arXiv:2607.02566v1 Announce Type: cross Abstract: Lunar positioning, navigation, and timing (PNT) is moving from concept to hardware, ESA's Moonlight/LCNS, NovaMoon reference stations, LunaNet, and Coordinated Lunar Time, all reducing to one estimation core: fix the orbits and clocks of the lunar infrastructure and tie them to an Earth/inertial frame. We ask which measurements make a surface station's absolute position observable, and prove the answer. In a snapshot batch fit, the internal observables (station-to-satellite and inter-satellite ranging plus clock-sync) constrain only relative geometry and leave a six-dimensional rigid-body datum defect: three translations and three rotations of the cluster. The clocks are fully observable, so the defect is purely positional, and closing it needs a tie to the Earth frame. Two such ties exist and are not interchangeable. An indirect tie (Earth-to-satellite ranging through the constellation) reaches the station only when the satellite geometry is rich; a direct tie (an Earth-baseline VLBI delay to the station beacon) fixes it regardless. This gives a conditional design law, not a single number: VLBI restores absolute observability when the constellation cannot supply it, and merely sharpens the bound when it can. For a sparse three-satellite constellation the station lies in the null space of the Fisher information until VLBI is added, reaching a Cramer-Rao bound of 20.1 m; for a rich six-satellite constellation VLBI tightens the bound from 23.2 m to 9.7 m. A single-epoch baseline informs at most two of three axes, so the datum closes at three non-collinear Earth stations. The Gauss-Newton estimator attains the bound (efficiency 1.02), with a 91x median station-error improvement in the sparse regime. The FIM/CRLB engine is validated against NumPy and published closed forms; the lunar application stays modelled, every figure deterministic and reproducible.
Science Journals
arXiv:2607.04249v1 Announce Type: new Abstract: Precise medical image segmentation is crucial for clinical diagnosis and treatment planning, yet relies heavily on expensive expert annotations. Semi-supervised medical image segmentation (SSMIS) offers a cost-effective solution but typically operates under the assumption of independent and identically distributed (i.i.d.) data, defaulting to random sampling. While statistically valid at scale, this strategy suffers from severe representation bias in low-data regimes, failing to capture the heterogeneous medical data manifold. To address this, we propose a highly data-efficient framework driven by distribution alignment. First, we introduce an offline Distribution-Aware Sample Selection strategy. By leveraging Vision Foundation Models (VFMs) and our designed Density-K-Center algorithm, we explicitly identify representative structural anchors, establishing a more representative labeled domain. Second, to bridge the remaining distribution gap, we propose the Memory-guided Copy-Paste (MCP) module. Tailored for the inherent class imbalance in medical scans, MCP leverages a semantic memory mechanism to retrieve historically consistent priors for cross-domain alignment, encouraging semantic consistency. Coupled with an easy-to-hard progressive schedule, this framework effectively mitigates early-stage pseudo-label noise. Extensive experiments on six diverse 2D and 3D datasets demonstrate strong segmentation performance, particularly in extremely low-labeled scenarios (\eg, 1/16 ratio).
arXiv:2508.15753v4 Announce Type: replace-cross Abstract: In recent years, the concept of kirigami has been used in creating deployable structures for various scientific and technological applications. While high-fidelity Finite Element Analysis (FEA) is the standard for analyzing stress distributions and material deformation, it is computationally intensive and often ill-suited for the rapid exploration of vast kinematic configuration spaces. In this work, we develop PyKirigami, a lightweight, open-source Python framework for the efficient deployment simulation of kirigami structures. Unlike continuum mechanics solvers, PyKirigami models tessellations as articulated rigid-body networks, allowing for the real-time simulation of global deployment trajectories and volumetric transformations. The tool incorporates collision detection and interactive actuation, enabling users to validate folding paths and identify geometric locking states in both 2D and 3D topologies. This framework serves as a fast kinematic prototyping tool for kirigami structures, allowing researchers to verify deployment mechanics and self-contacts prior to performing detailed mechanical analysis or physical fabrication.
