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2caaba9d59f72d5a3bb8a562dc2a029e01d6f7529bc3e4dc1e8af7d006c14feb
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2026-01-01T00:00:00-05:00
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From artificial to circular intelligence to support the well-being of our habitat
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arXiv:2512.24131v1 Announce Type: new Abstract: The proliferation of machine learning and artificial intelligence redefines the interaction between the anthropogenic and natural elements of our habitat.The use of monitoring tools, processing facilities and the internet of things supports the assessment of planetary health at any given time through automation. However, these data, natural resources and infrastructure intensive technologies are not neutral on the Earth. As the community of AI practitioners works on the creation of tools with minimal socio-environmental impacts, we contribute to the these efforts by proposing a novel conceptual and procedural framework which we call Circular Intelligence or CIntel. CIntel leverages a bottom-up and community-driven approach to learn from the ability of nature to regenerate and adapt. CIntel incorporates ethical principles in its technical design to preserve the stability of the habitat, while also increasing the well-being of its inhabitants by design.
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https://arxiv.org/abs/2512.24131
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Academic Papers
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2ae37c1c67d7c498ca964703ce1eb427c7fe3bb82d79653131eedd675b3a3b55
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2026-01-01T00:00:00-05:00
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From FPT Decision to FPT Enumeration
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arXiv:2512.24137v1 Announce Type: new Abstract: Fixed-parameter tractable (FPT) algorithms have been successfully applied to many intractable problems -- with a focus on decision and optimization problems. Their aim is to confine the exponential explosion to some parameter, while the time complexity only depends polynomially on the instance size. In contrast, intractable enumeration problems have received comparatively little attention so far. The goal of this work is to study how FPT decision algorithms could be turned into FPT enumeration algorithms. We thus inspect several fundamental approaches for designing FPT decision or optimization algorithms and we present ideas how they can be extended to FPT enumeration algorithms.
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https://arxiv.org/abs/2512.24137
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Academic Papers
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b15b7d579cbe53a2b61c06f6ce9a7ae22db348917b69a00363081d3b6cd1ce6b
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2026-01-01T00:00:00-05:00
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GARDO: Reinforcing Diffusion Models without Reward Hacking
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arXiv:2512.24138v1 Announce Type: new Abstract: Fine-tuning diffusion models via online reinforcement learning (RL) has shown great potential for enhancing text-to-image alignment. However, since precisely specifying a ground-truth objective for visual tasks remains challenging, the models are often optimized using a proxy reward that only partially captures the true goal. This mismatch often leads to reward hacking, where proxy scores increase while real image quality deteriorates and generation diversity collapses. While common solutions add regularization against the reference policy to prevent reward hacking, they compromise sample efficiency and impede the exploration of novel, high-reward regions, as the reference policy is usually sub-optimal. To address the competing demands of sample efficiency, effective exploration, and mitigation of reward hacking, we propose Gated and Adaptive Regularization with Diversity-aware Optimization (GARDO), a versatile framework compatible with various RL algorithms. Our key insight is that regularization need not be applied universally; instead, it is highly effective to selectively penalize a subset of samples that exhibit high uncertainty. To address the exploration challenge, GARDO introduces an adaptive regularization mechanism wherein the reference model is periodically updated to match the capabilities of the online policy, ensuring a relevant regularization target. To address the mode collapse issue in RL, GARDO amplifies the rewards for high-quality samples that also exhibit high diversity, encouraging mode coverage without destabilizing the optimization process. Extensive experiments across diverse proxy rewards and hold-out unseen metrics consistently show that GARDO mitigates reward hacking and enhances generation diversity without sacrificing sample efficiency or exploration, highlighting its effectiveness and robustness.
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https://arxiv.org/abs/2512.24138
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Academic Papers
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284a2d0aebf445c39451e31d5e22399a74734016555d8a5919649ff28aa5bb02
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2026-01-01T00:00:00-05:00
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Colorful Pinball: Density-Weighted Quantile Regression for Conditional Guarantee of Conformal Prediction
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arXiv:2512.24139v1 Announce Type: new Abstract: While conformal prediction provides robust marginal coverage guarantees, achieving reliable conditional coverage for specific inputs remains challenging. Although exact distribution-free conditional coverage is impossible with finite samples, recent work has focused on improving the conditional coverage of standard conformal procedures. Distinct from approaches that target relaxed notions of conditional coverage, we directly minimize the mean squared error of conditional coverage by refining the quantile regression components that underpin many conformal methods. Leveraging a Taylor expansion, we derive a sharp surrogate objective for quantile regression: a density-weighted pinball loss, where the weights are given by the conditional density of the conformity score evaluated at the true quantile. We propose a three-headed quantile network that estimates these weights via finite differences using auxiliary quantile levels at \(1-\alpha \pm \delta\), subsequently fine-tuning the central quantile by optimizing the weighted loss. We provide a theoretical analysis with exact non-asymptotic guarantees characterizing the resulting excess risk. Extensive experiments on diverse high-dimensional real-world datasets demonstrate remarkable improvements in conditional coverage performance.
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https://arxiv.org/abs/2512.24139
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Academic Papers
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ccc352116fdbac6eced3b8b5b68f7d6280c50f6d07a72d4947feb2d77bd47b2a
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2026-01-01T00:00:00-05:00
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Environmental Sound Deepfake Detection Challenge: An Overview
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arXiv:2512.24140v1 Announce Type: new Abstract: Recent progress in audio generation models has made it possible to create highly realistic and immersive soundscapes, which are now widely used in film and virtual-reality-related applications. However, these audio generators also raise concerns about potential misuse, such as producing deceptive audio for fabricated videos or spreading misleading information. Therefore, it is essential to develop effective methods for detecting fake environmental sounds. Existing datasets for environmental sound deepfake detection (ESDD) remain limited in both scale and the diversity of sound categories they cover. To address this gap, we introduced EnvSDD, the first large-scale curated dataset designed for ESDD. Based on EnvSDD, we launched the ESDD Challenge, recognized as one of the ICASSP 2026 Grand Challenges. This paper presents an overview of the ESDD Challenge, including a detailed analysis of the challenge results.
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https://arxiv.org/abs/2512.24140
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Academic Papers
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dfeef0faa914052954d5607f9bc7964e54476ba2f141c084d0254d4f15e1601c
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2026-01-01T00:00:00-05:00
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Activation Steering for Masked Diffusion Language Models
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arXiv:2512.24143v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) generate text through an iterative denoising process. They have recently gained attention due to mask-parallel decoding and competitive performance with autoregressive large language models. However, effective mechanisms for inference-time control and steering in MDLMs remain largely unexplored. We present an activation-steering framework for MDLMs that computes layer-wise steering vectors from a single forward pass using contrastive examples, without simulating the denoising trajectory. These directions are applied at every reverse-diffusion step, yielding an efficient inference-time control mechanism. Experiments on LLaDA-8B-Instruct demonstrate reliable modulation of high-level attributes, with ablations examining the effects of steering across transformer sub-modules and token scope (prompt vs.\ response).
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https://arxiv.org/abs/2512.24143
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Academic Papers
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73f7b2121cf18c6e5310300694dc4688488fe1f71a9e194bc7317a3305527f08
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2026-01-01T00:00:00-05:00
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Paired Seed Evaluation: Statistical Reliability for Learning-Based Simulators
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arXiv:2512.24145v1 Announce Type: new Abstract: Machine learning systems appear stochastic but are deterministically random, as seeded pseudorandom number generators produce identical realisations across executions. Learning-based simulators are widely used to compare algorithms, design choices, and interventions under such dynamics, yet evaluation outcomes often exhibit high variance due to random initialisation and learning stochasticity. We analyse the statistical structure of comparative evaluation in these settings and show that standard independent evaluation designs fail to exploit shared sources of randomness across alternatives. We formalise a paired seed evaluation design in which competing systems are evaluated under identical random seeds, inducing matched realisations of stochastic components and strict variance reduction whenever outcomes are positively correlated at the seed level. This yields tighter confidence intervals, higher statistical power, and effective sample size gains at fixed computational budgets. Empirically, seed-level correlations are typically large and positive, producing order-of-magnitude efficiency gains. Paired seed evaluation is weakly dominant in practice, improving statistical reliability when correlation is present and reducing to independent evaluation without loss of validity when it is not.
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https://arxiv.org/abs/2512.24145
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Academic Papers
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svg
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3311ac0ad1779eaff4ca2095d037dd25b8123e089310555c5dc2117348ae927f
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2026-01-01T00:00:00-05:00
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Taming Preference Mode Collapse via Directional Decoupling Alignment in Diffusion Reinforcement Learning
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arXiv:2512.24146v1 Announce Type: new Abstract: Recent studies have demonstrated significant progress in aligning text-to-image diffusion models with human preference via Reinforcement Learning from Human Feedback. However, while existing methods achieve high scores on automated reward metrics, they often lead to Preference Mode Collapse (PMC)-a specific form of reward hacking where models converge on narrow, high-scoring outputs (e.g., images with monolithic styles or pervasive overexposure), severely degrading generative diversity. In this work, we introduce and quantify this phenomenon, proposing DivGenBench, a novel benchmark designed to measure the extent of PMC. We posit that this collapse is driven by over-optimization along the reward model's inherent biases. Building on this analysis, we propose Directional Decoupling Alignment (D$^2$-Align), a novel framework that mitigates PMC by directionally correcting the reward signal. Specifically, our method first learns a directional correction within the reward model's embedding space while keeping the model frozen. This correction is then applied to the reward signal during the optimization process, preventing the model from collapsing into specific modes and thereby maintaining diversity. Our comprehensive evaluation, combining qualitative analysis with quantitative metrics for both quality and diversity, reveals that D$^2$-Align achieves superior alignment with human preference.
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https://arxiv.org/abs/2512.24146
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Academic Papers
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svg
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84fb323e5268d5b68be86568795613d3e2b0ce4e646b10da24b3011ec8f072ca
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2026-01-01T00:00:00-05:00
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Large Emotional World Model
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arXiv:2512.24149v1 Announce Type: new Abstract: World Models serve as tools for understanding the current state of the world and predicting its future dynamics, with broad application potential across numerous fields. As a key component of world knowledge, emotion significantly influences human decision-making. While existing Large Language Models (LLMs) have shown preliminary capability in capturing world knowledge, they primarily focus on modeling physical-world regularities and lack systematic exploration of emotional factors. In this paper, we first demonstrate the importance of emotion in understanding the world by showing that removing emotionally relevant information degrades reasoning performance. Inspired by theory of mind, we further propose a Large Emotional World Model (LEWM). Specifically, we construct the Emotion-Why-How (EWH) dataset, which integrates emotion into causal relationships and enables reasoning about why actions occur and how emotions drive future world states. Based on this dataset, LEWM explicitly models emotional states alongside visual observations and actions, allowing the world model to predict both future states and emotional transitions. Experimental results show that LEWM more accurately predicts emotion-driven social behaviors while maintaining comparable performance to general world models on basic tasks.
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https://arxiv.org/abs/2512.24149
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Academic Papers
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dbb42184195853c9d6a87b0266cb9dd7518ff7c8bea4f32c1d82bee4c3118130
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2026-01-01T00:00:00-05:00
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Graph-Based Exploration for ARC-AGI-3 Interactive Reasoning Tasks
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arXiv:2512.24156v1 Announce Type: new Abstract: We present a training-free graph-based approach for solving interactive reasoning tasks in the ARC-AGI-3 benchmark. ARC-AGI-3 comprises game-like tasks where agents must infer task mechanics through limited interactions, and adapt to increasing complexity as levels progress. Success requires forming hypotheses, testing them, and tracking discovered mechanics. The benchmark has revealed that state-of-the-art LLMs are currently incapable of reliably solving these tasks. Our method combines vision-based frame processing with systematic state-space exploration using graph-structured representations. It segments visual frames into meaningful components, prioritizes actions based on visual salience, and maintains a directed graph of explored states and transitions. By tracking visited states and tested actions, the agent prioritizes actions that provide the shortest path to untested state-action pairs. On the ARC-AGI-3 Preview Challenge, this structured exploration strategy solves a median of 30 out of 52 levels across six games and ranks 3rd on the private leaderboard, substantially outperforming frontier LLM-based agents. These results demonstrate that explicit graph-structured exploration, even without learning, can serve as a strong baseline for interactive reasoning and underscore the importance of systematic state tracking and action prioritization in sparse-feedback environments where current LLMs fail to capture task dynamics. The code is open source and available at https://github.com/dolphin-in-a-coma/arc-agi-3-just-explore.
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https://arxiv.org/abs/2512.24156
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Academic Papers
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7b5a0b7c437cfa4a6472ecaf6da0e227c9d175b682c5ca90c820b9c7d4280623
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2026-01-01T00:00:00-05:00
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Training Report of TeleChat3-MoE
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arXiv:2512.24157v1 Announce Type: new Abstract: TeleChat3-MoE is the latest series of TeleChat large language models, featuring a Mixture-of-Experts (MoE) architecture with parameter counts ranging from 105 billion to over one trillion,trained end-to-end on Ascend NPU cluster. This technical report mainly presents the underlying training infrastructure that enables reliable and efficient scaling to frontier model sizes. We detail systematic methodologies for operator-level and end-to-end numerical accuracy verification, ensuring consistency across hardware platforms and distributed parallelism strategies. Furthermore, we introduce a suite of performance optimizations, including interleaved pipeline scheduling, attention-aware data scheduling for long-sequence training,hierarchical and overlapped communication for expert parallelism, and DVM-based operator fusion. A systematic parallelization framework, leveraging analytical estimation and integer linear programming, is also proposed to optimize multi-dimensional parallelism configurations. Additionally, we present methodological approaches to cluster-level optimizations, addressing host- and device-bound bottlenecks during large-scale training tasks. These infrastructure advancements yield significant throughput improvements and near-linear scaling on clusters comprising thousands of devices, providing a robust foundation for large-scale language model development on hardware ecosystems.
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https://arxiv.org/abs/2512.24157
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Academic Papers
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e00d423542f48dcacbc268c77e2344b9c23b4b9363b6b1d42de42ba645f16b42
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2026-01-01T00:00:00-05:00
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Developing controlled natural language for formal specification patterns using AI assistants
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arXiv:2512.24159v1 Announce Type: new Abstract: Using an AI assistant, we developed a method for systematically constructing controlled natural language for requirements based on formal specification patterns containing logical attributes. The method involves three stages: 1) compiling a generalized natural language requirement pattern that utilizes all attributes of the formal specification template; 2) generating, using the AI assistant, a corpus of natural language requirement patterns, reduced by partially evaluating attributes (the developed prompt utilizes the generalized template, attribute definitions, and specific formal semantics of the requirement patterns); and 3) formalizing the syntax of the controlled natural language based on an analysis of the grammatical structure of the resulting patterns. The method has been tested for event-driven temporal requirements.
