutions on words. We then employ the celebrated determinant construction of Mahajan and Vinay (SODA'97), to obtain the results.
arXiv:2511.06001v2 Announce Type: replace
Abstract: In large-scale Internet of things networks, efficient medium access control (MAC) is critical due to the growing number of devices competing for limited communication resources. In this work, we consider a new challenge in which a set of nodes must transmit a set of shared messages to a central controller, without inter-node communication or retransmissions. To our knowledge, this is the first work to formalize and analyze the multi-shared-message decentralized random access problem. Messages are distributed among random subsets of nodes, which must implicitly coordinate their transmissions over shared communication opportunities. The objective is to guarantee the delivery of all shared messages, regardless of which nodes transmit them. We first prove the existence of an optimal deterministic strategy, and characterize the success rate degradation of a deterministic strategy under dynamic message-transmission patterns. To solve this problem, we propose a decentralized learning-based framework that enables nodes to autonomously synthesize deterministic transmission strategies aiming to maximize message delivery success, together with an online adaptation mechanism that maintains stable performance in dynamic scenarios. Extensive simulations validate the framework's effectiveness, scalability, and adaptability, demonstrating its robustness to varying network sizes and fast adaptation to dynamic changes in transmission patterns, outperforming state-of-the-art approaches.
arXiv:2512.04574v2 Announce Type: replace
Abstract: High-order discontinuous Galerkin (DG) methods offer excellent accuracy for turbulent-flow simulations and are increasingly attractive on GPU-oriented architectures, where high polynomial orders can improve arithmetic intensity. However, very high-order under-resolved simulations remain sensitive to the balance between numerical and modeled dissipation. We investigate how explicit Vreman subgrid-scale (SGS) modeling interacts with dissipation from split-form stabilization and Riemann solvers in a DGSEM framework. Using the three-dimensional Taylor-Green vortex at Re=1600 and in the inviscid limit, we assess kinetic-energy dissipation, spectral accuracy, and stability across well-resolved, under-resolved viscous, and strongly under-resolved regimes, comparing lower- and very high-order configurations at similar degrees of freedom.
The usefulness of explicit SGS modeling depends strongly on resolution, polynomial order, and the numerical dissipation already present. In the well-resolved cases considered, Vreman modeling does not improve accuracy because its active wavenumber range overlaps with the scheme's inherent dissipation. At similar degrees of freedom, lower-order simulations introduce stronger damping near the smallest resolved scales, whereas very high-order simulations preserve more spectral content but are more susceptible to high-wavenumber energy accumulation when dissipation is insufficient. Under stronger under-resolution, a weak SGS contribution can control this accumulation, while excessive SGS dissipation degrades intermediate scales.
These results identify regimes in which explicit SGS modeling is beneficial, neutral, or detrimental, and provide practical guidance for selecting dissipation mechanisms in very high-order DG turbulence simulations suited to modern GPU architectures.
arXiv:2607.12435v2 Announce Type: replace
Abstract: Optical chirality has been proposed as the fundamental quantity governing chiral light-matter interactions, but direct experimental verification has remained elusive. Here we realize an optical field with spatially modulated optical chirality and nearly uniform electric energy density, and provide the first direct experimental verification that optical chirality governs nanoscale chiral light-matter interactions. A single chiral nanoparticle exhibits a differential response that follows the spatial modulation of optical chirality, whereas no modulation is observed for an achiral nanoparticle. Electromagnetic simulations further demonstrate the feasibility of enantioselective optical trapping through experimentally achievable chiral optical forces.