arXiv:2508.17471v3 Announce Type: replace-cross Abstract: This paper proposes a two-stage distributed variational quantum eigensolver (DVQE) software for solving quadratic unconstrained binary optimization (QUBO) problems and bounded constrained quadratic programming (QP) problems. The proposed DVQE solver supports both monolithic and distributed quantum-circuit execution and evaluates QUBO objectives directly from measured bitstrings. To improve variational training, DVQE uses a two-stage procedure that combines metaheuristic warm-start initialization with sampling-based variational refinement. The software supports several metaheuristic approaches as warm-start strategies. To extend QUBO-based quantum optimization to constrained continuous problems, this paper also develops a sequential QP to QUBO framework, called QQP. QQP first scales the bounded continuous variables to a normalized box and then handles equality and inequality constraints using a Powell-Hestenes-Rockafellar (PHR) augmented-Lagrangian formulation. Under a fixed PHR active region, the constrained augmented-Lagrangian subproblem becomes an ordinary bounded quadratic problem. QQP then solves this bounded quadratic problem through repeated local one-bit QUBO reformulations, where each binary variable represents a local up/down move of one continuous variable inside a trust region. In this way, QQP converts a constrained continuous QP into a sequence of QUBO subproblems without introducing slack variables. Each local QUBO subproblem can be solved using either a classical QUBO backend or the proposed DVQE solver. Numerical experiments evaluate the proposed software on QUBO and QP test problems. The results show that the distributed DVQE framework can recover high-quality QUBO solutions, and that the QQP framework can solve bounded constrained QP instances with small optimality, feasibility, and solution gaps.
arXiv:2303.15121v5 Announce Type: replace-cross Abstract: We consider the problem of finite-time identification of linear dynamical systems from $T$ samples of a single trajectory. Recent results have predominantly focused on the setup where either no structural assumption is made on the system matrix $A^* \in \mathbb{R}^{n \times n}$, or specific structural assumptions (e.g. sparsity) are made on $A^*$. We assume prior structural information on $A^*$ is available, which can be captured in the form of a convex set $\mathcal{K}$ containing $A^*$. For the solution of the ensuing constrained least squares estimator, we derive non-asymptotic error bounds in the Frobenius norm that depend on the local size of $\mathcal{K}$ at $A^*$. To illustrate the usefulness of these results, we instantiate them for four examples, namely when (i) $A^*$ is sparse and $\mathcal{K}$ is a suitably scaled $\ell_1$ ball; (ii) $\mathcal{K}$ is a subspace; (iii) $\mathcal{K}$ consists of matrices each of which is formed by sampling a bivariate convex function on a uniform $n \times n$ grid (convex regression); (iv) $\mathcal{K}$ consists of matrices each row of which is formed by uniform sampling (with step size $1/T$) of a univariate Lipschitz function. In all these situations, we show that $A^*$ can be reliably estimated for values of $T$ much smaller than what is needed for the unconstrained setting.
arXiv:2406.14340v2 Announce Type: replace-cross Abstract: The standard stochastic gradient descent (SGD) optimization method, as well as adaptive methods such as the Adam optimizer fail to converge if the learning rates do not converge to zero (particularly, in the situation of constant learning rates). In practice, human-tuned deterministic learning rate schedules or small constant learning rates are often used, and implementations in machine learning frameworks like Tensorflow and Pytorch typically employ constant learning rates. We propose a learning-rate-adaptive approach for SGD methods, adjusting the learning rate based on empirical estimates for the objective function values. Specifically, we propose a learning-rate-adaptive variant of the Adam optimizer and implement it for several machine learning problems, including deep learning methods for partial differential equations such as deep Kolmogorov methods, physics-informed neural networks, and deep Ritz methods. We refer to https://github.com/deeplearningmethods/adaptive-learning-rate for the Python source codes for the numerical simulations in this work. Our results show that the proposed adaptive Adam variant achieves faster reductions of the objective function value compared to Adam with default learning rates. For certain quadratic minimization problems, we rigorously prove that an adaptive SGD variant converges to the global minimizer. This proof uses properties of invariant measures of the SGD dynamics and a generalized convergence analysis for SGD with random predictable learning rates which we develop in this work.