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https://arxiv.org/abs/2512.24159
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Academic Papers
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579bcc2f80224a4479947d58f4aba51da30429e96cb2029cbb531082b0baf194
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2026-01-01T00:00:00-05:00
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Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset
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arXiv:2512.24160v1 Announce Type: new Abstract: We present IMDD-1M, the first large-scale Industrial Multimodal Defect Dataset comprising 1,000,000 aligned image-text pairs, designed to advance multimodal learning for manufacturing and quality inspection. IMDD-1M contains high-resolution real-world defects spanning over 60 material categories and more than 400 defect types, each accompanied by expert-verified annotations and fine-grained textual descriptions detailing defect location, severity, and contextual attributes. This dataset enables a wide spectrum of applications, including classification, segmentation, retrieval, captioning, and generative modeling. Building upon IMDD-1M, we train a diffusion-based vision-language foundation model from scratch, specifically tailored for industrial scenarios. The model serves as a generalizable foundation that can be efficiently adapted to specialized domains through lightweight fine-tuning. With less than 5% of the task-specific data required by dedicated expert models, it achieves comparable performance, highlighting the potential of data-efficient foundation model adaptation for industrial inspection and generation, paving the way for scalable, domain-adaptive, and knowledge-grounded manufacturing intelligence.
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https://arxiv.org/abs/2512.24160
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Academic Papers
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47bc5265511d59ab464455df7d4593ca9ebb45acab04a6bba3605a9d8e3384ea
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2026-01-01T00:00:00-05:00
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Bayesian Self-Distillation for Image Classification
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arXiv:2512.24162v1 Announce Type: new Abstract: Supervised training of deep neural networks for classification typically relies on hard targets, which promote overconfidence and can limit calibration, generalization, and robustness. Self-distillation methods aim to mitigate this by leveraging inter-class and sample-specific information present in the model's own predictions, but often remain dependent on hard targets, reducing their effectiveness. With this in mind, we propose Bayesian Self-Distillation (BSD), a principled method for constructing sample-specific target distributions via Bayesian inference using the model's own predictions. Unlike existing approaches, BSD does not rely on hard targets after initialization. BSD consistently yields higher test accuracy (e.g. +1.4% for ResNet-50 on CIFAR-100) and significantly lower Expected Calibration Error (ECE) (-40% ResNet-50, CIFAR-100) than existing architecture-preserving self-distillation methods for a range of deep architectures and datasets. Additional benefits include improved robustness against data corruptions, perturbations, and label noise. When combined with a contrastive loss, BSD achieves state-of-the-art robustness under label noise for single-stage, single-network methods.
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https://arxiv.org/abs/2512.24162
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Academic Papers
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05818c9990aa54196b8f7e480e4b825a69940e559e372b6c40fe1c6f077adc92
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2026-01-01T00:00:00-05:00
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DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models
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arXiv:2512.24165v1 Announce Type: new Abstract: While recent Multimodal Large Language Models (MLLMs) have attained significant strides in multimodal reasoning, their reasoning processes remain predominantly text-centric, leading to suboptimal performance in complex long-horizon, vision-centric tasks. In this paper, we establish a novel Generative Multimodal Reasoning paradigm and introduce DiffThinker, a diffusion-based reasoning framework. Conceptually, DiffThinker reformulates multimodal reasoning as a native generative image-to-image task, achieving superior logical consistency and spatial precision in vision-centric tasks. We perform a systematic comparison between DiffThinker and MLLMs, providing the first in-depth investigation into the intrinsic characteristics of this paradigm, revealing four core properties: efficiency, controllability, native parallelism, and collaboration. Extensive experiments across four domains (sequential planning, combinatorial optimization, constraint satisfaction, and spatial configuration) demonstrate that DiffThinker significantly outperforms leading closed source models including GPT-5 (+314.2\%) and Gemini-3-Flash (+111.6\%), as well as the fine-tuned Qwen3-VL-32B baseline (+39.0\%), highlighting generative multimodal reasoning as a promising approach for vision-centric reasoning.
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https://arxiv.org/abs/2512.24165
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Academic Papers
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a32f0b47bbb320cb38673c1ab617346a2c962831bc33d2407aa5ba6c93ca966d
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2026-01-01T00:00:00-05:00
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External Human-Machine Interface based on Intent Recognition: Framework Design and Experimental Validation
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arXiv:2512.24166v1 Announce Type: new Abstract: Increasing autonomous vehicles (AVs) in transportation systems makes effective interactions between AVs and pedestrians indispensable. External human--machine interface (eHMI), which employs visual or auditory cues to explicitly convey vehicle behaviors can compensate for the loss of human-like interactions and enhance AV--pedestrian cooperation. To facilitate faster intent convergence between pedestrian and AVs, this study incorporates an adaptive interaction mechanism into eHMI based on pedestrian intent recognition, namely IR-eHMI. IR-eHMI dynamically detects and infers the behavioral intentions of both pedestrians and AVs through identifying their cooperation states. The proposed interaction framework is implemented and evaluated on a virtual reality (VR) experimental platform to demonstrate its effectiveness through statistical analysis. Experimental results show that IR-eHMI significantly improves crossing efficiency, reduces gaze distraction while maintaining interaction safety compared to traditional fixed-distance eHMI. This adaptive and explicit interaction mode introduces an innovative procedural paradigm for AV--pedestrian cooperation.
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https://arxiv.org/abs/2512.24166
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Academic Papers
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0beb86a098c6b01904d89c9fce0959bad923594fae46f7ad25c374f0f42754bc
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2026-01-01T00:00:00-05:00
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Hybrid Voltage and Current Control Method for Harmonic Mitigation of Single-Phase AC Loads in DC Microgrids
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arXiv:2512.24170v1 Announce Type: new Abstract: DC microgrids provide an efficient framework for the interconnection of DC distributed energy resources (DERs) and DC loads. To continue to supply legacy single-phase AC loads, DC/AC converters can be integrated in the DC microgrid. The oscillatory instantaneous power of the single-phase AC load translates into a harmonic current on the converter's DC side, which increases the losses and causes unwanted voltage harmonics in the DC microgrid. To mitigate this issue, this paper proposes a hybrid voltage and current control method (HCM) for DERs. This scheme consists of an inner current control loop and an outer control layer which determines the reference for the inner loop. The outer control layer combines the DC voltage control loop with an output harmonic current control loop. This hybrid structure enables simultaneous regulation of the DC components of the DER output voltage and control of the harmonic component of the DER output current in accordance with the local single-phase AC load's demand. Frequency-domain analysis of the proposed method is presented to demonstrate the DC voltage and harmonic current loops are decoupled and there is no unwanted interaction between them. Additionally, time-domain response of the proposed scheme is validated through hardware-in-the-loop test results.
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https://arxiv.org/abs/2512.24170
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Academic Papers
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7cd76fb1731cc47e2b52c4c8b7c9d90ee247e7becc9e23478c827faf17776a90
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2026-01-01T00:00:00-05:00
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Deep Global Clustering for Hyperspectral Image Segmentation: Concepts, Applications, and Open Challenges
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arXiv:2512.24172v1 Announce Type: new Abstract: Hyperspectral imaging (HSI) analysis faces computational bottlenecks due to massive data volumes that exceed available memory. While foundation models pre-trained on large remote sensing datasets show promise, their learned representations often fail to transfer to domain-specific applications like close-range agricultural monitoring where spectral signatures, spatial scales, and semantic targets differ fundamentally. This report presents Deep Global Clustering (DGC), a conceptual framework for memory-efficient HSI segmentation that learns global clustering structure from local patch observations without pre-training. DGC operates on small patches with overlapping regions to enforce consistency, enabling training in under 30 minutes on consumer hardware while maintaining constant memory usage. On a leaf disease dataset, DGC achieves background-tissue separation (mean IoU 0.925) and demonstrates unsupervised disease detection through navigable semantic granularity. However, the framework suffers from optimization instability rooted in multi-objective loss balancing: meaningful representations emerge rapidly but degrade due to cluster over-merging in feature space. We position this work as intellectual scaffolding - the design philosophy has merit, but stable implementation requires principled approaches to dynamic loss balancing. Code and data are available at https://github.com/b05611038/HSI_global_clustering.
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https://arxiv.org/abs/2512.24172
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Academic Papers
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daa18822db4934674b13e701d28fbe2552417a492f70cd42478c7a156f82f9f2
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2026-01-01T00:00:00-05:00
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Guiding a Diffusion Transformer with the Internal Dynamics of Itself
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arXiv:2512.24176v1 Announce Type: new Abstract: The diffusion model presents a powerful ability to capture the entire (conditional) data distribution. However, due to the lack of sufficient training and data to learn to cover low-probability areas, the model will be penalized for failing to generate high-quality images corresponding to these areas. To achieve better generation quality, guidance strategies such as classifier free guidance (CFG) can guide the samples to the high-probability areas during the sampling stage. However, the standard CFG often leads to over-simplified or distorted samples. On the other hand, the alternative line of guiding diffusion model with its bad version is limited by carefully designed degradation strategies, extra training and additional sampling steps. In this paper, we proposed a simple yet effective strategy Internal Guidance (IG), which introduces an auxiliary supervision on the intermediate layer during training process and extrapolates the intermediate and deep layer's outputs to obtain generative results during sampling process. This simple strategy yields significant improvements in both training efficiency and generation quality on various baselines. On ImageNet 256x256, SiT-XL/2+IG achieves FID=5.31 and FID=1.75 at 80 and 800 epochs. More impressively, LightningDiT-XL/1+IG achieves FID=1.34 which achieves a large margin between all of these methods. Combined with CFG, LightningDiT-XL/1+IG achieves the current state-of-the-art FID of 1.19.
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https://arxiv.org/abs/2512.24176
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Academic Papers
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86c34032bc23baa3b728d53f8439cf4fe3526aba01e026facee9e4b8046e5f61
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2026-01-01T00:00:00-05:00
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Now or Never: Continuous Surveillance AIoT System for Ephemeral Events in Intermittent Sensor Networks
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arXiv:2512.24179v1 Announce Type: new Abstract: Wilderness monitoring tasks, such as poaching surveillance and forest fire detection, require pervasive and high-accuracy sensing. While AIoT offers a promising path, covering vast, inaccessible regions necessitates the massive deployment of maintenance-free, battery-less nodes with limited computational resources. However, these constraints create a critical `Availability Gap.' Conventional intermittent operations prioritize computation throughput, forcing sensors to sleep during energy buffering. Consequently, systems miss ephemeral, `now-or-never' events (e.g., Vocalizations of natural monuments or Fire), which is fatal for detecting rare but high-stakes anomalies. To address this, we propose an Energy-aware Elastic Split Computing Algorithm that prioritizes continuous sensing by dynamically offloading tasks to energy-rich neighbors. Preliminary results demonstrate stable monitoring of an additional $2,496\;\text{m}^2$ and the capture of approximately 103 more critical events per day. Ultimately, this algorithm establishes a robust foundation for building resilient, fail-safe surveillance systems even on resource-constrained nodes.
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https://arxiv.org/abs/2512.24179
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Academic Papers
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6ad52b1d3f6abd0422e7966f0c59c3f9013ae6ed94725e3d6cd6120109cd26d6
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2026-01-01T00:00:00-05:00
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MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiring
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arXiv:2512.24181v1 Announce Type: new Abstract: Recent advancements in Large Language Models (LLMs) have demonstrated significant promise in clinical diagnosis. However, current models struggle to emulate the iterative, diagnostic hypothesis-driven reasoning of real clinical scenarios. Specifically, current LLMs suffer from three critical limitations: (1) generating hallucinated medical content due to weak grounding in verified knowledge, (2) asking redundant or inefficient questions rather than discriminative ones that hinder diagnostic progress, and (3) losing coherence over multi-turn dialogues, leading to contradictory or inconsistent conclusions. To address these challenges, we propose MedKGI, a diagnostic framework grounded in clinical practices. MedKGI integrates a medical knowledge graph (KG) to constrain reasoning to validated medical ontologies, selects questions based on information gain to maximize diagnostic efficiency, and adopts an OSCE-format structured state to maintain consistent evidence tracking across turns. Experiments on clinical benchmarks show that MedKGI outperforms strong LLM baselines in both diagnostic accuracy and inquiry efficiency, improving dialogue efficiency by 30% on average while maintaining state-of-the-art accuracy.
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https://arxiv.org/abs/2512.24181
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Academic Papers
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e6c67ad10ee6247bc5182e0fd137af9b9d494cab2f631c00e0b568e7dda5ccec
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2026-01-01T00:00:00-05:00
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CoHalLo: code hallucination localization via probing hidden layer vector
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arXiv:2512.24183v1 Announce Type: new Abstract: The localization of code hallucinations aims to identify specific lines of code containing hallucinations, helping developers to improve the reliability of AI-generated code more efficiently. Although recent studies have adopted several methods to detect code hallucination, most of these approaches remain limited to coarse-grained detection and lack specialized techniques for fine-grained hallucination localization. This study introduces a novel method, called CoHalLo, which achieves line-level code hallucination localization by probing the hidden-layer vectors from hallucination detection models. CoHalLo uncovers the key syntactic information driving the model's hallucination judgments and locates the hallucinating code lines accordingly. Specifically, we first fine-tune the hallucination detection model on manually annotated datasets to ensure that it learns features pertinent to code syntactic information. Subsequently, we designed a probe network that projects high-dimensional latent vectors onto a low-dimensional syntactic subspace, generating vector tuples and reconstructing the predicted abstract syntax tree (P-AST). By comparing P-AST with the original abstract syntax tree (O-AST) extracted from the input AI-generated code, we identify the key syntactic structures associated with hallucinations. This information is then used to pinpoint hallucinated code lines. To evaluate CoHalLo's performance, we manually collected a dataset of code hallucinations. The experimental results show that CoHalLo achieves a Top-1 accuracy of 0.4253, Top-3 accuracy of 0.6149, Top-5 accuracy of 0.7356, Top-10 accuracy of 0.8333, IFA of 5.73, Recall@1% Effort of 0.052721, and Effort@20% Recall of 0.155269, which outperforms the baseline methods.
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https://arxiv.org/abs/2512.24183
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Academic Papers
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7e67b7725d536a7bf786be8289989a37f1d4e962e0015b5d3abf41f4114ad765
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2026-01-01T00:00:00-05:00
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SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents
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arXiv:2512.24189v1 Announce Type: new Abstract: We introduce SCP: the Science Context Protocol, an open-source standard designed to accelerate discovery by enabling a global network of autonomous scientific agents. SCP is built on two foundational pillars: (1) Unified Resource Integration: At its core, SCP provides a universal specification for describing and invoking scientific resources, spanning software tools, models, datasets, and physical instruments. This protocol-level standardization enables AI agents and applications to discover, call, and compose capabilities seamlessly across disparate platforms and institutional boundaries. (2) Orchestrated Experiment Lifecycle Management: SCP complements the protocol with a secure service architecture, which comprises a centralized SCP Hub and federated SCP Servers. This architecture manages the complete experiment lifecycle (registration, planning, execution, monitoring, and archival), enforces fine-grained authentication and authorization, and orchestrates traceable, end-to-end workflows that bridge computational and physical laboratories. Based on SCP, we have constructed a scientific discovery platform that offers researchers and agents a large-scale ecosystem of more than 1,600 tool resources. Across diverse use cases, SCP facilitates secure, large-scale collaboration between heterogeneous AI systems and human researchers while significantly reducing integration overhead and enhancing reproducibility. By standardizing scientific context and tool orchestration at the protocol level, SCP establishes essential infrastructure for scalable, multi-institution, agent-driven science.