arXiv:2607.13415v1 Announce Type: new
Abstract: Recent advances in generative models and technological innovations have significantly addressed the fundamental challenges of character image animation. However, existing approaches predominantly focus on character animation from a single reference image, substantially limiting their applicability in scenarios such as multiple character interaction animation. To fill this gap, this paper introduces MultiAnimate, a comprehensive framework that enables concurrent animation of multiple characters within a shared environment while preserving both identity consistency and spatial relationships. The framework achieves these objectives through multiple well-designed mechanisms. First, we incorporate an identity-specific reference net that enables appearance extraction from multiple reference images, distinguishing MultiAnimate from existing approaches constrained to single reference inputs. Second, we implement an identity-aware pose encoder to address the character-pose binding challenge, wherein an attention mechanism enables the network to accurately differentiate and process multiple pose sequences during generation. Third, we introduce an interaction guider module that enhances the framework's capability to handle complex inter-character interactions by leveraging character-specific mask information, serving as an optional component that refines the pose sequences. Extensive experiments and ablation analyses demonstrate our framework's superiority in multiple character animation, particularly in scenarios involving complex motion sequences.
arXiv:2607.14009v1 Announce Type: new
Abstract: We present AeroMap3D, a monocular 6-DoF UAV localization system that anchors onboard imagery to visual, geometric, and semantic map priors for GNSS-denied navigation. AeroMap3D addresses two fundamental challenges in map-referenced aerial localization: the cross-view discrepancy between UAV imagery and satellite maps, and the structural inconsistency between bare-earth digital elevation models (DEMs) and urban scenes. First, we introduce a lightweight adapter that enables a dense matcher pretrained on internet-scale generic data to perform reliable UAV-to-map registration without finetuning. By estimating the scale ratio and yaw offset between the UAV image and map tile, the adapter removes the dominant geometric misalignment induced by altitude, camera field of view, and heading before dense correspondence estimation. Second, AeroMap3D lifts 2D UAV-map correspondences onto DEM terrain while using OpenStreetMap annotations to reject semantically unreliable matches before RANSAC-PnP pose estimation, thereby reducing errors caused by unmodeled building heights and off-nadir structures. Delayed map-based pose measurements are further fused with relative-motion priors using a delayed-state EKF for continuous trajectory estimation. Without UAV-Terra3D retraining or tuning, AeroMap3D localizes all trajectories across eight Austin sites within 50 m and achieves 5.88 m mean 3D error over 55 km of flight.
arXiv:2510.14317v2 Announce Type: replace-cross
Abstract: Column generation and branch-and-price (B&P) are leading mathematical optimization methods for large-scale exact optimization, iterating between solving a master problem and a pricing problem. Due to the difficulty of discrete optimization, high-performance column generation often relies on a custom pricing algorithm built specifically to exploit the problem's structure. This bespoke nature of the pricing solver makes column generation a problem-specific method and hinders the use of generic implementations across a wide range of problems. We show that domain-independent dynamic programming (DIDP), a model-based paradigm for dynamic programming, can be used as a generic pricing solver. We develop new modeling features and a solving algorithm for DIDP to achieve better performance in typical pricing problems. We demonstrate that in four problem classes, our implementations of B&P, with pricing by DIDP, empirically outperform an existing automated B&P solver and B&P with pricing by mixed-integer programming or constraint programming.
arXiv:2607.13199v1 Announce Type: new
Abstract: We present a two-stage inverse design procedure for producing disordered stealthy hyperuniform trivalent photonic networks in two dimensions with isotropic complete photonic band gaps (PBGs) blocking light regardless of direction or polarization (TE or TM) over a wide frequency range. Most ordinary disordered systems fail to maintain complete PBGs as system size increases. The only known exceptions that remain open in the largest simulations have been generated by mapping stealthy hyperuniform point patterns into trivalent networks. However, the resulting networks are not truly stealthy hyperuniform two-phase media. Although their PBGs remain open, they are relatively narrow due to limited overlap between the TE and TM band gaps and broad band tails caused by localized defect states. By contrast, our two-stage inverse design aims to make the final network itself stealthy hyperuniform, achieving unprecedented near-optimal overlap between the TE and TM band gaps and a small defect state density at the band edges. We obtain not only single realizations with large PBGs, but a striking homogeneity across a large ensemble, effectively probing a network with 100,000 vertices. This ensemble-based band gap is comparable in width to the complete PBG of an anisotropic honeycomb photonic crystal with the same network parameters and nearly an order of magnitude wider than the previously widest known isotropic complete PBGs. Our designs can be fabricated using additive manufacturing, offering new pathways to manipulate electromagnetic waves for photonic technologies.