arXiv:2312.15320v3 Announce Type: replace-cross Abstract: Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests, and genetic tests over a prolonged period of time, a process commonly described as the diagnostic odyssey. Addressing this odyssey has substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features that artificial intelligence algorithms can use to facilitate clinical diagnosis, to prioritize candidate diseases for further laboratory or genetic testing, and to support the phenotype-driven reinterpretation of genome or exome sequencing data. Existing methods that use frontal facial photographs were built on conventional convolutional neural networks, rely exclusively on facial images, and cannot capture non-facial phenotypic traits or demographic information that are essential for accurate diagnosis. Here we introduce GestaltMML, a multimodal machine learning approach based solely on the Transformer architecture. It integrates facial images, demographic information (age, sex, ethnicity), and clinical notes (optionally a list of Human Phenotype Ontology terms) to improve prediction accuracy. We evaluate GestaltMML on 528 diseases from the GestaltMatcher Database and on several in-house and published cohorts, including Beckwith-Wiedemann syndrome, Sotos syndrome, NAA10-related neurodevelopmental syndrome, Cornelia de Lange syndrome, and KBG syndrome. GestaltMML improves on the state-of-the-art image-only ensembled model, narrows the diagnostic accuracy gap for patients from under-represented ancestries, and clarifies when multimodal fusion is beneficial and when image-only inference is preferable. The results suggest that GestaltMML can greatly narrow the candidate diagnoses of rare diseases and may facilitate the reinterpretation of sequencing data.
arXiv:2411.14833v4 Announce Type: replace-cross Abstract: Conventional multi-stage cell tracking approaches rely heavily on detection or segmentation in each frame as a prerequisite, requiring substantial resources for high-quality segmentation masks and increasing the overall prediction time. To address these limitations, we propose CAP, a novel end-to-end one-stage framework that reimagines cell tracking by treating Cell as Point. Unlike traditional methods, CAP eliminates the need for explicit detection or segmentation, instead jointly tracking cells for sequences in one stage by leveraging the inherent correlations among their trajectories. This simplification reduces both labeling requirements and pipeline complexity. However, directly processing the entire sequence in one stage poses challenges related to data imbalance in capturing cell division events and long sequence inference. To solve these challenges, CAP introduces two key innovations: (1) adaptive event-guided (AEG) sampling, which prioritizes cell division events to mitigate the occurrence imbalance of cell events, and (2) the rolling-as-window (RAW) inference strategy, which ensures continuous and stable tracking of newly emerging cells over extended sequences. By removing the dependency on segmentation-based preprocessing while addressing the challenges of imbalanced occurrence of cell events and long-sequence tracking, CAP demonstrates promising cell tracking performance and is 8 to 32 times more efficient than existing methods. The code and model checkpoints are available at https://github.com/YXSong000/CAP.
arXiv:2603.11475v2 Announce Type: replace Abstract: Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions. Existing statistical-based and shallow machine learning models have shown limited prediction capabilities on multivariate time series. They prioritize improvements in average prediction accuracy, while overlooking heterogeneous dependency structures and performance variability across individual time series. Recent advances in large language models have introduced new directions for multivariate time series forecasting; however, their application in conjunction with explicit structural dependency modeling remains relatively underexplored, especially in networked environments. In this paper, we present a topology-aware learning framework for large-scale network traffic prediction that explicitly models both temporal dynamics and structural dependencies in multivariate network time series. We first investigate a graph attention model designed to capture topology-induced correlations among network traffic time series. We then evaluate a fine-tuned large language model-based representations for improved generalization across heterogeneous traffic patterns. To further address the diversity of cross-correlations in high-dimensional traffic data, we introduce a clustering-based preprocessing stage that groups traffic flows with similar dependency characteristics prior to model training, reducing input complexity and improving learning stability. Experiments on real backbone traffic data show consistent improvements over statistical and recurrent neural network baselines. In addition to average accuracy, we evaluate performance across individual time series and observe reduced variability in prediction quality.
