Searcharxiv⌕ Search

SEARCH · Searcharxiv

Search Searcharxiv

Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2Linked to original sources
arXiv2026

Tempora-Fusion: Time-Lock Puzzle with Efficient Verifiable Homomorphic Linear Combination

We present Tempora-Fusion, the first homomorphic TLP scheme with efficient public verification of both individual puzzle solutions and homomorphic linear combinations. Tempora-Fusion lets clients generate puzzles independently, later authorize a linear combination with its own release time, and enables any party to verify the released result without trusted setup or costly asymmetric-key proof systems. Technically, our construction maps independently generated RSA-based puzzles into a common finite field, uses oblivious linear evaluation to refresh blinding factors during evaluation, and embeds a hidden verification structure by encoding messages as polynomials with committed secret roots. We formalize verifiable homomorphic linear-combination TLPs, prove privacy and solution validity in this model, and capture the setting in which the evaluation result may be released before the underlying client puzzles are opened. Our prototype implementation shows that verifying an evaluated result takes less than 3 ms.

cs.CR↗
arXiv2026

Learning to Control PDEs with Differentiable Predictive Control and Time-Integrated Neural Operators

We present a data-driven control framework for partial differential equations (PDEs). Our approach integrates time-integrated deep operator networks (TI-DeepONet) as differentiable PDE surrogate models within differentiable predictive control (DPC), a self-supervised learning framework for constrained neural control policies. The TI-DeepONet architecture learns temporal derivatives and couples them with numerical integrators, while the DPC algorithm uses automatic differentiation to compute policy gradients by backpropagating the expectations of the optimal control loss through the learned TI-DeepONet. This approach enables efficient offline optimization of neural policies without the need for online optimization or supervisory controllers. We empirically demonstrate the proposed method across diverse PDE systems, including the heat, the nonlinear inviscid Burgers', and the reaction-diffusion equations. The learned policies achieve target tracking, constraint satisfaction, and curvature minimization objectives, while generalizing across distributions of initial conditions and parameters. Moreover, we demonstrate four to five orders of magnitude acceleration at inference time compared to nonlinear model predictive control benchmarks. These results highlight the promise of neural operator learning for scalable model-based control of PDEs.

cs.CE↗
arXiv2026

Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning

Signal decay and regime shifts pose recurring challenges for data-driven investment strategies in non-stationary markets, where conventional time-series and machine learning approaches often struggle to generalize beyond historical correlations. While large language models (LLMs) offer strong capabilities for processing unstructured information, their potential to support quantitative factor screening through explicit economic reasoning remains underexplored. Existing factor-based methods typically reduce alphas to numerical time series, overlooking the semantic rationale that determines when a factor is economically relevant. We present Alpha-R1, an RL-aligned LLM framework for context-aware alpha screening. Its core mechanism, semantic gating, evaluates each candidate factor's semantic profile against a dynamically constructed market state description, selecting a sparse subset of factors whose economic rationale aligns with current market conditions. The selection model is trained via group relative policy optimization (GRPO), using realized portfolio returns as the primary reward signal. Under a 12-month out-of-sample evaluation, Alpha-R1 achieves annualized returns of 47.87% on S&P 500 and 40.57% on CSI 300 with Sharpe ratios of 1.62 and 2.23. These results, obtained under a bounded candidate-pool evaluation protocol, provide evidence for second-stage semantic factor reranking in non-stationary markets. The full implementation and resources are available at https://github.com/FinStep-AI/Alpha-R1.

q-fin.TR↗
arXiv2026

ASDA: Automated Skill Distillation and Adaptation for Financial Reasoning

Adapting large language models (LLMs) to specialized financial reasoning typically requires expensive fine-tuning that produces model-locked expertise. Training-free alternatives have emerged, yet our experiments show that leading methods (GEPA and ACE) achieve only marginal gains on the FAMMA financial reasoning benchmark, exposing the limits of unstructured text optimization for complex, multi-step domain reasoning. We introduce Automated Skill Distillation and Adaptation (ASDA), a framework that automatically generates structured skill artifacts through iterative error-corrective learning without modifying model weights. A teacher model analyzes a student model's failures on financial reasoning tasks, clusters errors by subfield and error type, and synthesizes skill files containing reasoning procedures, code templates, and worked examples, which are dynamically injected during inference. Evaluated on FAMMA, ASDA achieves up to +17.33% improvement on arithmetic reasoning and +5.95% on non-arithmetic reasoning, substantially outperforming all training-free baselines. The resulting skill artifacts are human-readable, version-controlled, and compatible with the Agent Skills open standard, offering any organization with a labeled domain dataset a practical and auditable path to domain adaptation without weight access or retraining.