arXiv:2607.13728v1 Announce Type: new
Abstract: Large-scale approximate nearest neighbor search commonly relies on partitions for indexing: database vectors are partitioned into clusters, and for each query a probing function selects the clusters to be scanned. The query probing function and the database partition are rarely treated as separate entities: most techniques assign queries with the same assignment function as the database vectors, which is suboptimal especially when database and query distributions differ. This paper introduces CwA (Cluster with Auctions), which addresses this limitation by jointly learning a balanced database partition and a neural probing function. CwA optimizes search performance directly for the query distribution. It minimizes its objective by alternating two steps: (i) gradient descent on the neural network of the probing function, and (ii) a large-scale combinatorial optimization of the cluster assignment for the database vectors. We solve the latter with a parallelizable auction algorithm that balances the partition by design. To further scale CwA, we extend the method to a Cartesian product of clusters that increases the partition's granularity. When database and query distributions differ, CwA achieves up to 4.7$\times$ throughput over the state-of-the-art at equal recall. In the in-distribution (ID) setting, even a simple linear probing function trained with CwA outperforms competing deep neural methods.
arXiv:2607.13847v1 Announce Type: cross
Abstract: Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representations. Quantum machine learning offers the possibility of processing high-dimensional data in Hilbert spaces, but its practical success depends critically on how classical data is encoded into quantum states. We introduce \emph{quantum topological data encoding} (QTDE), a general framework for encoding topological information into quantum states via topology-driven quantum evolution. Our method generalises an existing topology-driven quantum encoding framework to higher-dimensional data. We test the proposed method on clique-complexes classification tasks, and provide preliminary evidence that topology-driven quantum representations can capture discriminative information beyond that available through direct comparisons of classical topological descriptors. The proposed quantum representations consistently outperform a baseline based on direct comparisons of the combinatorial Laplacians describing the underlying topological structure. We indicate several areas of application where the framework can be used to provide a more efficient and reliable data representation.
arXiv:2607.13930v1 Announce Type: cross
Abstract: Constraining the equation of state of dense matter requires confronting effective models with massive data that spans many orders of magnitude in scale, from sub-saturation nuclear matter properties to the masses, radii, and tidal deformabilities of neutron stars. Exploring the high-dimensional coupling space of such a model and fine tuning it against all of these constraints is a labor- and time-intensive task. We present \textsc{NNStar}, an end-to-end artificial-intelligence agent that automates this workflow. Rather than a bespoke application, \textsc{NNStar} is delivered as a portable \emph{skill} for an open large-language-model (LLM) agent platform -- a self-describing module that pairs worked usage conventions with symbolic and numerical physics engines that (i) build a relativistic mean-field model directly from a Lagrangian, (ii) solve the mean-field equations of motion and evaluate the saturation properties, (iii) construct the $\beta$-equilibrium equation of state, splice it to a crust, and integrate the Tolman--Oppenheimer--Volkoff equations, and (iv) score the resulting predictions through a Bayesian joint analysis against nuclear matter and astrophysical observations. The agent can read a model, fit its parameters, and report the full set of nuclear matter and neutron star observables without human intervention. \textsc{NNStar} therefore provides a new, AI-driven framework for analyzing nuclear matter and neutron-star observations.
arXiv:2607.11745v2 Announce Type: replace
Abstract: This paper establishes a conceptual and theoretical framework for multilevel communication between quantum, molecular, cellular, tissue-organ, whole body, and other biophysical spaces. The wave-mechanical description of electromagnetic signaling is developed, detailing how waves move through cellular structures and interact energetically with surrounding biomaterials. These regions transmit and receive partially coherent biomolecular signals within and across cells, tissues, organs, and neural networks, where resonant frequencies interfere, converge and respond. These processes introduce nonlinearities and stochastic delays that shape timing, coherence, and network-level dynamics, and can be integrated within a pragmatic framework linking mathematical modeling of the physical state to experiential outcomes. This contribution stands as an independent theoretical framework: it sets the foundation for the wave mechanical approach to understanding brain function, which may help to develop new methods to prevent aberrant brain behavior. Mathematical derivations of the inter-sector coupling operators and clinical and therapeutic applications are postponed for future work.
