digitado

arXiv:2604.09814v1 Announce Type: new Abstract: Medical image segmentation models built on Segment Anything Model (SAM) achieve strong performance on clean benchmarks, yet their reliability often degrades under realistic image corruptions such as noise, blur, motion artifacts, and modality-specific distortions. Existing approaches address either medical-domain adaptation or corruption robustness, but not both jointly. In SAM, we find that these capabilities are concentrated in complementary modules: the image encoder preserves medical priors, while the mask decoder governs corruption robustness. Motivated […]

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arXiv:2604.09813v1 Announce Type: new Abstract: Existing synthetic tool-use corpora are primarily designed for offline supervised fine-tuning, yet reinforcement learning (RL) requires executable environments that support reward-checkable online rollouts. We propose COVERT, a two-stage pipeline that first generates reliable base tool-use trajectories through self-evolving synthesis with multi-level validation, and then applies oracle-preserving augmentations that systematically increase environmental complexity. These augmentations introduce distractor tools, indirect or ambiguous user queries, and noisy, multi-format, or erroneous tool outputs, while strictly preserving oracle […]

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arXiv:2604.09812v1 Announce Type: new Abstract: Recurrent claims present a major challenge for automated fact-checking systems designed to combat misinformation, especially in multilingual settings. While tasks such as claim matching and fact-checked claim retrieval aim to address this problem by linking claim pairs, the broader challenge of effectively representing groups of similar claims that can be resolved with the same fact-check via claim clustering remains relatively underexplored. To address this gap, we introduce Claim2Vec, the first multilingual embedding model […]

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arXiv:2604.09811v1 Announce Type: new Abstract: Effective startup control is critical for the safe and reliable operation of Dual Active Bridge (DAB) converters. Unlike traditional soft-start techniques that rely solely on phase-shift control or fixed dead-time settings, the proposed approach gradually reduces the dead time from a value close to one switching period to the hardware-defined minimum. This enables a smooth buildup of the secondary-side voltage while effectively minimizing voltage overshoot and suppressing inrush current during startup. As a […]

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arXiv:2604.09806v1 Announce Type: new Abstract: We study the standard-form ILP problem $max{ c^top x colon A x = b,; x in Z_{geq 0}^n }$, where $Ain Z^{ktimes n}$ has full row rank. We obtain refined FPT algorithms parameterized by $k$ and $Delta$, the maximum absolute value of a $ktimes k$ minor of $A$. Our approach combines discrepancy-based dynamic programming with matrix discrepancy bounds in Koml’os’ setting. Let $kappa_k$ denote the maximum discrepancy over all matrices with $k$ columns […]

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arXiv:2604.09805v1 Announce Type: new Abstract: Enterprise teams building internal coding agents face a gap between prototype performance and production readiness. The root cause is that technical model quality alone is insufficient — tool design, safety enforcement, state management, and human trust calibration are equally decisive, yet underreported in the literature. We present CodeGen, an internal coding agent at Zup, and show that targeted tool design (e.g., string-replacement edits over full-file rewrites) and layered safety guardrails improved agent reliability […]

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arXiv:2604.09803v1 Announce Type: new Abstract: Multimodal music creation requires models that can both generate audio from high-level cues and edit existing mixtures in a targeted manner. Yet most multimodal music systems are built for a single task and a fixed prompting interface, making their conditioning brittle when guidance is ambiguous, temporally misaligned, or partially missing. Common additive fusion or feature concatenation further weakens cross-modal grounding, often causing prompt drift and spurious musical content during generation and editing. We […]

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arXiv:2604.09800v1 Announce Type: new Abstract: This paper presents an analytical framework to study the geometry arising when a soft continuum arm grasps a planar object. Both the arm centerline and the object boundary are modeled as smooth curves. The grasping problem is formulated as a kinematic boundary following problem, in which the object boundary acts as the arm’s ‘shadow curve’. This formulation leads to a set of reduced kinematic equations expressed in terms of relative geometric shape variables, […]

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arXiv:2604.09799v1 Announce Type: new Abstract: Human activity recognition (HAR) has become a key component of intelligent systems for healthcare monitoring, assistive living, smart environments, and human-computer interaction. Although deep learning has substantially improved HAR performance on multivariate sensor data, the resulting models often remain opaque, limiting trust, reliability, and real-world deployment. Explainable artificial intelligence (XAI) has therefore emerged as a critical direction for making HAR systems more transparent and human-centered. This paper presents a comprehensive review of explainable […]

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arXiv:2604.09795v1 Announce Type: new Abstract: This paper investigates a planar tracking problem between a leader and follower agent. We propose a novel feedback speed control law, paired with a constant bearing steering strategy, to maintain an abreast formation between the two agents. We prove that the proposed control yields asymptotic stability of the closed-loop system when the steering of the leader is known. For the case when the leader’s steering is unavailable to the follower, we show that […]

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