cs.CL↗
arXiv2026

IC-ThermBench: An Open, Progressive Benchmark for Generalizable 2.5D/3D-IC Thermal Learning

Standardized benchmarks are fundamental to reliable progress in AI for EDA, including learning-based thermal modeling. However, existing thermal prediction studies often rely on different datasets, simulators, data splits, preprocessing pipelines, and metrics, while most datasets and implementations remain unavailable, making fair and reproducible comparison difficult. We introduce IC-ThermBench, an open and progressive benchmark that combines established 3D-IC steady-state, transient, and industrial package tasks with a new 50,000-sample 2.5D chiplet extension designed to evaluate progressively broader represented physical variation and cross-package OOD transfer. Five Generalization Scopes cover 3D-IC fixed-design prediction, Within-Family Generalization under layout, material, and boundary-condition variation, and Cross-Package OOD transfer to unseen package systems. We evaluate eight representative baselines under common data, splits, labels, and metrics. Performance degrades gradually from S2 to S4 as represented physical support broadens, but Cross-Package OOD produces a much sharper degradation: the best RMSE and MAE increase from 1.216 and 0.938~K at S4 to 15.99 and 15.00~K at S5, respectively. With only 10 labeled samples per OOD case, target-domain adaptation reduces the best MAE to 2.60~K. IC-ThermBench further provides a unified generation, training, inference, and evaluation pipeline, enabling reproducible and fair comparison of existing and new thermal predictor.

cs.CE↗
arXiv2026

Elastoplastic inherent strain-based topology optimization for residual stress reduction in metal additive manufacturing

This paper proposes a topology optimization method for reducing the residual stress arising in the building process of metal additive manufacturing. First, a layer-by-layer process analysis model based on an elastoplastic inherent strain method is introduced. In this model, the incremental displacement is solved anew at each layer step, and the stress history is explicitly incorporated into the constitutive equation as the stress accumulated up to the previous step, which guarantees the stress continuity across layer interfaces without introducing activation strains. Next, the design sensitivity of this analysis model is derived based on the adjoint method. Taking the pair of the stress and the equivalent plastic strain as the state variables reduces the dependency between layer steps to a one-step recurrence, and the adjoint fields are constructed as a layer-by-layer reverse sweep that reuses the coefficient tensors obtained in the forward analysis. Consequently, the cost of the sensitivity analysis scales linearly with the number of layers and remains of the same order as that of the forward analysis. An optimization problem is then formulated based on the density method to minimize the P-norm of the residual stress at the completion of the building process under the volume and final-use compliance constraints, and the derived sensitivities are verified by comparison with central finite differences. Finally, the proposed method is demonstrated through two- and three-dimensional examples of residual stress minimization under a compliance constraint. The results clarify that, under the elastoplastic analysis, the maximum residual stress is bounded by the yield surface, and the optimization therefore reduces the extent of the yielded and plastic strain accumulating regions rather than the peak stress value.

cs.CE↗
arXiv2026

Buildability Assessment of 3D-Printed Concrete Structures Using Smoothed Mohr-Coulomb Plasticity with Isotropic Hardening