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https://arxiv.org/abs/2512.24189
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Academic Papers
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30248221d48d96dc676416d9a2794950c5ee24d718f523b6de32d95e28d17014
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2026-01-01T00:00:00-05:00
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PointRAFT: 3D deep learning for high-throughput prediction of potato tuber weight from partial point clouds
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arXiv:2512.24193v1 Announce Type: new Abstract: Potato yield is a key indicator for optimizing cultivation practices in agriculture. Potato yield can be estimated on harvesters using RGB-D cameras, which capture three-dimensional (3D) information of individual tubers moving along the conveyor belt. However, point clouds reconstructed from RGB-D images are incomplete due to self-occlusion, leading to systematic underestimation of tuber weight. To address this, we introduce PointRAFT, a high-throughput point cloud regression network that directly predicts continuous 3D shape properties, such as tuber weight, from partial point clouds. Rather than reconstructing full 3D geometry, PointRAFT infers target values directly from raw 3D data. Its key architectural novelty is an object height embedding that incorporates tuber height as an additional geometric cue, improving weight prediction under practical harvesting conditions. PointRAFT was trained and evaluated on 26,688 partial point clouds collected from 859 potato tubers across four cultivars and three growing seasons on an operational harvester in Japan. On a test set of 5,254 point clouds from 172 tubers, PointRAFT achieved a mean absolute error of 12.0 g and a root mean squared error of 17.2 g, substantially outperforming a linear regression baseline and a standard PointNet++ regression network. With an average inference time of 6.3 ms per point cloud, PointRAFT supports processing rates of up to 150 tubers per second, meeting the high-throughput requirements of commercial potato harvesters. Beyond potato weight estimation, PointRAFT provides a versatile regression network applicable to a wide range of 3D phenotyping and robotic perception tasks. The code, network weights, and a subset of the dataset are publicly available at https://github.com/pieterblok/pointraft.git.
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https://arxiv.org/abs/2512.24193
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Academic Papers
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2f7f071d0ddf3b93305ac497b0b9133549448eddfaace6e202a70ed6939c366c
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2026-01-01T00:00:00-05:00
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CorGi: Contribution-Guided Block-Wise Interval Caching for Training-Free Acceleration of Diffusion Transformers
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arXiv:2512.24195v1 Announce Type: new Abstract: Diffusion transformer (DiT) achieves remarkable performance in visual generation, but its iterative denoising process combined with larger capacity leads to a high inference cost. Recent works have demonstrated that the iterative denoising process of DiT models involves substantial redundant computation across steps. To effectively reduce the redundant computation in DiT, we propose CorGi (Contribution-Guided Block-Wise Interval Caching), training-free DiT inference acceleration framework that selectively reuses the outputs of transformer blocks in DiT across denoising steps. CorGi caches low-contribution blocks and reuses them in later steps within each interval to reduce redundant computation while preserving generation quality. For text-to-image tasks, we further propose CorGi+, which leverages per-block cross-attention maps to identify salient tokens and applies partial attention updates to protect important object details. Evaluation on the state-of-the-art DiT models demonstrates that CorGi and CorGi+ achieve up to 2.0x speedup on average, while preserving high generation quality.
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https://arxiv.org/abs/2512.24195
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Academic Papers
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8c12bc464c4ac6e6fc27831a51043c52429886fdbca947bf2afebae137377e4b
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2026-01-01T00:00:00-05:00
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PartMotionEdit: Fine-Grained Text-Driven 3D Human Motion Editing via Part-Level Modulation
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arXiv:2512.24200v1 Announce Type: new Abstract: Existing text-driven 3D human motion editing methods have demonstrated significant progress, but are still difficult to precisely control over detailed, part-specific motions due to their global modeling nature. In this paper, we propose PartMotionEdit, a novel fine-grained motion editing framework that operates via part-level semantic modulation. The core of PartMotionEdit is a Part-aware Motion Modulation (PMM) module, which builds upon a predefined five-part body decomposition. PMM dynamically predicts time-varying modulation weights for each body part, enabling precise and interpretable editing of local motions. To guide the training of PMM, we also introduce a part-level similarity curve supervision mechanism enhanced with dual-layer normalization. This mechanism assists PMM in learning semantically consistent and editable distributions across all body parts. Furthermore, we design a Bidirectional Motion Interaction (BMI) module. It leverages bidirectional cross-modal attention to achieve more accurate semantic alignment between textual instructions and motion semantics. Extensive quantitative and qualitative evaluations on a well-known benchmark demonstrate that PartMotionEdit outperforms the state-of-the-art methods.
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https://arxiv.org/abs/2512.24200
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Academic Papers
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11fa794eb3f2bdaa60c2b8543124221a2db5ce2d83a69633ff858b35f2c9a995
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2026-01-01T00:00:00-05:00
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BATISNet: Instance Segmentation of Tooth Point Clouds with Boundary Awareness
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arXiv:2512.24201v1 Announce Type: new Abstract: Accurate segmentation of the tooth point cloud is of great significance for diagnosis clinical assisting and treatment planning. Existing methods mostly employ semantic segmentation, focusing on the semantic feature between different types of teeth. However, due to the tightly packed structure of teeth, unclear boundaries, and the diversity of complex cases such as missing teeth, malposed teeth, semantic segmentation often struggles to achieve satisfactory results when dealing with complex dental cases. To address these issues, this paper propose BATISNet, a boundary-aware instance network for tooth point cloud segmentation. This network model consists of a feature extraction backbone and an instance segmentation module. It not only focuses on extracting the semantic features of different types of teeth but also learns the instance features of individual teeth. It helps achieve more robust and accurate tooth instance segmentation in complex clinical scenarios such as missing teeth and malposed teeth. Additionally, to further enhance the completeness and accuracy of tooth boundary segmentation, a boundary-aware loss function is designed to specifically supervise the boundary segmentation between instances. It mitigates effectively tooth adhesion and boundary ambiguity issues. Extensive experimental results show that BATISNet outperforms existing methods in tooth integrity segmentation, providing more reliable and detailed data support for practical clinical applications.
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https://arxiv.org/abs/2512.24201
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Academic Papers
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c2b5a55509ea7ddbba706d5db4f87cb7e578592e4a55a50e6e783908b4f2a271
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2026-01-01T00:00:00-05:00
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Micro-Macro Tensor Neural Surrogates for Uncertainty Quantification in Collisional Plasma
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arXiv:2512.24205v1 Announce Type: new Abstract: Plasma kinetic equations exhibit pronounced sensitivity to microscopic perturbations in model parameters and data, making reliable and efficient uncertainty quantification (UQ) essential for predictive simulations. However, the cost of uncertainty sampling, the high-dimensional phase space, and multiscale stiffness pose severe challenges to both computational efficiency and error control in traditional numerical methods. These aspects are further emphasized in presence of collisions where the high-dimensional nonlocal collision integrations and conservation properties pose severe constraints. To overcome this, we present a variance-reduced Monte Carlo framework for UQ in the Vlasov--Poisson--Landau (VPL) system, in which neural network surrogates replace the multiple costly evaluations of the Landau collision term. The method couples a high-fidelity, asymptotic-preserving VPL solver with inexpensive, strongly correlated surrogates based on the Vlasov--Poisson--Fokker--Planck (VPFP) and Euler--Poisson (EP) equations. For the surrogate models, we introduce a generalization of the separable physics-informed neural network (SPINN), developing a class of tensor neural networks based on an anisotropic micro-macro decomposition, to reduce velocity-moment costs, model complexity, and the curse of dimensionality. To further increase correlation with VPL, we calibrate the VPFP model and design an asymptotic-preserving SPINN whose small- and large-Knudsen limits recover the EP and VP systems, respectively. Numerical experiments show substantial variance reduction over standard Monte Carlo, accurate statistics with far fewer high-fidelity samples, and lower wall-clock time, while maintaining robustness to stochastic dimension.
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https://arxiv.org/abs/2512.24205
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Academic Papers
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10479f957b8dbd64ae1ed9ab91650ffb4277ae5dbe531c4c76278d65f558eadb
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2026-01-01T00:00:00-05:00
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GR-Dexter Technical Report
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arXiv:2512.24210v1 Announce Type: new Abstract: Vision-language-action (VLA) models have enabled language-conditioned, long-horizon robot manipulation, but most existing systems are limited to grippers. Scaling VLA policies to bimanual robots with high degree-of-freedom (DoF) dexterous hands remains challenging due to the expanded action space, frequent hand-object occlusions, and the cost of collecting real-robot data. We present GR-Dexter, a holistic hardware-model-data framework for VLA-based generalist manipulation on a bimanual dexterous-hand robot. Our approach combines the design of a compact 21-DoF robotic hand, an intuitive bimanual teleoperation system for real-robot data collection, and a training recipe that leverages teleoperated robot trajectories together with large-scale vision-language and carefully curated cross-embodiment datasets. Across real-world evaluations spanning long-horizon everyday manipulation and generalizable pick-and-place, GR-Dexter achieves strong in-domain performance and improved robustness to unseen objects and unseen instructions. We hope GR-Dexter serves as a practical step toward generalist dexterous-hand robotic manipulation.
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https://arxiv.org/abs/2512.24210
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Academic Papers
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a8bb78a9af8d5deef72bfd3ef4382573a14f73a68cf1d61f082bf49d3931ce25
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2026-01-01T00:00:00-05:00
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RANGER: A Monocular Zero-Shot Semantic Navigation Framework through Contextual Adaptation
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arXiv:2512.24212v1 Announce Type: new Abstract: Efficiently finding targets in complex environments is fundamental to real-world embodied applications. While recent advances in multimodal foundation models have enabled zero-shot object goal navigation, allowing robots to search for arbitrary objects without fine-tuning, existing methods face two key limitations: (1) heavy reliance on precise depth and pose information provided by simulators, which restricts applicability in real-world scenarios; and (2) lack of in-context learning (ICL) capability, making it difficult to quickly adapt to new environments, as in leveraging short videos. To address these challenges, we propose RANGER, a novel zero-shot, open-vocabulary semantic navigation framework that operates using only a monocular camera. Leveraging powerful 3D foundation models, RANGER eliminates the dependency on depth and pose while exhibiting strong ICL capability. By simply observing a short video of a new environment, the system can also significantly improve task efficiency without requiring architectural modifications or fine-tuning. The framework integrates several key components: keyframe-based 3D reconstruction, semantic point cloud generation, vision-language model (VLM)-driven exploration value estimation, high-level adaptive waypoint selection, and low-level action execution. Experiments on the HM3D benchmark and real-world environments demonstrate that RANGER achieves competitive performance in terms of navigation success rate and exploration efficiency, while showing superior ICL adaptability, with no previous 3D mapping of the environment required.
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https://arxiv.org/abs/2512.24212
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Academic Papers
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628029dab96362daf41595b449a438865de474a538047289564647435b4f2936
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2026-01-01T00:00:00-05:00
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Medical Image Classification on Imbalanced Data Using ProGAN and SMA-Optimized ResNet: Application to COVID-19
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arXiv:2512.24214v1 Announce Type: new Abstract: The challenge of imbalanced data is prominent in medical image classification. This challenge arises when there is a significant disparity in the number of images belonging to a particular class, such as the presence or absence of a specific disease, as compared to the number of images belonging to other classes. This issue is especially notable during pandemics, which may result in an even more significant imbalance in the dataset. Researchers have employed various approaches in recent years to detect COVID-19 infected individuals accurately and quickly, with artificial intelligence and machine learning algorithms at the forefront. However, the lack of sufficient and balanced data remains a significant obstacle to these methods. This study addresses the challenge by proposing a progressive generative adversarial network to generate synthetic data to supplement the real ones. The proposed method suggests a weighted approach to combine synthetic data with real ones before inputting it into a deep network classifier. A multi-objective meta-heuristic population-based optimization algorithm is employed to optimize the hyper-parameters of the classifier. The proposed model exhibits superior cross-validated metrics compared to existing methods when applied to a large and imbalanced chest X-ray image dataset of COVID-19. The proposed model achieves 95.5% and 98.5% accuracy for 4-class and 2-class imbalanced classification problems, respectively. The successful experimental outcomes demonstrate the effectiveness of the proposed model in classifying medical images using imbalanced data during pandemics.
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https://arxiv.org/abs/2512.24214
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Academic Papers
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fb8eff06d093958cabcf95d29304f462a6198a92ca8177a6a94587491bcfd209
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2026-01-01T00:00:00-05:00
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Efficient Decoding of Twisted GRS Codes and Roth--Lempel Codes
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arXiv:2512.24217v1 Announce Type: new Abstract: MDS codes play a central role in practice due to their broad applications. To date, most known MDS codes are generalized Reed-Solomon (GRS) codes, leaving codes that are not equivalent to GRS codes comparatively less understood. Studying this non-GRS regime is therefore of intrinsic theoretical interest, and is also practically relevant since the strong algebraic structure of GRS codes can be undesirable in cryptographic settings. Among the known non-GRS codes, twisted generalized Reed-Solomon (TGRS) codes and Roth-Lempel codes are two representative families of non-GRS codes that have attracted significant attention. Though substantial work has been devoted to the construction and structural analysis of TGRS and Roth-Lempel codes, comparatively little attention has been paid to their decoding, and many problems remain open. In this paper, we propose list and unique decoding algorithms for TGRS codes and Roth-Lempel codes based on the Guruswami-Sudan algorithm. Under suitable parameter conditions, our algorithms achieve near-linear running time in the code length, improving upon the previously best-known quadratic-time complexity. Our TGRS decoder supports fixed-rate TGRS codes with up to O(n^2) twists, substantially extending prior work that only handled the single-twist case. For Roth-Lempel codes, we provide what appears to be the first efficient decoder. Moreover, our list decoders surpass the classical unique-decoding radius for a broad range of parameters. Finally, we incorporate algebraic manipulation detection (AMD) codes into the list-decoding framework, enabling recovery of the correct message from the output list with high probability.
