February 2026

arXiv:2602.17807v1 Announce Type: new Abstract: Existing online video segmentation models typically combine a per-frame segmenter with complex specialized tracking modules. While effective, these modules introduce significant architectural complexity and computational overhead. Recent studies suggest that plain Vision Transformer (ViT) encoders, when scaled with sufficient capacity and large-scale pre-training, can conduct accurate image segmentation without requiring specialized modules. Motivated by this observation, we propose the Video Encoder-only Mask Transformer (VidEoMT), a simple encoder-only video segmentation model that eliminates the […]

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arXiv:2602.17805v1 Announce Type: new Abstract: Intent-based cross-chain bridges have emerged as an alternative to traditional interoperability protocols by allowing off-chain entities (emph{solvers}) to immediately fulfill users’ orders by fronting their own liquidity. While improving user experience, this approach introduces new systemic risks, such as solver liquidity concentration and delayed settlement. In this paper, we propose a new class of attacks called emph{liquidity exhaustion attacks} and a replay-based parameterized attack simulation framework. We analyze 3.5 million cross-chain intents that […]

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arXiv:2602.17801v1 Announce Type: new Abstract: A Euclidean noncrossing Steiner $(1+epsilon)$-spanner for a point set $Psubsetmathbb{R}^2$ is a planar straight-line graph that, for any two points $a, b in P$, contains a path whose length is at most $1+epsilon$ times the Euclidean distance between $a$ and $b$. We construct a Euclidean noncrossing Steiner $(1+epsilon)$-spanner with $O(n/epsilon^{3/2})$ edges for any set of $n$ points in the plane. This result improves upon the previous best upper bound of $O(n/epsilon^{4})$ obtained nearly […]

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arXiv:2602.17799v1 Announce Type: new Abstract: Recent advances in Vision Language Models (VLMs) and Vision Foundation Models (VFMs) have opened new opportunities for zero-shot text-guided segmentation of remote sensing imagery. However, most existing approaches still rely on additional trainable components, limiting their generalisation and practical applicability. In this work, we investigate to what extent text-based remote sensing segmentation can be achieved without additional training, by relying solely on existing foundation models. We propose a simple yet effective approach that […]

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arXiv:2602.17798v1 Announce Type: new Abstract: Mixture-of-Experts models rely on learned routers to assign tokens to experts, yet standard softmax gating provides no principled mechanism to control the tradeoff between sparsity and utilization. We propose Grassmannian MoE (GrMoE), a routing framework that operates on the Grassmannian manifold of subspaces, where gating weights arise from the concentration parameters of Matrix Bingham distributions. This construction yields a single, interpretable knob — the concentration matrix $Lambda$ — that continuously controls routing entropy, […]

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arXiv:2602.17794v1 Announce Type: new Abstract: Squatting is one of the most demanding lower-limb movements, requiring substantial muscular effort and coordination. Reducing the physical demands of this task through intelligent and personalized assistance has significant implications, particularly in industries involving repetitive low-level assembly activities. In this study, we evaluated the effectiveness of a neural network controller for a modular Hip-Knee exoskeleton designed to assist squatting tasks. The neural network controller was trained via reinforcement learning (RL) in a physics-based, […]

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arXiv:2602.17793v1 Announce Type: new Abstract: It is a critical task to evalaute HER2 expression level accurately for breast cancer evaluation and targeted treatment therapy selection. However, the standard multi-step Immunohistochemistry (IHC) staining is resource-intensive, expensive, and time-consuming, which is also often unavailable in many areas. Consequently, predicting HER2 levels directly from H&E slides has emerged as a potential alternative solution. It has been shown to be effective to use virtual IHC images from H&E images for automatic HER2 […]

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arXiv:2602.17791v1 Announce Type: new Abstract: Large language models (LLMs) have become popular writing tools among students and may expand access to high-quality feedback for students with less access to traditional writing support. At the same time, LLMs may standardize student voice or invite overreliance. This study examines how adoption of LLM-assisted writing varies across socioeconomic groups and how it relates to outcomes in a high-stakes context: U.S. college admissions. We analyze a de-identified longitudinal dataset of applications to […]

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arXiv:2602.17787v1 Announce Type: new Abstract: Generative model ecosystems increasingly operate as competitive multi-platform markets, where platforms strategically select models from a shared pool and users with heterogeneous preferences choose among them. Understanding how platforms interact, when market equilibria exist, how outcomes are shaped by model-providers, platforms, and user behavior, and how social welfare is affected is critical for fostering a beneficial market environment. In this paper, we formalize a three-layer model-platform-user market game and identify conditions for the […]

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arXiv:2602.17785v1 Announce Type: new Abstract: Monocular depth and pose estimation play an important role in the development of colonoscopy-assisted navigation, as they enable improved screening by reducing blind spots, minimizing the risk of missed or recurrent lesions, and lowering the likelihood of incomplete examinations. However, this task remains challenging due to the presence of texture-less surfaces, complex illumination patterns, deformation, and a lack of in-vivo datasets with reliable ground truth. In this paper, we propose **PRISM** (Pose-Refinement with […]

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