arXiv:2607.04919v1 Announce Type: new Abstract: Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost. We provide the first systematic break-even analysis answering when this investment pays off. Across 30 benchmark datasets, we compare zero-shot and LoRA fine-tuned foundation models (Chronos, Moirai, Lag-Llama) against classical baselines (Naive, ETS, ARIMA, XGBoost) at six training set sizes from 2% to 100% of available data. Foundation models outperform classical methods at every evaluated training fraction on 15 of 30 datasets -- GPU deployment is unconditionally justified on these regardless of data volume. On 6 datasets, classical methods surpass zero-shot foundation models with as little as 2% of training data (21-2,768 samples); on the remaining 9, break-even ranges from 24 to 8,361 samples. One robust deployment rule requires no model training: if n_train < 700 and seasonality is non-negligible, use FM zero-shot and skip fine-tuning -- this resolves 10 of 30 deployment decisions immediately. Contrary to common practice, LoRA fine-tuning can actively degrade performance on short series. We operationalise these findings as a two-step decision framework -- compute dataset length and seasonality strength, run a brief 5-10% pilot only if needed -- enabling practitioners to make the FM-versus-classical decision before committing to full infrastructure. Four dataset features motivate mechanistic hypotheses for the remaining cases, though reliable automated prediction at this benchmark scale remains an open problem. Code, benchmark, and decision tools are available at https://github.com/nicolaisi/fm-breakeven.
arXiv:2607.02741v1 Announce Type: cross Abstract: This paper develops an Optimality-Informed Neural Network (OINN) approach for the energy-optimal, free-final-time powered descent of a lunar lander from any initial position, velocity, and mass within a bounded operating envelope to a fixed landing site with zero terminal velocity. Building on a recent framework that jointly embeds Pontryagin's minimum principle and the Hamilton-Jacobi-Bellman equation for general nonlinear optimal control, the proposed OINN approach specializes that idea to a lunar landing problem with free time of flight and fixed terminal state. Every boundary and transversality condition is hard-encoded into the network architecture by construction, the closed-form Pontryagin-optimal thrust magnitude and direction law is substituted directly rather than learned, and the remaining state, costate, and an auxiliary value-function output are trained against a physics-residual loss formed entirely from the necessary conditions of optimality, with no precomputed optimal trajectories required. A preliminary theoretical analysis is explored, establishing a stochastic-optimization stationarity guarantee for the offline training procedure, an explicit bound translating the achieved training residual into bounds on touchdown position, touchdown velocity, and flight-time error, and a fixed, input-independent onboard computational and memory cost suitable for real-time deployment. Numerical simulations evaluate the trained policy, with no retraining, against an independently solved indirect-method boundary-value problem at six representative initial states spanning the operating envelope and against eighty additional Monte Carlo simulation runs, demonstrating close agreement with the indirect-method solution and consistently small dynamics and transversality residuals throughout the envelope.
arXiv:2607.04281v1 Announce Type: new Abstract: Semantic caching reduces the latency and cost of retrieval-augmented generation (RAG) by serving cached answers to semantically similar queries, but most existing methods do not model the time-varying freshness of open-web evidence. We present FreshCache, a three-tier semantic cache that treats cache reuse as a risk-constrained temporal inference problem: before approving a cache hit, FreshCache estimates the probability that the cached result is stale using a fitted exponential decay model enhanced by a learned MLP, and approves reuse only when that probability falls below a per-tier error budget across answers (epsilon = 0.10), URL lists (epsilon = 0.20), and page content (epsilon = 0.35). This allows the system to degrade gracefully as entries age rather than forcing a binary choice between a stale hit and a full pipeline execution. We introduce FreshCache-Bench, a benchmark of 8,072 base queries across five freshness classes with ground truth staleness labels drawn from real web snapshots at 1, 12, 24 hours, and 7 days after a baseline crawl, expanded to 31,201 queries via paraphrase generation. At the 24-hour evaluation window, FreshCache_MLP achieves 97% search API savings at 0.1% hash-based stale error, and an LLM-judge evaluation on 396 confirmed change pairs shows that only 34.3% of detected content changes actually affect answer correctness, placing true answer-affecting stale error at approximately 0.034%. The rule-based FreshCache achieves 98% search savings at 3.3% stale error under a temporal holdout calibration, outperforming SemanticTTL (14.9% stale, 72% saved), vCache (7.2% stale, 47% saved), and SCALM (5.2% stale, 96% saved). Ablations show the temporal risk gate accounts for an 11.6 point reduction in stale error over similarity-only reuse, and the learned MLP reduces stale error a further 3.2 points over the rule-based model.