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https://arxiv.org/abs/2512.24217
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Academic Papers
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2c3d535eb5631451029a8e9649930fae218d854215bbfbf18bd529350cc5444e
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2026-01-01T00:00:00-05:00
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ARM: A Learnable, Plug-and-Play Module for CLIP-based Open-vocabulary Semantic Segmentation
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arXiv:2512.24224v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) is fundamentally hampered by the coarse, image-level representations of CLIP, which lack precise pixel-level details. Existing training-free methods attempt to resolve this by either importing priors from costly external foundation models (e.g., SAM, DINO) or by applying static, hand-crafted heuristics to CLIP's internal features. These approaches are either computationally expensive or sub-optimal. We propose the Attention Refinement Module (ARM), a lightweight, learnable module that effectively unlocks and refines CLIP's internal potential. Unlike static-fusion methods, ARM learns to adaptively fuse hierarchical features. It employs a semantically-guided cross-attention block, using robust deep features (K, V) to select and refine detail-rich shallow features (Q), followed by a self-attention block. The key innovation lies in a ``train once, use anywhere" paradigm. Trained once on a general-purpose dataset (e.g., COCO-Stuff), ARM acts as a universal plug-and-play post-processor for diverse training-free frameworks. Extensive experiments show that ARM consistently boosts baseline performance on multiple benchmarks with negligible inference overhead, establishing an efficient and effective paradigm for training-free OVSS.
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https://arxiv.org/abs/2512.24224
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Academic Papers
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2c1753e16bcf881ade319cec1788e116a93fa8fde709f21041c4abe1ce25d207
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2026-01-01T00:00:00-05:00
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Mirage: One-Step Video Diffusion for Photorealistic and Coherent Asset Editing in Driving Scenes
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arXiv:2512.24227v1 Announce Type: new Abstract: Vision-centric autonomous driving systems rely on diverse and scalable training data to achieve robust performance. While video object editing offers a promising path for data augmentation, existing methods often struggle to maintain both high visual fidelity and temporal coherence. In this work, we propose \textbf{Mirage}, a one-step video diffusion model for photorealistic and coherent asset editing in driving scenes. Mirage builds upon a text-to-video diffusion prior to ensure temporal consistency across frames. However, 3D causal variational autoencoders often suffer from degraded spatial fidelity due to compression, and directly passing 3D encoder features to decoder layers breaks temporal causality. To address this, we inject temporally agnostic latents from a pretrained 2D encoder into the 3D decoder to restore detail while preserving causal structures. Furthermore, because scene objects and inserted assets are optimized under different objectives, their Gaussians exhibit a distribution mismatch that leads to pose misalignment. To mitigate this, we introduce a two-stage data alignment strategy combining coarse 3D alignment and fine 2D refinement, thereby improving alignment and providing cleaner supervision. Extensive experiments demonstrate that Mirage achieves high realism and temporal consistency across diverse editing scenarios. Beyond asset editing, Mirage can also generalize to other video-to-video translation tasks, serving as a reliable baseline for future research. Our code is available at https://github.com/wm-research/mirage.
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https://arxiv.org/abs/2512.24227
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Academic Papers
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06fa5e84a873e8944beb6d540942285dc05bdb70179a311b6856c2a00f7b8bfe
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2026-01-01T00:00:00-05:00
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MotivNet: Evolving Meta-Sapiens into an Emotionally Intelligent Foundation Model
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arXiv:2512.24231v1 Announce Type: new Abstract: In this paper, we introduce MotivNet, a generalizable facial emotion recognition model for robust real-world application. Current state-of-the-art FER models tend to have weak generalization when tested on diverse data, leading to deteriorated performance in the real world and hindering FER as a research domain. Though researchers have proposed complex architectures to address this generalization issue, they require training cross-domain to obtain generalizable results, which is inherently contradictory for real-world application. Our model, MotivNet, achieves competitive performance across datasets without cross-domain training by using Meta-Sapiens as a backbone. Sapiens is a human vision foundational model with state-of-the-art generalization in the real world through large-scale pretraining of a Masked Autoencoder. We propose MotivNet as an additional downstream task for Sapiens and define three criteria to evaluate MotivNet's viability as a Sapiens task: benchmark performance, model similarity, and data similarity. Throughout this paper, we describe the components of MotivNet, our training approach, and our results showing MotivNet is generalizable across domains. We demonstrate that MotivNet can be benchmarked against existing SOTA models and meets the listed criteria, validating MotivNet as a Sapiens downstream task, and making FER more incentivizing for in-the-wild application. The code is available at https://github.com/OSUPCVLab/EmotionFromFaceImages.
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https://arxiv.org/abs/2512.24231
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Academic Papers
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e7371f4b3287d14138e9d64077c5e5526f3f6da717a4592d7be91a2faa009403
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2026-01-01T00:00:00-05:00
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SC-LDPC Codes Over $\mathbb{F}_q$: Minimum Distance, Decoding Analysis and Threshold Saturation
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arXiv:2512.24232v1 Announce Type: new Abstract: We investigate random spatially coupled low-density parity-check (SC-LDPC) code ensembles over finite fields. Under different variable-node edge-spreading rules, the random Tanner graphs of several coupled ensembles are defined by multiple independent, uniformly random monomial maps. The two main coupled ensembles considered are referred to as the standard coupled ensemble and the improved coupled ensemble. We prove that both coupled ensembles exhibit asymptotically good minimum distance and minimum stopping set size. Theoretical and numerical results show that the improved coupled ensemble can achieve better distance performance than the standard coupled ensemble. We introduce the essential preliminaries and analytical tools needed to analyze the iterative decoding threshold of coupled ensembles over any finite field. We consider a class of memoryless channels with special symmetry, termed q-ary input memoryless symmetric channels (QMSCs), and show that, for these channels, the distribution of channel messages (in form of probability vectors) likewise exhibits this symmetry. Consequently, we define symmetric probability measures and their reference measures on a finite-dimensional probability simplex, analyze their foundational properties and those of their linear functionals, endow their respective spaces with metric topologies, and conduct an in-depth study of their degradation theory. Based on our analytical framework, we establish a universal threshold saturation result for both of the coupled ensembles over a q-ary finite field on QMSCs. Specifically, as the coupling parameters increase, the belief-propagation threshold of a coupled system saturates to a well-defined threshold that depends only on the underlying ensemble and the channel family.
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https://arxiv.org/abs/2512.24232
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Academic Papers
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88e9b02c6ab517804ff3ef5534a7b7992a0ce361ccdd55e28d0aa91e166664a5
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2026-01-01T00:00:00-05:00
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LAILA: A Large Trait-Based Dataset for Arabic Automated Essay Scoring
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arXiv:2512.24235v1 Announce Type: new Abstract: Automated Essay Scoring (AES) has gained increasing attention in recent years, yet research on Arabic AES remains limited due to the lack of publicly available datasets. To address this, we introduce LAILA, the largest publicly available Arabic AES dataset to date, comprising 7,859 essays annotated with holistic and trait-specific scores on seven dimensions: relevance, organization, vocabulary, style, development, mechanics, and grammar. We detail the dataset design, collection, and annotations, and provide benchmark results using state-of-the-art Arabic and English models in prompt-specific and cross-prompt settings. LAILA fills a critical need in Arabic AES research, supporting the development of robust scoring systems.
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https://arxiv.org/abs/2512.24235
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Academic Papers
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1f3d27a799868926f70c22cc3d82286c3e00d2c51324bcff7d75b71472d41b1f
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2026-01-01T00:00:00-05:00
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A Framing and Analysis of Applicative Tangible Interfaces
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arXiv:2512.24237v1 Announce Type: new Abstract: The investigation of tangible user interfaces commenced approximately thirty years ago. Questions on its commercial potential become more pressing as the field becomes mature. To take the field one step further -- as the emergence of components contributed to the commercial development of graphical user interfaces -- this article suggests that applicative tangible user interfaces could also be split into components. These components are composed of the aggregation, combination, or coupling of physical items and fulfil four roles that are described through a new interaction model. This article successfully distributed among these four components' roles all of the 159 physical items from a representative collection of 35 applications. Further examination of these applicative tangible interfaces coincides with four research phases in the field and identifies three main paths for future research to fully realize the potential of tangible user interfaces.
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https://arxiv.org/abs/2512.24237
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Academic Papers
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94a2258ab389b4b3122bf8bbe6f165082487cd5ee9a71572c7fea3cce8ee0f02
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2026-01-01T00:00:00-05:00
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Spatial Discretization for Fine-Grain Zone Checks with STARKs
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arXiv:2512.24238v1 Announce Type: new Abstract: Many location-based services rely on a point-in-polygon test (PiP), checking whether a point or a trajectory lies inside a geographic zone. Since geometric operations are expensive in zero-knowledge proofs, privately performing the PiP test is challenging. In this paper, we answer the research questions of how different ways of encoding zones affect accuracy and proof cost by exploiting gridbased lookup tables under a fixed STARK execution model. Beyond a Boolean grid-based baseline that marks cells as in- or outside, we explore a distance-aware encoding approach that stores how far each cell is from a zone boundary and uses interpolation to reason within a cell. Our experiments on real-world data demonstrate that the proposed distance-aware approach achieves higher accuracy on coarse grids (max. 60%p accuracy gain) with only a moderate verification overhead (approximately 1.4x), making zone encoding the key lever for efficient zero-knowledge spatial checks.
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https://arxiv.org/abs/2512.24238
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Academic Papers
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a4dc178c0075b12d6dfc42a8abd624332e4fb4553040d859afcec4779c7245cd
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2026-01-01T00:00:00-05:00
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The Uncanny Valley in medical simulation-based training: a visual summary
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arXiv:2512.24240v1 Announce Type: new Abstract: The purpose of this review article is to provide a bibliographical as well as evidence-based visual guide regarding the effect of ``Uncanny Valley'' (UV) and how it profoundly influences medical virtual reality simulation-based training. The phenomenon, where increasingly realistic virtual humans elicit discomfort due to subtle imperfections, is crucial to understand and address in the context of medical training, where realism and immersion are key to effective learning. Our research team, consisting of experts in computer graphics, virtual reality, and medical education, brings a diverse and multidisciplinary perspective to this subject. Our collective experience spans developing advanced computer graphics systems, VR character simulation, and innovative educational technologies. We have collaborated across institutions and industries to push the boundaries of VR applications in medical training.
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https://arxiv.org/abs/2512.24240
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Academic Papers
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59c934c6987bc1a1bd8568e70d9171a88758281ab1ceb12cab1c2848c7feace9
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2026-01-01T00:00:00-05:00
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MambaSeg: Harnessing Mamba for Accurate and Efficient Image-Event Semantic Segmentation
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arXiv:2512.24243v1 Announce Type: new Abstract: Semantic segmentation is a fundamental task in computer vision with wide-ranging applications, including autonomous driving and robotics. While RGB-based methods have achieved strong performance with CNNs and Transformers, their effectiveness degrades under fast motion, low-light, or high dynamic range conditions due to limitations of frame cameras. Event cameras offer complementary advantages such as high temporal resolution and low latency, yet lack color and texture, making them insufficient on their own. To address this, recent research has explored multimodal fusion of RGB and event data; however, many existing approaches are computationally expensive and focus primarily on spatial fusion, neglecting the temporal dynamics inherent in event streams. In this work, we propose MambaSeg, a novel dual-branch semantic segmentation framework that employs parallel Mamba encoders to efficiently model RGB images and event streams. To reduce cross-modal ambiguity, we introduce the Dual-Dimensional Interaction Module (DDIM), comprising a Cross-Spatial Interaction Module (CSIM) and a Cross-Temporal Interaction Module (CTIM), which jointly perform fine-grained fusion along both spatial and temporal dimensions. This design improves cross-modal alignment, reduces ambiguity, and leverages the complementary properties of each modality. Extensive experiments on the DDD17 and DSEC datasets demonstrate that MambaSeg achieves state-of-the-art segmentation performance while significantly reducing computational cost, showcasing its promise for efficient, scalable, and robust multimodal perception.
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https://arxiv.org/abs/2512.24243
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Academic Papers
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25e6973d43e129298553182fe687eefb7c357f2300b166549c964cb02b98307e
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2026-01-01T00:00:00-05:00
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Time-Aware Adaptive Side Information Fusion for Sequential Recommendation
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arXiv:2512.24246v1 Announce Type: new Abstract: Incorporating item-side information, such as category and brand, into sequential recommendation is a well-established and effective approach for improving performance. However, despite significant advancements, current models are generally limited by three key challenges: they often overlook the fine-grained temporal dynamics inherent in timestamps, exhibit vulnerability to noise in user interaction sequences, and rely on computationally expensive fusion architectures. To systematically address these challenges, we propose the Time-Aware Adaptive Side Information Fusion (TASIF) framework. TASIF integrates three synergistic components: (1) a simple, plug-and-play time span partitioning mechanism to capture global temporal patterns; (2) an adaptive frequency filter that leverages a learnable gate to denoise feature sequences adaptively, thereby providing higher-quality inputs for subsequent fusion modules; and (3) an efficient adaptive side information fusion layer, this layer employs a "guide-not-mix" architecture, where attributes guide the attention mechanism without being mixed into the content-representing item embeddings, ensuring deep interaction while ensuring computational efficiency. Extensive experiments on four public datasets demonstrate that TASIF significantly outperforms state-of-the-art baselines while maintaining excellent efficiency in training. Our source code is available at https://github.com/jluo00/TASIF.
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https://arxiv.org/abs/2512.24246
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Academic Papers
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2eaec33a4c17f3919fab2d18be6237d2c419f6c8ccfb91adf91350327b10f7d4
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2026-01-01T00:00:00-05:00
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Heteroscedastic Bayesian Optimization-Based Dynamic PID Tuning for Accurate and Robust UAV Trajectory Tracking
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arXiv:2512.24249v1 Announce Type: new Abstract: Unmanned Aerial Vehicles (UAVs) play an important role in various applications, where precise trajectory tracking is crucial. However, conventional control algorithms for trajectory tracking often exhibit limited performance due to the underactuated, nonlinear, and highly coupled dynamics of quadrotor systems. To address these challenges, we propose HBO-PID, a novel control algorithm that integrates the Heteroscedastic Bayesian Optimization (HBO) framework with the classical PID controller to achieve accurate and robust trajectory tracking. By explicitly modeling input-dependent noise variance, the proposed method can better adapt to dynamic and complex environments, and therefore improve the accuracy and robustness of trajectory tracking. To accelerate the convergence of optimization, we adopt a two-stage optimization strategy that allow us to more efficiently find the optimal controller parameters. Through experiments in both simulation and real-world scenarios, we demonstrate that the proposed method significantly outperforms state-of-the-art (SOTA) methods. Compared to SOTA methods, it improves the position accuracy by 24.7% to 42.9%, and the angular accuracy by 40.9% to 78.4%.
