February 2026

arXiv:2602.15156v1 Announce Type: new Abstract: Language models are increasingly used to reason over content they were not trained on, such as new documents, evolving knowledge, and user-specific data. A common approach is retrieval-augmented generation (RAG), which stores verbatim documents externally (as chunks) and retrieves only a relevant subset at inference time for an LLM to reason over. However, this results in inefficient usage of test-time compute (LLM repeatedly reasons over the same documents); moreover, chunk retrieval can inject […]

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arXiv:2602.15155v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have emerged as promising surrogates for large 3D scientific simulations due to their ability to continuously model spatial and conditional fields, yet they face a critical fidelity-speed dilemma: deep MLPs suffer from high inference cost, while efficient embedding-based models lack sufficient expressiveness. To resolve this, we propose the Decoupled Representation Refinement (DRR) architectural paradigm. DRR leverages a deep refiner network, alongside non-parametric transformations, in a one-time offline process to […]

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arXiv:2602.15154v1 Announce Type: new Abstract: High-quality video datasets are foundational for training robust models in tasks like action recognition, phase detection, and event segmentation. However, many real-world video datasets suffer from annotation errors such as *mislabeling*, where segments are assigned incorrect class labels, and *disordering*, where the temporal sequence does not follow the correct progression. These errors are particularly harmful in phase-annotated tasks, where temporal consistency is critical. We propose a novel, model-agnostic method for detecting annotation errors […]

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arXiv:2602.15149v1 Announce Type: new Abstract: We introduce SoliDualSPHysics, a novel open-source and GPU-accelerated software that extends DualSPHysics to enable the numerical simulation of hyperelastic, finite-strain plastic, and brittle fracture behavior in deformable solids within a unified smoothed particle hydrodynamics (SPH) formulation. The software implements a total Lagrangian formulation for solid mechanics that allows direct application of external loads and boundary conditions, enabling independent solid mechanics simulations. Brittle fracture is modeled through a phase-field approach coupled with SPH, allowing […]

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arXiv:2602.15147v1 Announce Type: new Abstract: We introduce a new method, dubbed textbf{textit{Geometric Structure-Preserving Interpolation ({Gamma}-SPIN)}}, to simultaneously preserve physics-constraints inherent in the material parameter limits of the finite-strain Cosserat micropolar model, and satisfy objectivity under superimposed rigid body motions. The method advocates to interpolate the Cosserat rotation tensor using geodesic elements, which maintain objectivity and correctly represent curvature measures. At the same time, it proposes relaxing the interaction between the rotation tensor and the deformation tensor to alleviate […]

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arXiv:2602.15145v1 Announce Type: new Abstract: We investigate a remote monitoring framework with multiple sensing modalities including IoT sensors on the ground, mobile UAVs in the air, and a periodically available satellite constellation. While the IoT sensors cover small areas and remain fixed, the UAVs can move between locations and cover larger areas, and the satellites can observe the entire region but have high latency and low reliability. We divide the deployment region into cells and model it as […]

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arXiv:2602.15143v1 Announce Type: new Abstract: Knowledge distillation is a widely adopted technique for transferring capabilities from LLMs to smaller, more efficient student models. However, unauthorized use of knowledge distillation takes unfair advantage of the considerable effort and cost put into developing frontier models. We investigate methods for modifying teacher-generated reasoning traces to achieve two objectives that deter unauthorized distillation: (1) emph{anti-distillation}, or degrading the training usefulness of query responses, and (2) emph{API watermarking}, which embeds verifiable signatures in […]

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arXiv:2602.15139v1 Announce Type: new Abstract: Accurate QA over classical Islamic texts remains challenging due to domain specific semantics, long context dependencies, and concept sensitive reasoning. Therefore, a new CGRA DeBERTa, a concept guided residual domain augmentation transformer framework, is proposed that enhances theological QA over Hadith corpora. The CGRA DeBERTa builds on a customized DeBERTa transformer backbone with lightweight LoRA based adaptations and a residual concept aware gating mechanism. The customized DeBERTa embedding block learns global and positional […]

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arXiv:2602.15138v1 Announce Type: new Abstract: The study of histopathological subtypes is valuable for the personalisation of effective treatment strategies for ovarian cancer. However, increasing diagnostic workloads present a challenge for UK pathology departments, leading to the rise in AI approaches. While traditional approaches in this field have relied on pre-computed, frozen image features, recent advances have shifted towards end-to-end feature extraction, providing an improvement in accuracy but at the expense of significantly reduced scalability during training and time-consuming […]

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arXiv:2602.15135v1 Announce Type: new Abstract: Passkeys — discoverable WebAuthn credentials synchronised across devices are widely promoted as the future of passwordless authentication. Built on the FIDO2 standard, they eliminate shared secrets and resist phishing while offering usability through platform credential managers. Since their introduction in 2022, major vendors have integrated passkeys into operating systems and browsers, and prominent websites have announced support. Yet the true extent of adoption across the broader web remains unknown. Measuring this is challenging […]

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