Extrusion-based 3D concrete printing builds structural elements layer upon layer without formwork, so freshly deposited material must carry the growing weight of the structure while it is still gaining strength and stiffness. The largest number of layers that can be printed before failure, the buildability, is therefore central to process design. Its prediction rests above all on how accurately the constitutive model represents fresh concrete. Existing finite element frameworks pair gradual pre-failure hardening only with smooth yield surfaces, which leaves the Mohr-Coulomb surface that best describes fresh concrete restricted to perfect plasticity. This work combines a smoothed Mohr-Coulomb yield surface with nonlinear isotropic hardening of the cohesion, formulated in an updated Lagrangian setting with an activation field for layer deposition. Failure is identified from a tangent-stiffness eigenvalue criterion that matches the analytical self-weight buckling load of a slender wall and is insensitive to geometric imperfection. The framework reproduces the measured collapse of a printed straight wall within one layer and predicts the collapse of a hollow cylinder closer to experiment than existing frameworks. A study over six diameters and six hardening rates shows buildability peaking at intermediate diameters and rising with the hardening rate. Recording how much of its strength the material has used at failure, slender cylinders fail with much still in reserve while intermediate ones use nearly all of it. The two failure modes are therefore identified as the limits of one process rather than separate failure mechanisms.

cs.CE↗
arXiv2026

Risk-Aware Goal-Oriented Bayesian Optimal Experimental Design

Traditional Bayesian optimal experimental design (OED) selects measurements that best inform a model's parameters. However, such measurements can be suboptimal for downstream predictions. Goal-oriented OED targets the prediction directly. However, the existing goal-oriented criteria value all reductions in predictive uncertainty equally, with no way to prioritize rare, high-consequence outcomes. In this article, we develop a risk-aware framework that composes risk at three levels, each generalizing an ingredient of classical $I$- and $G$-optimal design: a deviation measure of the posterior predictive uncertainty (generalizing the predictive variance), a risk measure across the prediction domain (interpolating $I$-optimal averaging and $G$-optimal worst-case selection), and a risk measure over datasets (generalizing the expectation). We generate each level from a regret function in the risk quadrangle, so that one triple specifies a practitioner's risk preference. We relax the design to continuous weights on the unit simplex and construct a nested-quadrature estimator that is differentiable in the design variable. This enables solving the optimal design problem with gradient-based methods, avoiding a combinatorial search over candidate designs. For a linear-Gaussian lognormal model and a nonlinear extension, we derive closed-form objectives. These give exact references against which we verify that the estimator converges. We demonstrate this framework for finding optimal sensor placements in an inverse problem governed by an advection-diffusion equation. We find that the risk-aware designs substantially outperform the expected-information-gain baseline, which is statistically indistinguishable from a random allocation.

stat.ME↗
arXiv2026

Nonlinear path-following via the asymptotic numerical method on a quantum processor

Quantum computing offers a promising avenue for advancing computational methods in science and engineering. In this work, we introduce the quantum asymptotic numerical method (qANM), a framework for solving nonlinear path-following problems using quantum computing. Based on the principle of high-order perturbation techniques, the proposed method uses Taylor series expansions to transform complex nonlinear systems into sequences of linear equations, which are then solved using quantum linear solvers. The central objective of this study is to demonstrate nonlinear path-following with the required linear systems solved on real quantum hardware. To realize this objective, we develop the quantum-enhanced Jacobi method (q-Jacobi), an iterative quantum linear solver used in the hardware experiment. Numerical simulations on a quantum simulator validate the convergence of the method. A highlight of this work is a proof-of-principle experiment on a superconducting quantum processor. Despite the noise inherent in near-term quantum hardware, the experiment achieves 98% accuracy in tracking the nonlinear solution path. We believe this work provides a useful reference for applying quantum computing to nonlinear computational mechanics.

quant-ph↗
arXiv2026

Monolithic solution and null-space condensation of internal variables in finite viscoelasticity