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https://arxiv.org/abs/2512.24249
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Academic Papers
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a1728efd3eb8bc6877cffe0197926e7e6e1a98075f551cc952673b2ee81d3d9d
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2026-01-01T00:00:00-05:00
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Deep Reinforcement Learning for Solving the Fleet Size and Mix Vehicle Routing Problem
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arXiv:2512.24251v1 Announce Type: new Abstract: The Fleet Size and Mix Vehicle Routing Problem (FSMVRP) is a prominent variant of the Vehicle Routing Problem (VRP), extensively studied in operations research and computational science. FSMVRP requires simultaneous decisions on fleet composition and routing, making it highly applicable to real-world scenarios such as short-term vehicle rental and on-demand logistics. However, these requirements also increase the complexity of FSMVRP, posing significant challenges, particularly in large-scale and time-constrained environments. In this paper, we propose a deep reinforcement learning (DRL)-based approach for solving FSMVRP, capable of generating near-optimal solutions within a few seconds. Specifically, we formulate the problem as a Markov Decision Process (MDP) and develop a novel policy network, termed FRIPN, that seamlessly integrates fleet composition and routing decisions. Our method incorporates specialized input embeddings designed for distinctdecision objectives, including a remaining graph embedding to facilitate effective vehicle employment decisions. Comprehensive experiments are conducted on both randomly generated instances and benchmark datasets. The experimental results demonstrate that our method exhibits notable advantages in terms of computational efficiency and scalability, particularly in large-scale and time-constrained scenarios. These strengths highlight the potential of our approach for practical applications and provide valuable inspiration for extending DRL-based techniques to other variants of VRP.
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https://arxiv.org/abs/2512.24251
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Academic Papers
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0d0ada21ae6753c960da470fd5a4ee120ab9849f585fbb8243aceb46373c4210
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2026-01-01T00:00:00-05:00
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Early Prediction of Sepsis using Heart Rate Signals and Genetic Optimized LSTM Algorithm
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arXiv:2512.24253v1 Announce Type: new Abstract: Sepsis, characterized by a dysregulated immune response to infection, results in significant mortality, morbidity, and healthcare costs. The timely prediction of sepsis progression is crucial for reducing adverse outcomes through early intervention. Despite the development of numerous models for Intensive Care Unit (ICU) patients, there remains a notable gap in approaches for the early detection of sepsis in non-ward settings. This research introduces and evaluates four novel machine learning algorithms designed for predicting the onset of sepsis on wearable devices by analyzing heart rate data. The architecture of these models was refined through a genetic algorithm, optimizing for performance, computational complexity, and memory requirements. Performance metrics were subsequently extracted for each model to evaluate their feasibility for implementation on wearable devices capable of accurate heart rate monitoring. The models were initially tailored for a prediction window of one hour, later extended to four hours through transfer learning. The encouraging outcomes of this study suggest the potential for wearable technology to facilitate early sepsis detection outside ICU and ward environments.
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https://arxiv.org/abs/2512.24253
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Academic Papers
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bb822144bc6099fefc58304363dad0a6610e6aed7be7c80ee24f0909721bab8b
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2026-01-01T00:00:00-05:00
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How Would Oblivious Memory Boost Graph Analytics on Trusted Processors?
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arXiv:2512.24255v1 Announce Type: new Abstract: Trusted processors provide a way to perform joint computations while preserving data privacy. To overcome the performance degradation caused by data-oblivious algorithms to prevent information leakage, we explore the benefits of oblivious memory (OM) integrated in processors, to which the accesses are unobservable by adversaries. We focus on graph analytics, an important application vulnerable to access-pattern attacks. With a co-design between storage structure and algorithms, our prototype system is 100x faster than baselines given an OM sized around the per-core cache which can be implemented on existing processors with negligible overhead. This gives insights into equipping trusted processors with OM.
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https://arxiv.org/abs/2512.24255
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Academic Papers
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8d182980bb2b2a3b93e48912a1f63a747a308bd65cec4445b6bb2bec580e8182
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2026-01-01T00:00:00-05:00
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Tracing the Flow of Knowledge From Science to Technology Using Deep Learning
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arXiv:2512.24259v1 Announce Type: new Abstract: We develop a language similarity model suitable for working with patents and scientific publications at the same time. In a horse race-style evaluation, we subject eight language (similarity) models to predict credible Patent-Paper Citations. We find that our Pat-SPECTER model performs best, which is the SPECTER2 model fine-tuned on patents. In two real-world scenarios (separating patent-paper-pairs and predicting patent-paper-pairs) we demonstrate the capabilities of the Pat-SPECTER. We finally test the hypothesis that US patents cite papers that are semantically less similar than in other large jurisdictions, which we posit is because of the duty of candor. The model is open for the academic community and practitioners alike.
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https://arxiv.org/abs/2512.24259
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Academic Papers
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f06312594ab90587435308a00a025841b5ed3fb727aa167f5b0b407d81530389
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2026-01-01T00:00:00-05:00
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Physically-Grounded Manifold Projection with Foundation Priors for Metal Artifact Reduction in Dental CBCT
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arXiv:2512.24260v1 Announce Type: new Abstract: Metal artifacts in Dental CBCT severely obscure anatomical structures, hindering diagnosis. Current deep learning for Metal Artifact Reduction (MAR) faces limitations: supervised methods suffer from spectral blurring due to "regression-to-the-mean", while unsupervised ones risk structural hallucinations. Denoising Diffusion Models (DDPMs) offer realism but rely on slow, stochastic iterative sampling, unsuitable for clinical use. To resolve this, we propose the Physically-Grounded Manifold Projection (PGMP) framework. First, our Anatomically-Adaptive Physics Simulation (AAPS) pipeline synthesizes high-fidelity training pairs via Monte Carlo spectral modeling and patient-specific digital twins, bridging the synthetic-to-real gap. Second, our DMP-Former adapts the Direct x-Prediction paradigm, reformulating restoration as a deterministic manifold projection to recover clean anatomy in a single forward pass, eliminating stochastic sampling. Finally, a Semantic-Structural Alignment (SSA) module anchors the solution using priors from medical foundation models (MedDINOv3), ensuring clinical plausibility. Experiments on synthetic and multi-center clinical datasets show PGMP outperforms state-of-the-art methods on unseen anatomy, setting new benchmarks in efficiency and diagnostic reliability. Code and data: https://github.com/ricoleehduu/PGMP
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https://arxiv.org/abs/2512.24260
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Academic Papers
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e58a70f8da3d3dcb9867f22129af0ba882282f1cb27ea1c23c086e10ef673890
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2026-01-01T00:00:00-05:00
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Constrained Language Model Policy Optimization via Risk-aware Stepwise Alignment
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arXiv:2512.24263v1 Announce Type: new Abstract: When fine-tuning pre-trained Language Models (LMs) to exhibit desired behaviors, maintaining control over risk is critical for ensuring both safety and trustworthiness. Most existing safety alignment methods, such as Safe RLHF and SACPO, typically operate under a risk-neutral paradigm that is insufficient to address the risks arising from deviations from the reference policy and offers limited robustness against rare but potentially catastrophic harmful behaviors. To address this limitation, we propose Risk-aware Stepwise Alignment (RSA), a novel alignment method that explicitly incorporates risk awareness into the policy optimization process by leveraging a class of nested risk measures. Specifically, RSA formulates safety alignment as a token-level risk-aware constrained policy optimization problem and solves it through a stepwise alignment procedure that yields token-level policy updates derived from the nested risk measures. This design offers two key benefits: (1) it mitigates risks induced by excessive model shift away from a reference policy, and (2) it explicitly suppresses low-probability yet high-impact harmful behaviors. Moreover, we provide theoretical analysis on policy optimality under mild assumptions. Experimental results demonstrate that our method achieves high levels of helpfulness while ensuring strong safety and significantly suppresses tail risks, namely low-probability yet high-impact unsafe responses.
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https://arxiv.org/abs/2512.24263
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Academic Papers
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7c82dfb6df1ad1c782b83be83ff847cc8b81722e0d738e97e4b985ffc5f5e31c
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2026-01-01T00:00:00-05:00
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Joint Selection for Large-Scale Pre-Training Data via Policy Gradient-based Mask Learning
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arXiv:2512.24265v1 Announce Type: new Abstract: A fine-grained data recipe is crucial for pre-training large language models, as it can significantly enhance training efficiency and model performance. One important ingredient in the recipe is to select samples based on scores produced by defined rules, LLM judgment, or statistical information in embeddings, which can be roughly categorized into quality and diversity metrics. Due to the high computational cost when applied to trillion-scale token pre-training datasets such as FineWeb and DCLM, these two or more types of metrics are rarely considered jointly in a single selection process. However, in our empirical study, selecting samples based on quality metrics exhibit severe diminishing returns during long-term pre-training, while selecting on diversity metrics removes too many valuable high-quality samples, both of which limit pre-trained LLMs' capabilities. Therefore, we introduce DATAMASK, a novel and efficient joint learning framework designed for large-scale pre-training data selection that can simultaneously optimize multiple types of metrics in a unified process, with this study focusing specifically on quality and diversity metrics. DATAMASK approaches the selection process as a mask learning problem, involving iterative sampling of data masks, computation of policy gradients based on predefined objectives with sampled masks, and updating of mask sampling logits. Through policy gradient-based optimization and various acceleration enhancements, it significantly reduces selection time by 98.9% compared to greedy algorithm, enabling our study to explore joint learning within trillion-scale tokens. With DATAMASK, we select a subset of about 10% from the 15 trillion-token FineWeb dataset, termed FineWeb-Mask. Evaluated across 12 diverse tasks, we achieves significant improvements of 3.2% on a 1.5B dense model and 1.9% on a 7B MoE model.
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https://arxiv.org/abs/2512.24265
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Academic Papers
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dd66b59434bdeeeab4e9b996da5a6e037ebc9758d7853cf98044202e724a1d9a
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2026-01-01T00:00:00-05:00
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RAGPart & RAGMask: Retrieval-Stage Defenses Against Corpus Poisoning in Retrieval-Augmented Generation
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arXiv:2512.24268v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to enhance large language models (LLMs) with external knowledge, reducing hallucinations and compensating for outdated information. However, recent studies have exposed a critical vulnerability in RAG pipelines corpus poisoning where adversaries inject malicious documents into the retrieval corpus to manipulate model outputs. In this work, we propose two complementary retrieval-stage defenses: RAGPart and RAGMask. Our defenses operate directly on the retriever, making them computationally lightweight and requiring no modification to the generation model. RAGPart leverages the inherent training dynamics of dense retrievers, exploiting document partitioning to mitigate the effect of poisoned points. In contrast, RAGMask identifies suspicious tokens based on significant similarity shifts under targeted token masking. Across two benchmarks, four poisoning strategies, and four state-of-the-art retrievers, our defenses consistently reduce attack success rates while preserving utility under benign conditions. We further introduce an interpretable attack to stress-test our defenses. Our findings highlight the potential and limitations of retrieval-stage defenses, providing practical insights for robust RAG deployments.
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https://arxiv.org/abs/2512.24268
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Academic Papers
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581134e28e6c2ad0714fd6868ce297ae8ff315899da80e91612233027834268c
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2026-01-01T00:00:00-05:00
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Taming Hallucinations: Boosting MLLMs' Video Understanding via Counterfactual Video Generation
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arXiv:2512.24271v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have made remarkable progress in video understanding. However, they suffer from a critical vulnerability: an over-reliance on language priors, which can lead to visual ungrounded hallucinations, especially when processing counterfactual videos that defy common sense. This limitation, stemming from the intrinsic data imbalance between text and video, is challenging to address due to the substantial cost of collecting and annotating counterfactual data. To address this, we introduce DualityForge, a novel counterfactual data synthesis framework that employs controllable, diffusion-based video editing to transform real-world videos into counterfactual scenarios. By embedding structured contextual information into the video editing and QA generation processes, the framework automatically produces high-quality QA pairs together with original-edited video pairs for contrastive training. Based on this, we build DualityVidQA, a large-scale video dataset designed to reduce MLLM hallucinations. In addition, to fully exploit the contrastive nature of our paired data, we propose Duality-Normalized Advantage Training (DNA-Train), a two-stage SFT-RL training regime where the RL phase applies pair-wise $\ell_1$ advantage normalization, thereby enabling a more stable and efficient policy optimization. Experiments on DualityVidQA-Test demonstrate that our method substantially reduces model hallucinations on counterfactual videos, yielding a relative improvement of 24.0% over the Qwen2.5-VL-7B baseline. Moreover, our approach achieves significant gains across both hallucination and general-purpose benchmarks, indicating strong generalization capability. We will open-source our dataset and code.
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https://arxiv.org/abs/2512.24271
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Academic Papers
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fe0526ad12996da0949271c5b515d7d41efb11dd3363f79fcd22ce236a39f24f
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2026-01-01T00:00:00-05:00
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Local Path Optimization in The Latent Space Using Learned Distance Gradient
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arXiv:2512.24272v1 Announce Type: new Abstract: Constrained motion planning is a common but challenging problem in robotic manipulation. In recent years, data-driven constrained motion planning algorithms have shown impressive planning speed and success rate. Among them, the latent motion method based on manifold approximation is the most efficient planning algorithm. Due to errors in manifold approximation and the difficulty in accurately identifying collision conflicts within the latent space, time-consuming path validity checks and path replanning are required. In this paper, we propose a method that trains a neural network to predict the minimum distance between the robot and obstacles using latent vectors as inputs. The learned distance gradient is then used to calculate the direction of movement in the latent space to move the robot away from obstacles. Based on this, a local path optimization algorithm in the latent space is proposed, and it is integrated with the path validity checking process to reduce the time of replanning. The proposed method is compared with state-of-the-art algorithms in multiple planning scenarios, demonstrating the fastest planning speed
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https://arxiv.org/abs/2512.24272
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Academic Papers
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6b8c4cc982dab43f8a34769245d49f80a2b490accbd976acb4a9c4ad7d6d96c3
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2026-01-01T00:00:00-05:00
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LiftProj: Space Lifting and Projection-Based Panorama Stitching
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arXiv:2512.24276v1 Announce Type: new Abstract: Traditional image stitching techniques have predominantly utilized two-dimensional homography transformations and mesh warping to achieve alignment on a planar surface. While effective for scenes that are approximately coplanar or exhibit minimal parallax, these approaches often result in ghosting, structural bending, and stretching distortions in non-overlapping regions when applied to real three-dimensional scenes characterized by multiple depth layers and occlusions. Such challenges are exacerbated in multi-view accumulations and 360{\deg} closed-loop stitching scenarios. In response, this study introduces a spatially lifted panoramic stitching framework that initially elevates each input image into a dense three-dimensional point representation within a unified coordinate system, facilitating global cross-view fusion augmented by confidence metrics. Subsequently, a unified projection center is established in three-dimensional space, and an equidistant cylindrical projection is employed to map the fused data onto a single panoramic manifold, thereby producing a geometrically consistent 360{\deg} panoramic layout. Finally, hole filling is conducted within the canvas domain to address unknown regions revealed by viewpoint transitions, restoring continuous texture and semantic coherence. This framework reconceptualizes stitching from a two-dimensional warping paradigm to a three-dimensional consistency paradigm and is designed to flexibly incorporate various three-dimensional lifting and completion modules. Experimental evaluations demonstrate that the proposed method substantially mitigates geometric distortions and ghosting artifacts in scenarios involving significant parallax and complex occlusions, yielding panoramic results that are more natural and consistent.