arXiv:2607.12062v2 Announce Type: replace
Abstract: Differentiating non-alcoholic steatohepatitis (NASH) from non-alcoholic fatty liver disease (NAFLD) using ultrasound remains challenging due to subtle tissue alterations and the limited information available in conventional B-mode imaging. In this work, we investigate whether complementary ultrasound representations derived from the same acquisition can improve NASH versus NAFLD classification. Specifically, we combine conventional B-mode ultrasound with physics-guided and local phase-based image representations and evaluate their effectiveness using self-supervised masked autoencoders (MAEs) and graph convolutional networks (GCNs). Experiments were conducted on a multi-site Mayo Clinic cohort consisting of 2,547 liver ultrasound scans from 125 patients. Compared with conventional B-mode ultrasound alone, complementary ultrasound representations consistently improved classification performance, yielding gains of up to 32.4% in accuracy and 91.2% in F1-score. Furthermore, performance improvements were consistently observed across age groups, sex, race, ethnicity,and acquisition sites.
arXiv:2607.13499v1 Announce Type: new
Abstract: 3D anomaly detection has recently emerged as an important research topic in computer vision. Although existing methods have achieved high performance, excessive anomaly responses in normal regions and false positives near object boundaries remain unresolved challenges. To address these challenges, we propose a novel 3D anomaly detection model, Memory-to-Prototype Anomaly Detection (M2P-AD), which effectively models the distribution of normal features while suppressing excessive anomaly scores in normal regions and false positives near object boundaries. Specifically, we introduce a Memory-to-Prototype (M2P) module that learns representative prototypes from normal feature embeddings to preserve important structural information of objects. In addition, a Boundary extraction (BE) module is integrated to identify object boundaries, and a Boundary-aware score refinement (BSR) strategy is applied to recalibrate anomaly scores by incorporating boundary characteristics. The proposed method is evaluated on Real3D-AD, Anomaly-ShapeNet, and MulSen-AD, achieving state-of-the-art performance. Qualitative results demonstrate that excessive anomaly scores in normal regions are reduced and false positives near object boundaries are suppressed, resulting in more accurate and stable anomaly localization. The results indicate that the proposed approach enables more reliable 3D anomaly detection and provides a robust solution applicable to real-world industrial environments.
arXiv:2607.13799v1 Announce Type: new
Abstract: Selective harvesting in clustered strawberry environments is challenging because ripe fruits are often occluded by surrounding unripe fruits, making direct grasping unreliable. To address this problem, this paper proposes a hierarchical reinforcement learning framework, termed VGPA, which integrates a vision-guided decision mechanism and a Progressive Adaptive Exploration Strategy (PAES) for vision-based obstacle separation and harvesting. The task was decomposed into two sequential stages: obstacle separation and target grasping. At the high level, the vision-guided mechanism improved option selection and accelerated policy convergence. At the low level, PAES improved exploration efficiency and training stability during continuous control learning. In simulation experiments, the learned policy achieved a success rate of 96.7%. In addition, sim-to-real transfer experiments on a self-developed parallel robot showed that the proposed method achieved success rates ranging from 71.7% to 88.3%, outperforming direct picking while requiring only 1.22~s more average harvesting time. These results verified the effectiveness, generalization ability, and practical potential of the proposed method for robotic harvesting in complex clustered environments.
arXiv:2607.13402v1 Announce Type: cross
Abstract: In bandit problems, standard regret-minimizing algorithms treat exploration as an amortized cost, which can expose early participants to unfair ex-ante losses in settings such as clinical trials. Recent work addresses this by evaluating the sequence of per-round expected rewards through the generalized $p$-mean, interpolating between utilitarian welfare ($p=1$), Nash welfare ($p\to0$), and Rawlsian fairness ($p\to-\infty$). Although tight guarantees are known for $p\ge0$, the strictly fair regime $q=-p>0$ remains unresolved because negative-power means are dominated by the smallest per-round rewards.