arXiv:2607.04927v1 Announce Type: new Abstract: World Action Models (WAMs) provide a promising alternative to Vision-Language-Action (VLA) policies by using video-based world modeling as dense supervision for robot action learning. Existing WAMs excel at physically grounded execution, but typically lack the explicit language-level planning interface in VLM-based VLAs for decomposing coarse instructions. Such decomposition becomes important when household tasks involve complex multi-step goals, where coarse user commands need to be converted into sequences of fine-grained executable subtasks. Meanwhile, the field still lacks a fair real-robot comparison between VLA and WAM execution capabilities, since existing systems often differ in data, robot embodiments, and task protocols. To address both the decomposition gap and the need for a controlled WAM-VLA comparison, we introduce DSWAM, a Dual-System World Action Foundation Model for fine-grained robot manipulation. DSWAM keeps a System 1 WAM executor as the default control path and optionally activates a System 2 vision-language subtask planner only when task decomposition is useful. The planner predicts executable subtasks from short-term visual history and a global task prompt, while the WAM executor performs world-aware action generation for each instruction or subtask. The executor is trained with action prediction and video co-training, but inference directly predicts action chunks without explicit future video generation. To make this execution path practical on real robots, we further integrate TensorRT acceleration, asynchronous execution, and real-time chunking (RTC) so that policy queries do not block robot control. To provide a fair real-robot comparison with VLA policies, we build and evaluate DSWAM under the DeMaVLA real-world deformable manipulation setting with matched robot platform, pretraining data, post-training data, and evaluation criteria.
arXiv:2509.00737v3 Announce Type: replace-cross Abstract: The ProbAbilistic Gradient Estimator algorithm (PAGE), a stochastic algorithm introduced by Li et al. in 2021, was designed to find stationary points for the average of smooth nonconvex functions. In this work, we study PAGE within the broad framework of $\tau$-weakly convex functions, providing a continuous interpolation between the general nonconvex $L$-smooth regime ($\tau=L$) and the convex regime ($\tau=0$). We establish new convergence rates for PAGE, showing that its complexity improves as $\tau$ decreases.
arXiv:2606.20723v2 Announce Type: replace Abstract: Chronic wound assessment remains a clinically challenging task that requires accurate interpretation of wound morphology, tissue composition, vascular characteristics, and infection risk. Recent advances in Vision-Language Models (VLMs) have introduced the possibility of automated multimodal wound analysis through image understanding combined with clinical reasoning. This study evaluates the performance of several general-purpose and medically specialized open-source and proprietary VLMs for clinical wound assessment using an expanded, curated dataset of 20 clinically diverse wounds spanning vascular, surgical, ischemic, venous, lymphedema, and amputation-related etiologies. Six VLMs were evaluated using a structured twelve-question clinical framework covering wound classification, infection risk, vascular intervention recommendations, debridement urgency, wound therapy selection, and advanced management planning. Across 20 wound cases and 240 clinician-graded wound-analysis decisions, ChatGPT achieved the highest overall performance with 174/240 correct responses (72.50%), followed by Claude with 149/240 (62.08%). Among the open-source and medically specialized models, HuluMed achieved the strongest performance with 96/240 correct responses (40.00%), followed by Gemma 3 (81/240, 33.75%), MedGemma 4B (62/240, 25.83%), and MedGemma 27B (42/240, 17.50%). The findings suggest that frontier general-purpose multimodal systems currently demonstrate substantially stronger wound-analysis performance than medically specialized alternatives, highlighting the continued importance of broad multimodal reasoning capabilities alongside domain-specific medical knowledge. Although current VLMs demonstrate promising potential for clinical decision support, substantial limitations remain in advanced wound-management reasoning, procedural planning, and autonomous clinical reliability.