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https://arxiv.org/abs/2512.24276
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Academic Papers
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561fd09033ee23a5264899b98e44047d7f1682f5b0ad323d5345092c0e82d6bd
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2026-01-01T00:00:00-05:00
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One-shot synthesis of rare gastrointestinal lesions improves diagnostic accuracy and clinical training
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arXiv:2512.24278v1 Announce Type: new Abstract: Rare gastrointestinal lesions are infrequently encountered in routine endoscopy, restricting the data available for developing reliable artificial intelligence (AI) models and training novice clinicians. Here we present EndoRare, a one-shot, retraining-free generative framework that synthesizes diverse, high-fidelity lesion exemplars from a single reference image. By leveraging language-guided concept disentanglement, EndoRare separates pathognomonic lesion features from non-diagnostic attributes, encoding the former into a learnable prototype embedding while varying the latter to ensure diversity. We validated the framework across four rare pathologies (calcifying fibrous tumor, juvenile polyposis syndrome, familial adenomatous polyposis, and Peutz-Jeghers syndrome). Synthetic images were judged clinically plausible by experts and, when used for data augmentation, significantly enhanced downstream AI classifiers, improving the true positive rate at low false-positive rates. Crucially, a blinded reader study demonstrated that novice endoscopists exposed to EndoRare-generated cases achieved a 0.400 increase in recall and a 0.267 increase in precision. These results establish a practical, data-efficient pathway to bridge the rare-disease gap in both computer-aided diagnostics and clinical education.
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https://arxiv.org/abs/2512.24278
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Academic Papers
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59909ce23ca96e63d77123eab3c80614a3ba136d35e6a7e29a5081516dc495cd
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2026-01-01T00:00:00-05:00
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Safe Sliding Mode Control for Marine Vessels Using High-Order Control Barrier Functions and Fast Projection
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arXiv:2512.24281v1 Announce Type: new Abstract: This paper presents a novel safe control framework that integrates Sliding Mode Control (SMC), High-Order Control Barrier Functions (HOCBFs) with state-dependent adaptiveness and a lightweight projection for collision-free navigation of an over-actuated 3-DOF marine surface vessel subjected to strong environmental disturbances (wind, waves, and current). SMC provides robustness to matched disturbances common in marine operations, while HOCBFs enforce forward invariance of obstacle-avoidance constraints. A fast half-space projection method adjusts the SMC control only when needed, preserving robustness and minimizing chattering. The approach is evaluated on a nonlinear marine platform model that includes added mass, hydrodynamic damping, and full thruster allocation. Simulation results show robust navigation, guaranteed obstacle avoidance, and computational efficiency suitable for real-time embedded use. For small marine robots and surface vessels with limited onboard computational resources-where execution speed and computational efficiency are critical-the SMC-HOCBF framework constitutes a strong candidate for safety-critical control.
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https://arxiv.org/abs/2512.24281
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Academic Papers
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c5a5f81bbe128e9c150835e5d9aeab06725c7c95fc54643d275e6a7bc7d1d73f
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2026-01-01T00:00:00-05:00
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DRL-TH: Jointly Utilizing Temporal Graph Attention and Hierarchical Fusion for UGV Navigation in Crowded Environments
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arXiv:2512.24284v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) methods have demonstrated potential for autonomous navigation and obstacle avoidance of unmanned ground vehicles (UGVs) in crowded environments. Most existing approaches rely on single-frame observation and employ simple concatenation for multi-modal fusion, which limits their ability to capture temporal context and hinders dynamic adaptability. To address these challenges, we propose a DRL-based navigation framework, DRL-TH, which leverages temporal graph attention and hierarchical graph pooling to integrate historical observations and adaptively fuse multi-modal information. Specifically, we introduce a temporal-guided graph attention network (TG-GAT) that incorporates temporal weights into attention scores to capture correlations between consecutive frames, thereby enabling the implicit estimation of scene evolution. In addition, we design a graph hierarchical abstraction module (GHAM) that applies hierarchical pooling and learnable weighted fusion to dynamically integrate RGB and LiDAR features, achieving balanced representation across multiple scales. Extensive experiments demonstrate that our DRL-TH outperforms existing methods in various crowded environments. We also implemented DRL-TH control policy on a real UGV and showed that it performed well in real world scenarios.
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https://arxiv.org/abs/2512.24284
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Academic Papers
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367eea0de7c1f56951f8ec521477962582b9386d39447cfdfa3d2744bfd62c4c
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2026-01-01T00:00:00-05:00
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Data Heterogeneity-Aware Client Selection for Federated Learning in Wireless Networks
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arXiv:2512.24286v1 Announce Type: new Abstract: Federated Learning (FL) enables mobile edge devices, functioning as clients, to collaboratively train a decentralized model while ensuring local data privacy. However, the efficiency of FL in wireless networks is limited not only by constraints on communication and computational resources but also by significant data heterogeneity among clients, particularly in large-scale networks. This paper first presents a theoretical analysis of the impact of client data heterogeneity on global model generalization error, which can result in repeated training cycles, increased energy consumption, and prolonged latency. Based on the theoretical insights, an optimization problem is formulated to jointly minimize learning latency and energy consumption while constraining generalization error. A joint client selection and resource allocation (CSRA) approach is then proposed, employing a series of convex optimization and relaxation techniques. Extensive simulation results demonstrate that the proposed CSRA scheme yields higher test accuracy, reduced learning latency, and lower energy consumption compared to baseline methods that do not account for data heterogeneity.
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https://arxiv.org/abs/2512.24286
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Academic Papers
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079ccfbbf2b7dd817b49f297f18fcfa970548cd5017c992642d2472c910466bd
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2026-01-01T00:00:00-05:00
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Real-world Reinforcement Learning from Suboptimal Interventions
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arXiv:2512.24288v1 Announce Type: new Abstract: Real-world reinforcement learning (RL) offers a promising approach to training precise and dexterous robotic manipulation policies in an online manner, enabling robots to learn from their own experience while gradually reducing human labor. However, prior real-world RL methods often assume that human interventions are optimal across the entire state space, overlooking the fact that even expert operators cannot consistently provide optimal actions in all states or completely avoid mistakes. Indiscriminately mixing intervention data with robot-collected data inherits the sample inefficiency of RL, while purely imitating intervention data can ultimately degrade the final performance achievable by RL. The question of how to leverage potentially suboptimal and noisy human interventions to accelerate learning without being constrained by them thus remains open. To address this challenge, we propose SiLRI, a state-wise Lagrangian reinforcement learning algorithm for real-world robot manipulation tasks. Specifically, we formulate the online manipulation problem as a constrained RL optimization, where the constraint bound at each state is determined by the uncertainty of human interventions. We then introduce a state-wise Lagrange multiplier and solve the problem via a min-max optimization, jointly optimizing the policy and the Lagrange multiplier to reach a saddle point. Built upon a human-as-copilot teleoperation system, our algorithm is evaluated through real-world experiments on diverse manipulation tasks. Experimental results show that SiLRI effectively exploits human suboptimal interventions, reducing the time required to reach a 90% success rate by at least 50% compared with the state-of-the-art RL method HIL-SERL, and achieving a 100% success rate on long-horizon manipulation tasks where other RL methods struggle to succeed. Project website: https://silri-rl.github.io/.
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https://arxiv.org/abs/2512.24288
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Academic Papers
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921211dfbd827f339502108afe2b34b0e115d67355ac361f9a1c75667fad58d8
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2026-01-01T00:00:00-05:00
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Automated Analysis of Sustainability Reports: Using Large Language Models for the Extraction and Prediction of EU Taxonomy-Compliant KPIs
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arXiv:2512.24289v1 Announce Type: new Abstract: The manual, resource-intensive process of complying with the EU Taxonomy presents a significant challenge for companies. While Large Language Models (LLMs) offer a path to automation, research is hindered by a lack of public benchmark datasets. To address this gap, we introduce a novel, structured dataset from 190 corporate reports, containing ground-truth economic activities and quantitative Key Performance Indicators (KPIs). We use this dataset to conduct the first systematic evaluation of LLMs on the core compliance workflow. Our results reveal a clear performance gap between qualitative and quantitative tasks. LLMs show moderate success in the qualitative task of identifying economic activities, with a multi-step agentic framework modestly enhancing precision. Conversely, the models comprehensively fail at the quantitative task of predicting financial KPIs in a zero-shot setting. We also discover a paradox, where concise metadata often yields superior performance to full, unstructured reports, and find that model confidence scores are poorly calibrated. We conclude that while LLMs are not ready for full automation, they can serve as powerful assistive tools for human experts. Our dataset provides a public benchmark for future research.
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https://arxiv.org/abs/2512.24289
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Academic Papers
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2c01dfe9590aed11d7524d43b1ca851deacc425e0fc00edbaad63443361b71f0
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2026-01-01T00:00:00-05:00
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Virtual-Eyes: Quantitative Validation of a Lung CT Quality-Control Pipeline for Foundation-Model Cancer Risk Prediction
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arXiv:2512.24294v1 Announce Type: new Abstract: Robust preprocessing is rarely quantified in deep-learning pipelines for low-dose CT (LDCT) lung cancer screening. We develop and validate Virtual-Eyes, a clinically motivated 16-bit CT quality-control pipeline, and measure its differential impact on generalist foundation models versus specialist models. Virtual-Eyes enforces strict 512x512 in-plane resolution, rejects short or non-diagnostic series, and extracts a contiguous lung block using Hounsfield-unit filtering and bilateral lung-coverage scoring while preserving the native 16-bit grid. Using 765 NLST patients (182 cancer, 583 non-cancer), we compute slice-level embeddings from RAD-DINO and Merlin with frozen encoders and train leakage-free patient-level MLP heads; we also evaluate Sybil and a 2D ResNet-18 baseline under Raw versus Virtual-Eyes inputs without backbone retraining. Virtual-Eyes improves RAD-DINO slice-level AUC from 0.576 to 0.610 and patient-level AUC from 0.646 to 0.683 (mean pooling) and from 0.619 to 0.735 (max pooling), with improved calibration (Brier score 0.188 to 0.112). In contrast, Sybil and ResNet-18 degrade under Virtual-Eyes (Sybil AUC 0.886 to 0.837; ResNet-18 AUC 0.571 to 0.596) with evidence of context dependence and shortcut learning, and Merlin shows limited transferability (AUC approximately 0.507 to 0.567) regardless of preprocessing. These results demonstrate that anatomically targeted QC can stabilize and improve generalist foundation-model workflows but may disrupt specialist models adapted to raw clinical context.
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https://arxiv.org/abs/2512.24294
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Academic Papers
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410ab1214269c891f95045d504e7d7c9b9b9b2e15309f9352e1e7b51962fb86b
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2026-01-01T00:00:00-05:00
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Figure It Out: Improving the Frontier of Reasoning with Active Visual Thinking
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arXiv:2512.24297v1 Announce Type: new Abstract: Complex reasoning problems often involve implicit spatial, geometric, and structural relationships that are not explicitly encoded in text. While recent reasoning models have achieved strong performance across many domains, purely text-based reasoning struggles to represent global structural constraints in complex settings. In this paper, we introduce FIGR, which integrates active visual thinking into multi-turn reasoning via end-to-end reinforcement learning. FIGR externalizes intermediate structural hypotheses by constructing visual representations during problem solving. By adaptively regulating when and how visual reasoning should be invoked, FIGR enables more stable and coherent reasoning over global structural properties that are difficult to capture from text alone. Experiments on challenging mathematical reasoning benchmarks demonstrate that FIGR outperforms strong text-only chain-of-thought baselines. In particular, FIGR improves the base model by 13.12% on AIME 2025 and 11.00% on BeyondAIME, highlighting the effectiveness of figure-guided multimodal reasoning in enhancing the stability and reliability of complex reasoning.
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https://arxiv.org/abs/2512.24297
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Academic Papers
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9a84bf003cbdc320f94aa26f92fd4af937f1276f356e6c6ee329f16e8db51ae9
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2026-01-01T00:00:00-05:00
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World In Your Hands: A Large-Scale and Open-source Ecosystem for Learning Human-centric Manipulation in the Wild
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arXiv:2512.24310v1 Announce Type: new Abstract: Large-scale pre-training is fundamental for generalization in language and vision models, but data for dexterous hand manipulation remains limited in scale and diversity, hindering policy generalization. Limited scenario diversity, misaligned modalities, and insufficient benchmarking constrain current human manipulation datasets. To address these gaps, we introduce World In Your Hands (WiYH), a large-scale open-source ecosystem for human-centric manipulation learning. WiYH includes (1) the Oracle Suite, a wearable data collection kit with an auto-labeling pipeline for accurate motion capture; (2) the WiYH Dataset, featuring over 1,000 hours of multi-modal manipulation data across hundreds of skills in diverse real-world scenarios; and (3) extensive annotations and benchmarks supporting tasks from perception to action. Furthermore, experiments based on the WiYH ecosystem show that integrating WiYH's human-centric data significantly enhances the generalization and robustness of dexterous hand policies in tabletop manipulation tasks. We believe that World In Your Hands will bring new insights into human-centric data collection and policy learning to the community.
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https://arxiv.org/abs/2512.24310
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Academic Papers
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c9fd525a005467bd3b1a2a5cf121feea8d88ea19c045a68876533d7e23c06ca2
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2026-01-01T00:00:00-05:00
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QianfanHuijin Technical Report: A Novel Multi-Stage Training Paradigm for Finance Industrial LLMs
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arXiv:2512.24314v1 Announce Type: new Abstract: Domain-specific enhancement of Large Language Models (LLMs) within the financial context has long been a focal point of industrial application. While previous models such as BloombergGPT and Baichuan-Finance primarily focused on knowledge enhancement, the deepening complexity of financial services has driven a growing demand for models that possess not only domain knowledge but also robust financial reasoning and agentic capabilities. In this paper, we present QianfanHuijin, a financial domain LLM, and propose a generalizable multi-stage training paradigm for industrial model enhancement. Our approach begins with Continual Pre-training (CPT) on financial corpora to consolidate the knowledge base. This is followed by a fine-grained Post-training pipeline designed with increasing specificity: starting with Financial SFT, progressing to Finance Reasoning RL and Finance Agentic RL, and culminating in General RL aligned with real-world business scenarios. Empirical results demonstrate that QianfanHuijin achieves superior performance across various authoritative financial benchmarks. Furthermore, ablation studies confirm that the targeted Reasoning RL and Agentic RL stages yield significant gains in their respective capabilities. These findings validate our motivation and suggest that this fine-grained, progressive post-training methodology is poised to become a mainstream paradigm for various industrial-enhanced LLMs.