Finite-strain viscoelasticity is commonly discussed from a constitutive perspective, whereas the nonlinear solution architecture induced by time and space discretization is studied less systematically. In this work, we consider a representative viscoelastic model with a strain-like internal variable and focus on the fully discrete coupled problem in the deformation and the internal state. Starting from the underlying energy-dissipation structure, we derive the discrete weak forms and obtain a monolithic Newton system with a naturally non-symmetric block tangent. The increment of the internal variable is eliminated consistently at the level of the linearized system by a Schur complement, and the same reduction is shown to admit a geometric interpretation in terms of a null-space basis of the tangent space of the internal constraint manifold. This yields a genuinely monolithic counterpart to classical nested Gauss-point condensation, in which local constitutive equations are solved separately before the global equilibrium step. Numerical results for two- and three-dimensional Cook's membrane benchmarks show that the proposed strategy retains essentially the same outer Newton behavior as the classical approach while substantially reducing computational cost by avoiding repeated local Newton solves, it also remains convergent for load increments for which the nested scheme fails. Moreover, the numerical study suggests that suitably chosen approximation spaces for the internal variable can yield additional savings in computational effort without significant loss of accuracy. Although presented for finite viscoelasticity, the construction extends naturally to broader classes of thermodynamically consistent internal-variable models.

cs.CE↗
arXiv2026

CMADiff: Cross-Modal Aligned Diffusion for Controllable Protein Generation

AI-assisted protein design has emerged as a critical tool for advancing biotechnology, as deep generative models have demonstrated their reliability in this domain. However, most existing models primarily utilize protein sequence or structural data for training, neglecting the physicochemical properties of proteins.Moreover, they are deficient to control the generation of proteins in intuitive conditions. To address these limitations,we propose CMADiff here, a novel framework that enables controllable protein generation by aligning the physicochemical properties of protein sequences with text-based descriptions through a latent diffusion process. Specifically, CMADiff employs a Conditional Variational Autoencoder (CVAE) to integrate physicochemical features as conditional input, forming a robust latent space that captures biological traits. In this latent space, we apply a conditional diffusion process, which is guided by BioAligner, a contrastive learning-based module that aligns text descriptions with protein features, enabling text-driven control over protein sequence generation. Validated by a series of evaluations including AlphaFold3, the experimental results indicate that CMADiff outperforms protein sequence generation benchmarks and holds strong potential for future applications. The implementation and code are available at https://github.com/HPC-NEAU/PhysChemDiff.

cs.CE↗
arXiv2026

A review of weakly enforced Dirichlet boundary conditions in computational flow analysis

Strongly enforced Dirichlet boundary conditions require highly refined near-wall meshes to resolve steep velocity and thermal gradients. This introduces high computational costs, especially for practical flow simulations. Weakly enforced boundary conditions alleviate this burden by acting as a variationally consistent near-wall model. By allowing a controlled slip at the solid wall, weak enforcement recovers accurate flow quantities on coarse boundary-layer meshes across both incompressible and compressible regimes. Furthermore, weak boundary conditions serve as the fundamental enabling technology for immersogeometric analysis. Because the weak operator evaluates boundary integrals independently of the background mesh, high-fidelity flow analysis can be performed directly on complex geometries without fitting a mesh to the surface. This capability has facilitated direct geometry-to-analysis workflows for boundary-representation CAD models, raw point clouds, photogrammetric reconstructions, and segmented medical images. This review examines the unified mathematical development of the weak boundary condition framework from scalar advection-diffusion equations to the full Navier-Stokes equations, illustrating its versatility and robustness across incompressible and compressible flows, whether in traditional boundary-fitted, sliding-interface, or advanced immersogeometric applications.

math.NA↗
arXiv2026

MedGSSR: Generalizable Medical Image Super-Resolution 3D Reconstruction via Hierarchical Feed-forward Gaussian Splatting

High-resolution volumetric medical imaging is critical for clinical diagnosis, yet acquisition is often limited by scanner hardware, scan time, and for CT, radiation dose. Medical 3D Super-Resolution (Med3DSR) offers a computational alternative, but existing methods commonly rely on per-subject optimization, pretrained priors, or coordinate-based implicit representations, which compromise anatomical fidelity and limit efficiency. To address these limitations, we present MedGSSR, a fully end-to-end feed-forward framework that represents volumes as an explicit 3D Gaussian field for Med3DSR. Unlike coordinate-based implicit functions, our explicit 3D Gaussian representation naturally enhances signal continuity and local high-frequency fidelity. Specifically, MedGSSR explicitly decouples the reconstruction process into coarse-grained structural preservation and fine-grained textural refinement through the proposed Pyramid Anatomical Encoder and a Hierarchical Gaussian Projector. To support arbitrary-scale super-resolution, we introduce sub-voxel Gaussian decomposition and a Differentiable Gaussian Voxelizer that directly queries the continuous 3D intensity field, reducing discretization artifacts. Extensive experiments on MRI and CT benchmarks demonstrate that MedGSSR significantly outperforms state-of-the-art methods. Notably, our framework exhibits robust generalizability across unseen datasets without requiring per-subject optimization, enabling fast inference and high-fidelity volumetric super-resolution in practical clinical settings. Our project webpage, including code, is at https://william2ai.github.io/medgssr