arXiv:2606.26520v2 Announce Type: replace Abstract: Mammalian cell-culture processes underpin the manufacture of many biopharmaceuticals, yet keeping a run on track is hard: critical process parameters drift over days, and an off-specification trend is often confirmed too late to intervene. Early-stage, multi-day forecasts could enable timely adjustment of feeding, sampling, and control, but bioprocess forecasting is challenging because measurements are sparse and irregularly sampled, operating conditions are heterogeneous across cell lines and media, and runs with near-identical early behaviour can diverge into different futures. We propose an adaptive framework combining a Gated Bottleneck Latent Ordinary Differential Equation (GB-Latent ODE) with Multi-Path Just-In-Time Fine Tuning (MP-JIT-FT). The GB-Latent ODE augments the stan dard Latent ODE with learnable variable-wise gating and a mask-aware bottleneck that compress high-dimensional sparse inputs, improving learning under limited data. Given a partially observed run, MP-JIT-FT retrieves similar historical trajectories, clusters the local neighbourhood into candidate regimes, and fine-tunes a separate model per regime to produce multiple plausible paths, each with a reconstruction-based confidence score, not a single averaged forecast. We further fuse Raman spectroscopy data: a machine-learning soft sensor turns dense Raman spectra into pseudo-observations that enrich the sparse offline measurements for more robust training. On 38 fed-batch 5L bioreactor runs spanning 14 conditions, MP-JIT-FT with Raman fusion achieves the best average rank and outperforms a global Latent ODE baseline on 8 of 9 target variables. Using local-divergence metrics, we show the multi-path gains are largest when locally similar prefixes diverge, whereas Raman fusion helps most when early dynamics are representative of later behaviour.
arXiv:2407.09632v3 Announce Type: replace-cross Abstract: We introduce a rigorous mathematical framework for Granger causality in extremes, designed to identify causal links from extreme events in time series. Granger causality plays a pivotal role in uncovering directional relationships among time-varying variables. While this notion gains heightened importance during extreme and highly volatile periods, state-of-the-art methods primarily focus on causality within the body of the distribution, often overlooking causal mechanisms that manifest only during extreme events. Our framework is designed to infer causality mainly from extreme events by leveraging the causal tail coefficient. We establish equivalences between causality in extremes and other causal concepts, including (classical) Granger causality, Sims causality, and structural causality. We prove other key properties of Granger causality in extremes and show that the framework is especially helpful under the presence of hidden confounders. We also propose a novel inference method for detecting the presence of Granger causality in extremes from data. Our method is model-free, can handle non-linear and high-dimensional time series, outperforms current state-of-the-art methods in all considered setups, both in performance and speed, and was found to uncover coherent effects when applied to financial and extreme weather observations.
arXiv:2607.04935v1 Announce Type: new Abstract: Runtime estimates affect reservation quality, backfilling opportunities, and queue delay in HPC schedulers. Under heavy tailed workloads, however, averaging over jobs can misrepresent scheduling impact because a small fraction of jobs dominates resource usage. This paper presents an empirical evaluation methodology for HPC job runtime prediction that focuses on the tail, combining GeoAccuracy weighted by resource usage with decile and split analyses. Using production traces from NREL Eagle and ALCF Mira/Intrepid, we compare XGBoost and Last2 against the user provided walltime estimate at submission (UserReq). Across all three datasets, evaluation focused on the tail changes the offline conclusion: MeanAccuracy keeps the methods relatively close, whereas GeoAccuracy reveals clearer separation and makes UserReq's strength in the upper tail visible. In the top decile, UserReq achieves the highest GeoAccuracy and lowest underestimation rate on all three datasets, and this pattern remains stable across rolling splits. We then translate this signal into a simple hybrid scheduling policy that keeps XGBoost for most jobs and routes the top decile by proxy_cost at submission to UserReq. Online replay on four production queues reduces mean wait time by up to 8% and increases backfilled jobs by 50% to 115%. These results show that offline evaluation focused on the tail better characterizes prediction quality relevant to scheduling and informs scheduling policy design.