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https://arxiv.org/abs/2512.24314
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Academic Papers
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1f4a1755518d8f103a76a0dead249c5d6a84c4f9813a64b792b4ff676c9ba863
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2026-01-01T00:00:00-05:00
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UniAct: Unified Motion Generation and Action Streaming for Humanoid Robots
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arXiv:2512.24321v1 Announce Type: new Abstract: A long-standing objective in humanoid robotics is the realization of versatile agents capable of following diverse multimodal instructions with human-level flexibility. Despite advances in humanoid control, bridging high-level multimodal perception with whole-body execution remains a significant bottleneck. Existing methods often struggle to translate heterogeneous instructions -- such as language, music, and trajectories -- into stable, real-time actions. Here we show that UniAct, a two-stage framework integrating a fine-tuned MLLM with a causal streaming pipeline, enables humanoid robots to execute multimodal instructions with sub-500 ms latency. By unifying inputs through a shared discrete codebook via FSQ, UniAct ensures cross-modal alignment while constraining motions to a physically grounded manifold. This approach yields a 19% improvement in the success rate of zero-shot tracking of imperfect reference motions. We validate UniAct on UniMoCap, our 20-hour humanoid motion benchmark, demonstrating robust generalization across diverse real-world scenarios. Our results mark a critical step toward responsive, general-purpose humanoid assistants capable of seamless interaction through unified perception and control.
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https://arxiv.org/abs/2512.24321
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Academic Papers
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8e8e0ba6792e51dbed16839febdbc136be6d4147fabb23b933b1fb227c4e6a5a
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2026-01-01T00:00:00-05:00
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Robust Egocentric Referring Video Object Segmentation via Dual-Modal Causal Intervention
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arXiv:2512.24323v1 Announce Type: new Abstract: Egocentric Referring Video Object Segmentation (Ego-RVOS) aims to segment the specific object actively involved in a human action, as described by a language query, within first-person videos. This task is critical for understanding egocentric human behavior. However, achieving such segmentation robustly is challenging due to ambiguities inherent in egocentric videos and biases present in training data. Consequently, existing methods often struggle, learning spurious correlations from skewed object-action pairings in datasets and fundamental visual confounding factors of the egocentric perspective, such as rapid motion and frequent occlusions. To address these limitations, we introduce Causal Ego-REferring Segmentation (CERES), a plug-in causal framework that adapts strong, pre-trained RVOS backbones to the egocentric domain. CERES implements dual-modal causal intervention: applying backdoor adjustment principles to counteract language representation biases learned from dataset statistics, and leveraging front-door adjustment concepts to address visual confounding by intelligently integrating semantic visual features with geometric depth information guided by causal principles, creating representations more robust to egocentric distortions. Extensive experiments demonstrate that CERES achieves state-of-the-art performance on Ego-RVOS benchmarks, highlighting the potential of applying causal reasoning to build more reliable models for broader egocentric video understanding.
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https://arxiv.org/abs/2512.24323
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Academic Papers
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1e2614154d04040f26cc009b5ee169b1a9f6748a1f07d4e7a4352cf4eb782b06
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2026-01-01T00:00:00-05:00
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Empower Low-Altitude Economy: A Reliability-Aware Dynamic Weighting Allocation for Multi-modal UAV Beam Prediction
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arXiv:2512.24324v1 Announce Type: new Abstract: The low-altitude economy (LAE) is rapidly expanding driven by urban air mobility, logistics drones, and aerial sensing, while fast and accurate beam prediction in uncrewed aerial vehicles (UAVs) communications is crucial for achieving reliable connectivity. Current research is shifting from single-signal to multi-modal collaborative approaches. However, existing multi-modal methods mostly employ fixed or empirical weights, assuming equal reliability across modalities at any given moment. Indeed, the importance of different modalities fluctuates dramatically with UAV motion scenarios, and static weighting amplifies the negative impact of degraded modalities. Furthermore, modal mismatch and weak alignment further undermine cross-scenario generalization. To this end, we propose a reliability-aware dynamic weighting scheme applied to a semantic-aware multi-modal beam prediction framework, named SaM2B. Specifically, SaM2B leverages lightweight cues such as environmental visual, flight posture, and geospatial data to adaptively allocate contributions across modalities at different time points through reliability-aware dynamic weight updates. Moreover, by utilizing cross-modal contrastive learning, we align the "multi-source representation beam semantics" associated with specific beam information to a shared semantic space, thereby enhancing discriminative power and robustness under modal noise and distribution shifts. Experiments on real-world low-altitude UAV datasets show that SaM2B achieves more satisfactory results than baseline methods.
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https://arxiv.org/abs/2512.24324
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Academic Papers
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b9b9e2f6aa769f9d2ff21ba7dec176f236b6cccd2035c39134033488e8d76c0f
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2026-01-01T00:00:00-05:00
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MaRCA: Multi-Agent Reinforcement Learning for Dynamic Computation Allocation in Large-Scale Recommender Systems
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arXiv:2512.24325v1 Announce Type: new Abstract: Modern recommender systems face significant computational challenges due to growing model complexity and traffic scale, making efficient computation allocation critical for maximizing business revenue. Existing approaches typically simplify multi-stage computation resource allocation, neglecting inter-stage dependencies, thus limiting global optimality. In this paper, we propose MaRCA, a multi-agent reinforcement learning framework for end-to-end computation resource allocation in large-scale recommender systems. MaRCA models the stages of a recommender system as cooperative agents, using Centralized Training with Decentralized Execution (CTDE) to optimize revenue under computation resource constraints. We introduce an AutoBucket TestBench for accurate computation cost estimation, and a Model Predictive Control (MPC)-based Revenue-Cost Balancer to proactively forecast traffic loads and adjust the revenue-cost trade-off accordingly. Since its end-to-end deployment in the advertising pipeline of a leading global e-commerce platform in November 2024, MaRCA has consistently handled hundreds of billions of ad requests per day and has delivered a 16.67% revenue uplift using existing computation resources.
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https://arxiv.org/abs/2512.24325
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Academic Papers
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2c72e461df2074b2ba5efa339de15b3043f70e4431b076628c9ed49d5a92bc45
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2026-01-01T00:00:00-05:00
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3D Path-Following Guidance via Nonlinear Model Predictive Control for Fixed-Wing Small UAS
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arXiv:2512.24326v1 Announce Type: new Abstract: This paper presents the design, implementation, and flight test results of two novel 3D path-following guidance algorithms based on nonlinear model predictive control (MPC), with specific application to fixed-wing small uncrewed aircraft systems. To enable MPC, control-augmented modelling and system identification of the RAAVEN small uncrewed aircraft is presented. Two formulations of MPC are then showcased. The first schedules a static reference path rate over the MPC horizon, incentivizing a constant inertial speed. The second, with inspiration from model predictive contouring control, dynamically optimizes for the reference path rate over the controller horizon as the system operates. This allows for a weighted tradeoff between path progression and distance from path, two competing objectives in path-following guidance. Both controllers are formulated to operate over general smooth 3D arc-length parameterized curves. The MPC guidance algorithms are flown over several high-curvature test paths, with comparison to a baseline lookahead guidance law. The results showcase the real-world feasibility and superior performance of nonlinear MPC for 3D path-following guidance at ground speeds up to 36 meters per second.
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https://arxiv.org/abs/2512.24326
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Academic Papers
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d6af014364e50e3fe0d40b08337a3ff4fc2acb6ed3198cef0e3adad88836e6da
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2026-01-01T00:00:00-05:00
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World model inspired sarcasm reasoning with large language model agents
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arXiv:2512.24329v1 Announce Type: new Abstract: Sarcasm understanding is a challenging problem in natural language processing, as it requires capturing the discrepancy between the surface meaning of an utterance and the speaker's intentions as well as the surrounding social context. Although recent advances in deep learning and Large Language Models (LLMs) have substantially improved performance, most existing approaches still rely on black-box predictions of a single model, making it difficult to structurally explain the cognitive factors underlying sarcasm. Moreover, while sarcasm often emerges as a mismatch between semantic evaluation and normative expectations or intentions, frameworks that explicitly decompose and model these components remain limited. In this work, we reformulate sarcasm understanding as a world model inspired reasoning process and propose World Model inspired SArcasm Reasoning (WM-SAR), which decomposes literal meaning, context, normative expectation, and intention into specialized LLM-based agents. The discrepancy between literal evaluation and normative expectation is explicitly quantified as a deterministic inconsistency score, and together with an intention score, these signals are integrated by a lightweight Logistic Regression model to infer the final sarcasm probability. This design leverages the reasoning capability of LLMs while maintaining an interpretable numerical decision structure. Experiments on representative sarcasm detection benchmarks show that WM-SAR consistently outperforms existing deep learning and LLM-based methods. Ablation studies and case analyses further demonstrate that integrating semantic inconsistency and intention reasoning is essential for effective sarcasm detection, achieving both strong performance and high interpretability.
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https://arxiv.org/abs/2512.24329
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Academic Papers
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c5b7e2de439e86f71b7fc0403bee1e81792bce372fcb08dce2683d579a400670
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2026-01-01T00:00:00-05:00
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SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning
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arXiv:2512.24330v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) can solve complex tasks through agentic reasoning, their capabilities remain largely constrained to text-oriented chain-of-thought or isolated tool invocation. They fail to exhibit the human-like proficiency required to seamlessly interleave dynamic tool manipulation with continuous reasoning, particularly in knowledge-intensive and visually complex scenarios that demand coordinated external tools such as search and image cropping. In this work, we introduce SenseNova-MARS, a novel Multimodal Agentic Reasoning and Search framework that empowers VLMs with interleaved visual reasoning and tool-use capabilities via reinforcement learning (RL). Specifically, SenseNova-MARS dynamically integrates the image search, text search, and image crop tools to tackle fine-grained and knowledge-intensive visual understanding challenges. In the RL stage, we propose the Batch-Normalized Group Sequence Policy Optimization (BN-GSPO) algorithm to improve the training stability and advance the model's ability to invoke tools and reason effectively. To comprehensively evaluate the agentic VLMs on complex visual tasks, we introduce the HR-MMSearch benchmark, the first search-oriented benchmark composed of high-resolution images with knowledge-intensive and search-driven questions. Experiments demonstrate that SenseNova-MARS achieves state-of-the-art performance on open-source search and fine-grained image understanding benchmarks. Specifically, on search-oriented benchmarks, SenseNova-MARS-8B scores 67.84 on MMSearch and 41.64 on HR-MMSearch, surpassing proprietary models such as Gemini-3-Flash and GPT-5. SenseNova-MARS represents a promising step toward agentic VLMs by providing effective and robust tool-use capabilities. To facilitate further research in this field, we will release all code, models, and datasets.
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https://arxiv.org/abs/2512.24330
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Academic Papers
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d9f6e0083cdff560e2992891cfc498f8c1e4db84c4f55a6cd9610563e3858942
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2026-01-01T00:00:00-05:00
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Spatial-aware Vision Language Model for Autonomous Driving
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arXiv:2512.24331v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) show significant promise for end-to-end autonomous driving by leveraging the common sense embedded in language models, their reliance on 2D image cues for complex scene understanding and decision-making presents a critical bottleneck for safety and reliability. Current image-based methods struggle with accurate metric spatial reasoning and geometric inference, leading to unreliable driving policies. To bridge this gap, we propose LVLDrive (LiDAR-Vision-Language), a novel framework specifically designed to upgrade existing VLMs with robust 3D metric spatial understanding for autonomous driving by incoperating LiDAR point cloud as an extra input modality. A key challenge lies in mitigating the catastrophic disturbance introduced by disparate 3D data to the pre-trained VLMs. To this end, we introduce a Gradual Fusion Q-Former that incrementally injects LiDAR features, ensuring the stability and preservation of the VLM's existing knowledge base. Furthermore, we develop a spatial-aware question-answering (SA-QA) dataset to explicitly teach the model advanced 3D perception and reasoning capabilities. Extensive experiments on driving benchmarks demonstrate that LVLDrive achieves superior performance compared to vision-only counterparts across scene understanding, metric spatial perception, and reliable driving decision-making. Our work highlights the necessity of explicit 3D metric data for building trustworthy VLM-based autonomous systems.
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https://arxiv.org/abs/2512.24331
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Academic Papers
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b4f1bde2851c52caa0fb4659e1b63a19e0ff7c02577c3e7eab7f95ca552e10b4
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2026-01-01T00:00:00-05:00
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A density-based framework for community detection in attributed networks
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arXiv:2512.24336v1 Announce Type: new Abstract: Community structure in social and collaborative networks often emerges from a complex interplay between structural mechanisms, such as degree heterogeneity and leader-driven attraction, and homophily on node attributes. Existing community detection methods typically focus on these dimensions in isolation, limiting their ability to recover interpretable communities in presence of such mechanisms. In this paper, we propose AttDeCoDe, an attribute-driven extension of a density-based community detection framework, developed to analyse networks where node characteristics play a central role in group formation. Instead of defining density purely from network topology, AttDeCoDe estimates node-wise density in the attribute space, allowing communities to form around attribute-based community representatives while preserving structural connectivity constraints. This approach naturally captures homophily-driven aggregation while remaining sensitive to leader influence. We evaluate the proposed method through a simulation study based on a novel generative model that extends the degree-corrected stochastic block model by incorporating attribute-driven leader attraction, reflecting key features of collaborative research networks. We perform an empirical application to research collaboration data from the Horizon programmes, where organisations are characterised by project-level thematic descriptors. Both results show that AttDeCoDe offers a flexible and interpretable framework for community detection in attributed networks achieving competitive performance relative to topology-based and attribute-assisted benchmarks.
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https://arxiv.org/abs/2512.24336
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Academic Papers
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546c1af2cb82de5bb18bbeec2db601d8acfc7b2a1ac2fb89fffaebdeaa66356a
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2026-01-01T00:00:00-05:00
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The Mechanics of CNN Filtering with Rectification
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arXiv:2512.24338v1 Announce Type: new Abstract: This paper proposes elementary information mechanics as a new model for understanding the mechanical properties of convolutional filtering with rectification, inspired by physical theories of special relativity and quantum mechanics. We consider kernels decomposed into orthogonal even and odd components. Even components cause image content to diffuse isotropically while preserving the center of mass, analogously to rest or potential energy with zero net momentum. Odd kernels cause directional displacement of the center of mass, analogously to kinetic energy with non-zero momentum. The speed of information displacement is linearly related to the ratio of odd vs total kernel energy. Even-Odd properties are analyzed in the spectral domain via the discrete cosine transform (DCT), where the structure of small convolutional filters (e.g. $3 \times 3$ pixels) is dominated by low-frequency bases, specifically the DC $\Sigma$ and gradient components $\nabla$, which define the fundamental modes of information propagation. To our knowledge, this is the first work demonstrating the link between information processing in generic CNNs and the energy-momentum relation, a cornerstone of modern relativistic physics.
