The paper demonstrates that structural protection mechanisms are the dominant factor in maintaining high performance for KV cache eviction policies, often surpassing the benefits of complex scoring al…

The paper proposes using a mask-conditioned latent diffusion model to generate synthetic, labeled TEM images for data augmentation, achieving small but measurable performance improvements in defect de…

This paper investigates the limitations of polyconvex constitutive modeling, showing that while theoretically appealing, it can impose overly restrictive constraints and perform poorly in reproducing…

The paper systematically measures the risk of current image-to-3D models generating harmful geometries, finding that these models are effective at reconstruction and existing safeguards are insufficie…

The paper proposes Rowhammer Vulnerability Counter (RVC), a novel framework that improves RowHammer mitigation by tracking a row's actual vulnerability to bit flips rather than relying on simple activ…

The paper proposes a multi-task scientific machine learning framework that jointly predicts key engine health indicators (TGTU, DTGT) and the Remaining Useful Life (RUL) while quantifying prediction u…

The paper introduces OmniMatBench, a comprehensive, human-calibrated multimodal reasoning benchmark covering 19 materials science subfields, revealing that current multimodal language models (MLLMs) h…

The paper demonstrates that enforcing a local conservative finite volume structure is crucial for achieving stable, accurate long-term autoregressive rollouts of plasma transport simulations, outperfo…

The paper formalizes and quantifies the risk of side-channel leakage from public metrology releases by developing a statistical audit framework that yields precise information-theoretic bounds.

This review surveys advanced techniques—including generative models, multimodal learning, and closed-loop workflows—for automated inverse materials design, enabling the targeted discovery of novel cry…

The study models how fiber arrangement affects the transverse mechanical properties of continuous-fiber composites, finding that clustering increases stiffness but decreases tensile strength compared…

The paper introduces a history-aware adaptive Reduced-Order Model (ROM) framework using incremental Singular Value Decomposition (iSVD) that maintains accuracy for online dynamics far beyond the initi…

The paper introduces a novel padding method that leverages crystal symmetry to enhance the encoding of complex inorganic structures, significantly improving the generation of stable, novel materials.

The paper proposes using pseudo-sensitivities, derived from adjoint sensitivity fields, as an optimal conditioning signal in a Bernoulli flow-matching framework to significantly improve the out-of-dis…

The authors demonstrate that a physics foundation model, finetuned on simulation data, can successfully predict complex laboratory fluid dynamics, specifically resolving a long-standing discrepancy in…

The paper introduces COBALT, a Z3 SMT-based formal verification engine, to proactively detect arithmetic vulnerabilities (CWE-190/191/195) in the critical infrastructure surrounding frontier AI models…

The paper systematically characterizes the fault response of the Intel NCS2 accelerator to electromagnetic fault injection, revealing a major degradation mode that is undetectable by standard inferenc…

This perspective reviews advanced design principles for Metal-Organic Frameworks (MOFs) used in water harvesting and details how integrating Artificial Intelligence (AI) can accelerate the discovery o…

The paper introduces a unified Physics-Informed Deep Learning (PIDL) framework that simultaneously enforces physical laws and information-theoretic bounds, demonstrating robust, domain-agnostic entrop…

The paper introduces i-SDT, an intelligent Self-Defending Digital Twin, which enhances cyber-physical security by accurately discriminating various attack types and maintaining safe operation without…