For $\sigma$-sub-Gaussian rewards with nonnegative means, the best prior algorithm relied on uniform early exploration and achieved regret $O(k^{(q+1)/2}/\sqrt{T})$, while the only general lower bound was the classical $\Omega(\sigma\sqrt{k/T})$. Thus it was unclear whether the extra dependence on $k$ was intrinsic to strict fairness or an artifact of uniform exploration.
We close this gap by identifying the exact polynomial price of strict fairness. Using a needle-in-haystack construction, we prove an algorithm-independent lower bound $\Omega(\sigma\sqrt{k^{\max(1,q)}/T})$; for $q>1$, this shows that the penalty $k^{q/2}$ is information-theoretically unavoidable. We then introduce \textsf{UCB-HARE} (Harmonic Anchored Rank Exploration), which replaces uniform exploration with an inverse-weighted harmonic rank schedule protected by a certified positive-mean anchor. Its regret is $\widetilde{O}(\sigma\sqrt{k^{\max(1,q)}/T})$, matching the lower bound up to logarithmic factors. Experiments on synthetic instances confirm that \textsf{UCB-HARE} improves over uniform-exploration baselines, with gains increasing as $q$ grows.
arXiv:2507.04813v3 Announce Type: replace
Abstract: This paper presents a degradation-cost-aware optimization framework for multi-string battery energy storage systems, emphasizing the impact of inhomogeneous subsystem-level aging in operational decision-making. We evaluate four scenarios for an energy arbitrage scenario, that vary in model precision and treatment of aging costs. Key performance metrics include operational revenue, power schedule mismatch, missed revenues, capacity losses, and revenue generated per unit of capacity loss. Our analysis reveals that ignoring heterogeneity of subunits may lead to infeasible dispatch plans and reduced revenues. In contrast, combining accurate representation of degraded subsystems and the consideration of aging costs in the objective function improves operational accuracy and economic efficiency of BESS with heterogeneous aged subunits. The fully informed scenario, which combines aging-cost-aware optimization with precise string-level modeling, achieves 21% higher revenue per unit of SOH loss compared to the baseline scenario. These findings highlight that modeling aging heterogeneity is not just a technical refinement but may become a crucial enabler for maximizing both short-term profitability and long-term asset value in particular for long BESS usage scenarios.
arXiv:2507.09473v2 Announce Type: replace
Abstract: We study the dynamic allocation of indivisible resources to strategic agents under long-term constraints, where the planner aims to maximize social welfare, satisfy multiple constraints, and elicit near-truthful reports. We find standard primal-dual methods fragile in this setting: agents easily manipulate their reports to distort dual variables, sacrificing social efficiency for individual utility. To address this, we propose the Incentive-Aware Primal-Dual (IAPD) framework. On the primal side, we integrate three components to suppress manipulation: a VCG-based payment neutralizes immediate misreporting benefits, while epoch-based lazy updates and random exploration together ensure potential future gains are outweighed by immediate penalties. On the dual side, to overcome a learning barrier due to lazy updates -- which we call the "price of incentives" -- we design a novel optimistic online learning algorithm, O-FTRL-FP. It utilizes a fixed-point oracle to resolve the circular dependency between optimistic dual variables and the resulting allocations. Ultimately, our mechanism attains $\tilde{\mathcal O}(\sqrt T)$ social welfare regret, satisfies all long-term constraints, and induces a near-truthful equilibrium. It also smoothly generalizes to multi-unit multi-demand allocation problems. Notably, this $\tilde{\mathcal O}(\sqrt T)$ regret near-matches the non-strategic $\Omega(\sqrt T)$ lower bound, demonstrating that incentive-awareness can be accommodated at nearly no cost.
