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:2602.15072v1 Announce Type: new Abstract: Accurate polyp segmentation in colonoscopy is essential for cancer prevention but remains challenging due to: (1) high morphological variability (from flat to protruding lesions), (2) strong visual similarity to normal structures such as folds and vessels, and (3) the need for robust multi-scale detection. Existing deep learning approaches suffer from unidirectional processing, weak multi-scale fusion, and the absence of anatomical constraints, often leading to false positives (over-segmentation of normal structures) and false negatives […]

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arXiv:2601.05408v1 Announce Type: new Abstract: Electromagnetic formation flying (EMFF) is challenging due to the complex coupling between the electromagnetic fields generated by each satellite in the formation. To address this challenge, this article uses alternating magnetic field forces (AMFF) to decouple the electromagnetic forces between each pair of satellites. Each satellite’s electromagnetic actuation system is driven by a sum of amplitude-modulated sinusoids, where amplitudes are controlled to achieve desired forces between each pair of satellites. The main contribution […]

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arXiv:2603.12344v1 Announce Type: new Abstract: Molecular Property Prediction (MPP) is a central task in drug discovery. While Large Language Models (LLMs) show promise as generalist models for MPP, their current performance remains below the threshold for practical adoption. We propose TreeKD, a novel knowledge distillation method that transfers complementary knowledge from tree-based specialist models into LLMs. Our approach trains specialist decision trees on functional group features, then verbalizes their learned predictive rules as natural language to enable rule-augmented […]

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Large language models are increasingly evaluated as interactive agents, yet standard agent benchmarks conflate two qualitatively distinct sources of success: semantic tool-use and interface-specific interaction pattern memorization. Because both mechanisms can yield identical task success on the original interface, benchmark scores alone are not identifiable evidence of environment-invariant capability. We propose PIPE, a protocol-level evaluation augmentation for diagnosing interface reliance by minimally rewriting environment interfaces while preserving task semantics and execution behavior. Across 16 environments from AgentBench and […]

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Author(s): Shashwata Bhattacharjee Originally published on Towards AI. The headline writes itself: AI defeats human doctors in medical diagnosis, delivering results in under two seconds versus 13 minutes. But beneath this dramatic 390x speed differential lies a far more nuanced story about specialized model architecture, the evolving role of clinical decision support systems, and the critical distinction between diagnostic speed and comprehensive patient care. The Technical Architecture Behind the Victory Shanghai AI Lab’s Gastrointestinal Multimodal AI represents a […]

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arXiv:2603.24844v1 Announce Type: new Abstract: Given a question, a language model (LM) implicitly encodes a distribution over possible answers. In practice, post-training procedures for LMs often collapse this distribution onto a single dominant mode. While this is generally not a problem for benchmark-style evaluations that assume one correct answer, many real-world tasks inherently involve multiple valid answers or irreducible uncertainty. Examples include medical diagnosis, ambiguous question answering, and settings with incomplete information. In these cases, we would like […]

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