arXiv:2607.04893v1 Announce Type: cross Abstract: We present a quantum-kinetic linear theory of the gravitational instability in the context of fuzzy dark matter universe. Starting from the Wigner transport equation, we apply Landau's approach to the linearized Wigner--Poisson system and derive a kinetic dispersion relation that incorporates quantum effects exactly by introducing the plasma dispersion function. The growth rate as a function of wavenumber is characterized by a dimensionless quantum-to-thermal ratio $\alpha = k_{\mathrm{qJ}}/k_{\mathrm J}$, where $k_{\mathrm{qJ}}$ and $k_{\mathrm J}$ represent the quantum and thermal Jeans wavenumbers, respectively. We derive an analytic expression for the spectral slope at the cutoff wavenumber, revealing that the spectral shape undergoes a sharp transition across $\alpha \sim 0.5$. This implies a crossover from a thermally dominated kinetic regime, in which collisionless damping occurs via phase mixing and Landau resonance, to a regime dominated by quantum pressure. By applying these results to fuzzy dark matter, we show that the cutoff scale and its spectral shape depend sensitively on both the particle mass and the initial velocity dispersion, suggesting a method for simultaneously constraining these parameters through observations of the matter power spectrum. This framework provides a theoretical basis for future studies on the transition from early-phase thermal states to the formation of Bose-Einstein condensates in galactic structures.
arXiv:2511.01397v2 Announce Type: replace-cross Abstract: The Resource Constrained Shortest Path Problem (RCSPP) is a fundamental combinatorial optimisation problem in which the goal is to find a least-cost path in a directed graph subject to one or more resource constraints. Pull labelling, where a vertex gathers labels from its predecessors rather than pushing them to its successors, is a classical idea; we turn it into a parallel algorithm whose immutable, contention-free bucket storage is designed to scale on modern multi-core hardware. Its central component is an acyclic dependency bucket graph that orders the creation of labels so that buckets can be processed concurrently, without conflicting accesses, and stored as immutable objects. On top of this we introduce i) a highly parallelisable approach at the label-bucket level, ii) an extension to bi-directional search with a dynamic midpoint that emerges from the bucket processing order, and iii) a vectorised dominance criterion that uses vector instructions to speed-up the label comparison with another level of parallelisation. Compared to a baseline version of the algorithm the optimisations result in a speed-up of about 18 times on a set of hard instances and up to 274 times on the instance with the largest speed-up. Against an open implementation of the state-of-the-art bucket graph labelling algorithm, run on the same hardware and instances with each solver in its best parallel configuration, the pull algorithm is 1.9 to 2.4 times faster. The proposed algorithm demonstrates significant computational improvements that may enhance the efficiency of column generation frameworks incorporating resource constrained shortest path sub-problems, potentially enabling the efficient solution of larger-scale instances in routing, scheduling, supply chain and transportation network optimisation applications.
arXiv:2607.04939v1 Announce Type: new Abstract: Large Language Models (LLMs) unlocked new possibilities in automated code writing, becoming the backbone of most code completion tools. While LLMs excel in mainstream languages, they often lack support for the so-called low-resource languages where training data is scarce. As a result, these languages lag behind in the quality of code completion tooling available to their communities. A concrete example is Pharo, a Smalltalk-inspired language whose IDE currently offers only single-token completion. In this work, we report on our experience bringing LLM-based code completion to Pharo. First, we describe an end-to-end pipeline that combines Pharo-specific data curation, continued pre-training and fine-tuning of open code LLMs. Second, we introduce a set of Pharo code completion benchmarks designed to evaluate whether models (i) learn Pharo's syntax and (ii) accurately complete masked Pharo code from real-world GitHub repositories. Third, we show empirically that Pharo-specialized models substantially outperform their original base checkpoints and also exceed the accuracy of substantially larger code LLMs on Pharo completion. Overall, our case study demonstrates the feasibility of bringing strong LLM-based code completion to low-resource programming languages, with models small enough to provide ``real-time'' in-IDE support.