arXiv:2508.07500v3 Announce Type: replace
Abstract: The optical cycle-averaged ionization rate of Ar, O$_{2}$, and N$_{2}$ vs. local instantaneous laser intensity $I$ for linear polarized $800$ nm light is determined up to approx. $300$ TW/cm$^{2}$ by numerically inverting published time-of-flight ion spectrometer data. The published Ar$^{+}$ collection efficiency of the microchannel plate (MCP) at the end of the spectrometer and its $I_{0}$ scale are recalibrated by fitting it to its high $I_{0}$ solution. The relative collection efficiencies of the other species are determined by published MCP cathode data. Results for O$_2$ are consistent with a reevaluation of published data used to determine its cross section $\sigma_8$ in the multiphoton (low $I$) regime.
arXiv:2603.00546v2 Announce Type: replace
Abstract: Using Multimodal Large Language Models (MLLMs) as judges to achieve precise and consistent evaluations has gradually become an emerging paradigm across various domains. Evaluating the capability and reliability of MLLM-as-a-judge systems is therefore essential for ensuring trustworthy assessment. Existing judge benchmarks categorize samples by task types but fail to capture the fundamental judgment capabilities required for reliable evaluation. In this work, we introduce M-JudgeBench, a ten-dimensional capability-oriented benchmark designed to comprehensively assess the judgment abilities of MLLMs. Our benchmark decomposes evaluation into pairwise Chain-of-Thought (CoT) comparison, length bias avoidance, and process error detection tasks, jointly covering ten fine-grained subtasks. This design enables diagnosis of model reliability across reasoning styles, response lengths, and cross-model variations. Systematic evaluation uncovers the systematic weaknesses in existing MLLM-as-a-judge systems. To address this issue, we further propose Judge-MCTS, a data construction framework generating pairwise reasoning trajectories with various correctness and length. Using Judge-MCTS, we construct an MCTS-augmented dataset and train M-Judger, a series of strong judge models. Extensive experiments demonstrate the superiority of M-Judger on existing judge benchmarks as well as M-JudgeBench. Overall, our work establishes a more principled foundation for evaluating MLLM-as-a-judge through M-JudgeBench and Judge-MCTS framework, paving the way for future research on judge model evaluation and capability-driven judge training.
arXiv:2509.25753v3 Announce Type: replace
Abstract: We study the application of a quasi-Monte Carlo (QMC) method to a class of semi-linear parabolic reaction-diffusion partial differential equations used to model tumor growth. Mathematical models of tumor growth are largely phenomenological in nature, capturing infiltration of the tumor into surrounding healthy tissue, proliferation of the existing tumor, and patient response to therapies, such as chemotherapy and radiotherapy. Considerable inter-patient variability, inherent heterogeneity of the disease, sparse and noisy data collection, and model inadequacy all contribute to significant uncertainty in the model parameters. It is crucial that these uncertainties can be efficiently propagated through the model to compute quantities of interest (QoIs), which in turn may be used to inform clinical decisions. We show that QMC methods can be successful in computing expectations of meaningful QoIs. Well-posedness results are developed for the model and used to show a theoretical error bound for the case of uniform random fields. The theoretical linear error rate, which is superior to that of standard Monte Carlo, is verified numerically. Encouraging computational results are also provided for lognormal random fields, prompting further theoretical development.
arXiv:2509.26368v3 Announce Type: replace
Abstract: The growing demand for real-time, safety-critical systems has significantly increased both the adoption and complexity of Time Sensitive Networking (TSN). Configuring an optimized TSN network is highly challenging, requiring careful planning, design, analysis, verification, validation, and deployment. Large Language Models (LLMs) have recently demonstrated strong capabilities in solving complex tasks, positioning them as promising candidates for automating end-to-end TSN deployment and management, referred to as TSN orchestration. This paper outlines the steps involved in TSN orchestration and the associated challenges. To assess the capabilities of existing LLMs, we conduct an initial proof-of-concept case study focused on TSN tasks across multiple models. Building on these insights, we propose an LLM-assisted orchestration framework. Unlike prior research on LLMs in computer networks, which has concentrated on general configuration and management, TSN-specific orchestration has not yet been investigated. We present the building blocks for automating TSN using LLMs, describe the proposed pipeline, and analyze opportunities and limitations for real-world deployment. This work provides the first roadmap toward assessing the feasibility of LLM-assisted TSN orchestration.