eess.IV↗
arXiv2026

Hyperelastic constitutive model discovery with differentiable finite elements and structure-preserving neural networks

The discovery of constitutive laws from experimentally accessible measurements is a central problem in nonlinear computational mechanics. Many data-driven constitutive identification approaches rely either on paired strain-stress data or on full-field displacement measurements, both of which are difficult to obtain in realistic three-dimensional settings. We present a differentiable finite element framework for the discovery of hyperelastic material laws from partial observations, including boundary-only displacement measurements and global reaction forces. The method embeds the nonlinear finite element equilibrium problem directly into the learning loop, so that candidate strain-energy densities are assessed through the deformation fields and reactions they induce. This formulation enforces mechanical equilibrium as a constraint and allows the loss function to be evaluated only at observed locations. To ensure physical admissibility and promote numerical solvability throughout training, the constitutive response is represented by Hyperelastic Neural Networks, a structure-preserving neural class that enforces residual energy and stress-free conditions, frame indifference, isotropic material symmetry, polyconvexity, coercivity, and controlled volumetric growth by construction. The resulting PDE-constrained learning problem is solved using a quasi-Newton strategy combined with continuation and solver-aware backtracking. Numerical experiments in two- and three-dimensional finite elasticity demonstrate accurate recovery of hyperelastic isotropic responses from boundary-only data, robustness to measurement noise, and generalization across geometries, loading conditions, and boundary conditions.

math.NA↗
arXiv2026

Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoning

Biomedical knowledge resources often either preserve evidence as unstructured text or compress it into flat triples that omit study design, provenance, and quantitative support. Here we present EvidenceNet, a disease-specific dataset of record-level evidence collections and corresponding graph representations derived from full-text biomedical literature. EvidenceNet uses a large language model (LLM)-assisted pipeline to extract experimentally grounded findings as structured evidence records, normalize biomedical entities, score evidence quality, and connect related records through typed semantic relations. We release EvidenceNet-HCC with 7,872 evidence records and a corresponding graph with 10,328 nodes and 49,756 edges, and EvidenceNet-CRC with 6,622 records and a corresponding graph with 8,795 nodes and 39,361 edges. Technical validation shows high component fidelity, including 98.3% field-level extraction accuracy, 100.0% high-confidence entity-link accuracy, 87.5% fusion integrity, and 90.0% semantic relation-type accuracy. Downstream analyses show that the data support retrieval-augmented question answering and graph-based tasks such as future link prediction and target prioritization. These results establish EvidenceNet as a disease-specific biomedical knowledge base dataset for evidence-aware analysis and reuse.

cs.CE↗
arXiv2026

Amortized Neural Optimization for Pre-Layout Signal Integrity Design Space Exploration using Differentiable Surrogates