arXiv:2607.04940v1 Announce Type: new Abstract: Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities but remain difficult to train the control policies that can deploy on real hardware due to contact-rich physics and imperfect actuation. We present a sim-to-real reinforcement learning method that leverages dense tactile feedback combined with joint torque sensing to explicitly regulate physical interactions. To enable effective sim-to-real transfer, we introduce (i) a computationally fast tactile simulation that computes distances between dense virtual tactile units and the object via parallel forward kinematics, providing high-rate, high-resolution touch signals needed by RL; (ii) a current-to-torque calibration that eliminates the need for torque sensors on dexterous hands by mapping motor current to joint torque; and (iii) actuator dynamics modeling with randomization to account for non-ideal torque-speed effects and bridge the actuation gaps. Using an asymmetric actor-critic PPO pipeline, we train policies entirely in simulation and deploy them directly to a five-finger hand. The resulting policies demonstrate two essential human-hand skills: (1) command-based controllable grasp force tracking and (2) reorientation of objects in the hand, both of which are robustly executed without fine-tuning on the robot. By combining tactile and torque in the observation space with scalable sensing and actuation modeling, our system provides a practical solution to achieve reliable dexterous manipulation. To our knowledge, this is the first demonstration of controllable grasping on a multi-finger dexterous hand trained entirely in simulation and transferred zero-shot on real hardware.
arXiv:2605.11404v2 Announce Type: replace Abstract: Large language models (LLMs) can simulate human-like reasoning and decision-making in individual agents. LLM-powered multi-agent systems (MAS) combine such agents to simulate population-scale social phenomena such as polarization, information cascades, and market panics. Such studies require attributing macro emergence to individual agents, but existing axiomatic methods scale combinatorially in $N$ and have been confined to $N \lesssim 10^3$, while the phenomena they explain occur at $N \geq 10^6$. We address this gap by adapting Aumann--Shapley path-integral attribution to LLM-powered MAS at million-agent scale; the resulting method satisfies all four axioms, runs three to five orders of magnitude faster than sampled Shapley on the same hardware, and extends feasible axiomatic attribution by over three orders of magnitude (a $1670\times$ jump). We use this method to test the scale gap empirically: across 14 days of public Bluesky data ($1{,}671{,}587$ active users, five topics), we compute the attribution at both full scale and the visibility-biased $N = 10^2$ convenience sample used by small-scale studies, and the two disagree structurally. At full scale the long tail and middle tier jointly carry the majority; the biased small panel shifts about twice that share onto the upper follower tiers ($48\%$ versus $24\%$). We then prove that the disagreement cannot in general be reduced by post-hoc rescaling: an Attribution Scaling Bias theorem shows that a reconciling global rescaling factor exists exactly when the macro indicator is linear over agents, and our nonlinear indicators give residuals of $0.10$--$0.98$. For such nonlinear indicators, full-scale attribution is therefore a requirement rather than a methodological choice.
arXiv:2512.22767v3 Announce Type: replace-cross Abstract: We analyze a new Rydberg gate design based on the original $\pi-2\pi-\pi$ protocol [Jaksch, et. al. Phys. Rev. Lett. {\bf 85}, 2208 (2000)] that is modified to enable high fidelity operation without requiring a strong Rydberg interaction. The gate retains the $\pi-2\pi-\pi$ structure with an additional detuning added to the $2\pi$ pulse on the target qubit. The protocol reaches within a factor of 2.39 (1.68) of the fundamental fidelity limit set by Rydberg lifetime for equal (asymmetric) Rabi frequencies on the control and target qubits. We generalize the gate protocol to arbitrary controlled phases. We design optimal target-qubit phase waveforms to generalize the gate across a range of interaction strengths and we find that, within this family of gates, the constant-phase protocol is time-optimal for a fixed laser Rabi frequency and tunable interaction strength. Quantum control techniques are used to design gates that are robust against variations in Rydberg Rabi frequency or interaction strength.
arXiv:2512.22999v2 Announce Type: replace-cross Abstract: We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain. To this end, we introduce JADAI, a framework that jointly amortizes Bayesian adaptive design and inference by training a policy, a history network, and an inference network end-to-end. The networks minimize a generic loss that aggregates incremental reductions in posterior error along experimental sequences. Inference networks are instantiated with diffusion-based posterior estimators that can approximate high-dimensional and multimodal posteriors at every experimental step. Across standard adaptive design benchmarks, JADAI achieves superior or competitive performance.