Pre-layout design space exploration (DSE) for high-speed signal integrity (SI) analysis is often limited by the computational cost of simulations and iterative optimization algorithms within modern electronic design automation (EDA) workflows. While machine learning surrogate models accelerate the simulation step, optimizing designs still requires utilizing iterative black-box search methods. This iterative nature scales poorly and makes multi-corner sweeps computationally expensive. As a solution, this paper proposes amortized neural optimization (ANO) for pre-layout SI design. ANO avoids iterative black-box inference and utilizes a fully differentiable neural network surrogate model from which it extracts gradient information to train a global optimization policy. This does not solve the optimization problem repeatedly at inference, but learns the process offline, which amortizes the computational cost. Once the ANO policy is trained, it maps different channel contexts to near-optimal design parameters in a single deterministic forward pass. The efficiency and accuracy of the ANO framework are demonstrated based on three SI design scenarios, including DDR5 decision feedback equalization (DFE), 9-dimensional SerDes Tx/Rx co-equalization, and DDR3 DQS differential pair eye diagram optimization under intra-pair skew constraints. Trading roughly 10% in optimality compared to instance-specific black-box algorithms results in speedups of three to four orders of magnitude. For a large-scale 320,000-instance multi-corner SerDes sweep optimization, ANO reduces what would have taken days of computation using iterative search algorithms to a single batched forward pass that completes in milliseconds. This transforms computationally expensive SI optimization into real-time and interactive pre-layout DSE.

eess.SP↗
arXiv2026

Forecasting Global Volatility with Predictive Spillover Networks: A Neuro-Econometric Spatio-Temporal Transformer for Asynchronous Financial Markets

Forecasting global realized volatility requires a model that can learn from interconnected markets without treating zero-coded exchange closures as observed zero volatility. We develop PGA-Trans-HAR, a neuro-econometric architecture that combines a rolling ridge-VAR/GFEVD predictive-connectedness network, masked spatio-temporal attention, and a frozen HAR anchor. Missing observations used to estimate the rolling econometric prior are completed only within the trailing information set available at the forecast origin. An asymmetric source mask prevents closed markets from transmitting zero-coded closure signals. A learned, market-specific gate allocates weight between the econometric prior and data-driven spatial attention. A bounded inverse-softplus correction then refines the HAR forecast while preserving positivity. We evaluate eight international equity indices from 2006 to 2022 at 1-, 5-, and 22-union-calendar-day forecast leads, where the target is the one-day realized volatility observed at the corresponding future date. The design uses five-seed ensembles, select-and-refit estimation, structural ablations, HAC-adjusted Diebold--Mariano tests, and block-bootstrap Model Confidence Sets. PGA-Trans-HAR records the lowest cross-market average MAE at the 1-day forecast lead and the lowest average MSE and MAE at the 5- and 22-union-calendar-day forecast leads. Relative to HAR, both losses decline for all eight markets at the 1- and 5-day forecast leads and for seven markets at the 22-day forecast lead. The evidence shows that combining an origin-aligned econometric network with masked attention can improve multi-market volatility forecasts in asynchronous financial environments.

cs.CE↗
arXiv2026

A Computational Pipeline for Patient-Specific Modeling of Thoracic Aortic Aneurysm: From Medical Image to Finite Element Analysis

The aorta is the body's largest arterial vessel, serving as the primary pathway for oxygenated blood within the systemic circulation. Aortic aneurysms consistently rank among the top twenty causes of mortality in the United States. Thoracic aortic aneurysm (TAA) arises from abnormal dilation of the thoracic aorta and remains a clinically significant disease, ranking as one of the leading causes of death in adults. A thoracic aortic aneurysm ruptures when the integrity of all aortic wall layers is compromised due to elevated blood pressure. Currently, three-dimensional computed tomography (3D CT) is considered the gold standard for diagnosing TAA. The geometric characteristics of the aorta, which can be quantified from medical imaging, and stresses on the aortic wall, which can be obtained by finite element analysis (FEA), are critical in evaluating the risk of rupture and dissection. Deep learning based image segmentation has emerged as a reliable method for extracting anatomical regions of interest from medical images. Voxel based segmentation masks of anatomical structures are typically converted into structured mesh representation to enable accurate simulation. Hexahedral meshes are commonly used in finite element simulations of the aorta due to their computational efficiency and superior simulation accuracy. Due to anatomical variability, patient specific modeling enables detailed assessment of individual anatomical and biomechanics behaviors, supporting precise simulations, accurate diagnoses, and personalized treatment strategies. Finite element (FE) simulations provide valuable insights into the biomechanical behaviors of tissues and organs in clinical studies. Developing accurate FE models represents a crucial initial step in establishing a patient-specific, biomechanically based framework for predicting the risk of TAA.

eess.IV↗