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https://arxiv.org/abs/2512.24338
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Academic Papers
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06fa7485e50a9ed0a1c1fe16ae9b71114649f0a3f036e42a2610cfc9a722b14a
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2026-01-01T00:00:00-05:00
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Proof-Carrying PWL Verification for ReLU Networks: Convex-Hull Semantics, Exact \SMT/\MILP Encodings, and Symbolic Certificate Checking
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arXiv:2512.24339v1 Announce Type: new Abstract: ReLU networks are piecewise-linear (PWL), enabling exact symbolic verification via \SMT(\LRA) or \MILP. However, safety claims in certification pipelines require not only correctness but also \emph{checkable evidence}. We develop a proof-carrying verification core for PWL neural constraints: (i) we formalize ReLU networks as unions of polyhedra indexed by activation patterns; (ii) we present exact \SMT/\MILP encodings and the canonical convex-hull relaxation for each bounded ReLU; and (iii) we introduce a certificate calculus in which bound tightening, stabilization, strengthening, and pruning steps emit explicit algebraic witnesses (LP dual multipliers and Farkas infeasibility certificates). Crucially, these witnesses are \emph{symbolic objects} that admit independent verification in exact arithmetic over $\Q$. We provide a symbolic certificate checker, normalization rules that preserve validity, and a compositional view of region-wise certificates as a global proof artifact for universal safety.
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https://arxiv.org/abs/2512.24339
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Academic Papers
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b6ba9962e0d36314c2811dd82d9c7afb396ae92bbe38c07ed9bb55e4502bf55e
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2026-01-01T00:00:00-05:00
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DermaVQA-DAS: Dermatology Assessment Schema (DAS) & Datasets for Closed-Ended Question Answering & Segmentation in Patient-Generated Dermatology Images
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arXiv:2512.24340v1 Announce Type: new Abstract: Recent advances in dermatological image analysis have been driven by large-scale annotated datasets; however, most existing benchmarks focus on dermatoscopic images and lack patient-authored queries and clinical context, limiting their applicability to patient-centered care. To address this gap, we introduce DermaVQA-DAS, an extension of the DermaVQA dataset that supports two complementary tasks: closed-ended question answering (QA) and dermatological lesion segmentation. Central to this work is the Dermatology Assessment Schema (DAS), a novel expert-developed framework that systematically captures clinically meaningful dermatological features in a structured and standardized form. DAS comprises 36 high-level and 27 fine-grained assessment questions, with multiple-choice options in English and Chinese. Leveraging DAS, we provide expert-annotated datasets for both closed QA and segmentation and benchmark state-of-the-art multimodal models. For segmentation, we evaluate multiple prompting strategies and show that prompt design impacts performance: the default prompt achieves the best results under Mean-of-Max and Mean-of-Mean evaluation aggregation schemes, while an augmented prompt incorporating both patient query title and content yields the highest performance under majority-vote-based microscore evaluation, achieving a Jaccard index of 0.395 and a Dice score of 0.566 with BiomedParse. For closed-ended QA, overall performance is strong across models, with average accuracies ranging from 0.729 to 0.798; o3 achieves the best overall accuracy (0.798), closely followed by GPT-4.1 (0.796), while Gemini-1.5-Pro shows competitive performance within the Gemini family (0.783). We publicly release DermaVQA-DAS, the DAS schema, and evaluation protocols to support and accelerate future research in patient-centered dermatological vision-language modeling (https://osf.io/72rp3).
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https://arxiv.org/abs/2512.24340
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Academic Papers
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d37aa9e38b22231b59b9198225d4e3f53b126ae53e6593d4f23987a5d6b22b27
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2026-01-01T00:00:00-05:00
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FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles
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arXiv:2512.24345v1 Announce Type: new Abstract: This works presents an encoder-only transformer built with minimum layers for intrusion detection in the domain of Connected and Autonomous Vehicles using Federated Learning.
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https://arxiv.org/abs/2512.24345
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Academic Papers
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d8c81778a3ceb6f6609e1649894d93533a12d77334edbc7d79134b85cacaa60e
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2026-01-01T00:00:00-05:00
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Effects of Algorithmic Visibility on Conspiracy Communities: Reddit after Epstein's 'Suicide'
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arXiv:2512.24351v1 Announce Type: new Abstract: This paper examines how algorithmic visibility shapes a large conspiracy community on Reddit after Jeffrey Epstein's death. We ask whether homepage exposure changes who join r/conspiracy, how long they stay, and how they adapt linguistically, compared with users who arrive through organic discovery. Using a computational framework that combines toxicity scores, survival analysis, and lexical and semantic measures, the study shows that homepage visibility acts as a selection mechanism rather than a simple amplifier. Users who discover the community organically integrate more quickly into its linguistic and thematic norms and show more stable engagement over time. By contrast, users who arrive through visibility on the homepage remain semantically distant from core discourse and participate more briefly. Overall, algorithmic visibility reshapes audience size, community composition, and linguistic cohesion: newcomers who do not join organically have different incentives, integrate weakly, and leave quickly, which limits organic growth. In this high-risk setting, the observed behavioral and linguistic trajectories over five months do not match standard narratives in which incidental exposure to conspiracy content produces durable radicalization. These findings can inform the design of web platforms and recommendation systems that seek to curb harmful conspiracy exposure while supporting more responsible, transparent, and socially beneficial uses of algorithmic recommendations.
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https://arxiv.org/abs/2512.24351
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2a8d2e1b9dfb322cdc82afef97c4c6d6f1c31867f15949c6cc1d70de88ccd613
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2026-01-01T00:00:00-05:00
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Faster Algorithms for Global Minimum Vertex-Cut in Directed Graphs
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arXiv:2512.24355v1 Announce Type: new Abstract: We study the directed global minimum vertex-cut problem: given a directed vertex-weighted graph $G$, compute a vertex-cut $(L,S,R)$ in $G$ of minimum value, which is defined to be the total weight of all vertices in $S$. The problem, together with its edge-based variant, is one of the most basic in graph theory and algorithms, and has been studied extensively. The fastest currently known algorithm for directed global minimum vertex-cut (Henzinger, Rao and Gabow, FOCS 1996 and J. Algorithms 2000) has running time $\tilde{O}(mn)$, where $m$ and $n$ denote the number of edges and vertices in the input graph, respectively. A long line of work over the past decades led to faster algorithms for other main versions of the problem, including the undirected edge-based setting (Karger, STOC 1996 and J. ACM 2000), directed edge-based setting (Cen et al., FOCS 2021), and undirected vertex-based setting (Chuzhoy and Trabelsi, STOC 2025). However, for the vertex-based version in directed graphs, the 29 year-old $\tilde{O}(mn)$-time algorithm of Henzinger, Rao and Gabow remains the state of the art to this day, in all edge-density regimes. In this paper we break the $\Theta(mn)$ running time barrier for the first time, by providing a randomized algorithm for directed global minimum vertex-cut, with running time $O\left(mn^{0.976}\cdot\operatorname{polylog} W\right)$ where $W$ is the ratio of largest to smallest vertex weight. Additionally, we provide a randomized $O\left(\min\left\{m^{1+o(1)}\cdot k,n^{2+o(1)}\right\}\right)$-time algorithm for the unweighted version of directed global minimum vertex-cut, where $k$ is the value of the optimal solution. The best previous algorithm for the problem achieved running time $\tilde O\left(\min\left\{k^2 \cdot m, mn^{11/12+o(1)}, n^{2+o(1)}\right\}\right)$ (Forster et al., SODA 2020, Li et al., STOC 2021).
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https://arxiv.org/abs/2512.24355
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Academic Papers
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3109f244dcff19fa142504da411d111a0fb0d359bd4db51075455d7b93f70f9b
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2026-01-01T00:00:00-05:00
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Learning Context: A Unified Framework and Roadmap for Context-Aware AI in Education
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arXiv:2512.24362v1 Announce Type: new Abstract: We introduce a unified Learning Context (LC) framework designed to transition AI-based education from context-blind mimicry to a principled, holistic understanding of the learner. This white paper provides a multidisciplinary roadmap for making teaching and learning systems context-aware by encoding cognitive, affective, and sociocultural factors over the short, medium, and long term. To realize this vision, we outline concrete steps to operationalize LC theory into an interoperable computational data structure. By leveraging the Model Context Protocol (MCP), we will enable a wide range of AI tools to "warm-start" with durable context and achieve continual, long-term personalization. Finally, we detail our particular LC implementation strategy through the OpenStax digital learning platform ecosystem and SafeInsights R&D infrastructure. Using OpenStax's national reach, we are embedding the LC into authentic educational settings to support millions of learners. All research and pedagogical interventions are conducted within SafeInsights' privacy-preserving data enclaves, ensuring a privacy-first implementation that maintains high ethical standards while reducing equity gaps nationwide.
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https://arxiv.org/abs/2512.24362
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Academic Papers
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0e042fa759d3b6e76071b70cad3382cc0a1007b22ca2411d100e6fe852d252ed
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2026-01-01T00:00:00-05:00
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On the Factual Consistency of Text-based Explainable Recommendation Models
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arXiv:2512.24366v1 Announce Type: new Abstract: Text-based explainable recommendation aims to generate natural-language explanations that justify item recommendations, to improve user trust and system transparency. Although recent advances leverage LLMs to produce fluent outputs, a critical question remains underexplored: are these explanations factually consistent with the available evidence? We introduce a comprehensive framework for evaluating the factual consistency of text-based explainable recommenders. We design a prompting-based pipeline that uses LLMs to extract atomic explanatory statements from reviews, thereby constructing a ground truth that isolates and focuses on their factual content. Applying this pipeline to five categories from the Amazon Reviews dataset, we create augmented benchmarks for fine-grained evaluation of explanation quality. We further propose statement-level alignment metrics that combine LLM- and NLI-based approaches to assess both factual consistency and relevance of generated explanations. Across extensive experiments on six state-of-the-art explainable recommendation models, we uncover a critical gap: while models achieve high semantic similarity scores (BERTScore F1: 0.81-0.90), all our factuality metrics reveal alarmingly low performance (LLM-based statement-level precision: 4.38%-32.88%). These findings underscore the need for factuality-aware evaluation in explainable recommendation and provide a foundation for developing more trustworthy explanation systems.
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https://arxiv.org/abs/2512.24366
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Academic Papers
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1c41dfd2a38599709680edbe33ec01ea2c56a6ab1b82abef2e4e965c108a2bd2
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2026-01-01T00:00:00-05:00
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Skim-Aware Contrastive Learning for Efficient Document Representation
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arXiv:2512.24373v1 Announce Type: new Abstract: Although transformer-based models have shown strong performance in word- and sentence-level tasks, effectively representing long documents, especially in fields like law and medicine, remains difficult. Sparse attention mechanisms can handle longer inputs, but are resource-intensive and often fail to capture full-document context. Hierarchical transformer models offer better efficiency but do not clearly explain how they relate different sections of a document. In contrast, humans often skim texts, focusing on important sections to understand the overall message. Drawing from this human strategy, we introduce a new self-supervised contrastive learning framework that enhances long document representation. Our method randomly masks a section of the document and uses a natural language inference (NLI)-based contrastive objective to align it with relevant parts while distancing it from unrelated ones. This mimics how humans synthesize information, resulting in representations that are both richer and more computationally efficient. Experiments on legal and biomedical texts confirm significant gains in both accuracy and efficiency.
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https://arxiv.org/abs/2512.24373
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Academic Papers
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68380676ed60c0eef4aded376878101149ce57eab4b250fd89d5b6191a43cce9
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2026-01-01T00:00:00-05:00
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New Insights into Cascaded Geometric Flight Control: From Performance Guarantees to Practical Pitfalls
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arXiv:2512.24377v1 Announce Type: new Abstract: We present a new stability proof for cascaded geometric control used by aerial vehicles tracking time-varying position trajectories. Our approach uses sliding variables and a recently proposed quaternion-based sliding controller to demonstrate that exponentially convergent position trajectory tracking is theoretically possible. Notably, our analysis reveals new aspects of the control strategy, including how tracking error in the attitude loop influences the position loop, how model uncertainties affect the closed-loop system, and the practical pitfalls of the control architecture.
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https://arxiv.org/abs/2512.24377
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62490d74aa73c6735f8ef5aac713cf65336d702eb88426e3ce9f5eb4b9c0d9a8
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2026-01-01T00:00:00-05:00
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Tubular Riemannian Laplace Approximations for Bayesian Neural Networks
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arXiv:2512.24381v1 Announce Type: new Abstract: Laplace approximations are among the simplest and most practical methods for approximate Bayesian inference in neural networks, yet their Euclidean formulation struggles with the highly anisotropic, curved loss surfaces and large symmetry groups that characterize modern deep models. Recent work has proposed Riemannian and geometric Gaussian approximations to adapt to this structure. Building on these ideas, we introduce the Tubular Riemannian Laplace (TRL) approximation. TRL explicitly models the posterior as a probabilistic tube that follows a low-loss valley induced by functional symmetries, using a Fisher/Gauss-Newton metric to separate prior-dominated tangential uncertainty from data-dominated transverse uncertainty. We interpret TRL as a scalable reparametrised Gaussian approximation that utilizes implicit curvature estimates to operate in high-dimensional parameter spaces. Our empirical evaluation on ResNet-18 (CIFAR-10 and CIFAR-100) demonstrates that TRL achieves excellent calibration, matching or exceeding the reliability of Deep Ensembles (in terms of ECE) while requiring only a fraction (1/5) of the training cost. TRL effectively bridges the gap between single-model efficiency and ensemble-grade reliability.
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https://arxiv.org/abs/2512.24381
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Academic Papers
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3db583cba9413dd2c6087dc66f84ecbacbe95a691b4435970193cac26f6224d8
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2026-01-01T00:00:00-05:00
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Geometric Multi-Session Map Merging with Learned Local Descriptors
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arXiv:2512.24384v1 Announce Type: new Abstract: Multi-session map merging is crucial for extended autonomous operations in large-scale environments. In this paper, we present GMLD, a learning-based local descriptor framework for large-scale multi-session point cloud map merging that systematically aligns maps collected across different sessions with overlapping regions. The proposed framework employs a keypoint-aware encoder and a plane-based geometric transformer to extract discriminative features for loop closure detection and relative pose estimation. To further improve global consistency, we include inter-session scan matching cost factors in the factor-graph optimization stage. We evaluate our framework on the public datasets, as well as self-collected data from diverse environments. The results show accurate and robust map merging with low error, and the learned features deliver strong performance in both loop closure detection and relative pose estimation.
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https://arxiv.org/abs/2512.24384
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Academic Papers
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92dec7a2008adcc4e98d6da2e86143dfd9c35ceb3d8569a650e9232b1f44f923
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2026-01-01T00:00:00-05:00
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Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems
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arXiv:2512.24385v1 Announce Type: new Abstract: The rapid advancement of autonomous systems, including self-driving vehicles and drones, has intensified the need to forge true Spatial Intelligence from multi-modal onboard sensor data. While foundation models excel in single-modal contexts, integrating their capabilities across diverse sensors like cameras and LiDAR to create a unified understanding remains a formidable challenge. This paper presents a comprehensive framework for multi-modal pre-training, identifying the core set of techniques driving progress toward this goal. We dissect the interplay between foundational sensor characteristics and learning strategies, evaluating the role of platform-specific datasets in enabling these advancements. Our central contribution is the formulation of a unified taxonomy for pre-training paradigms: ranging from single-modality baselines to sophisticated unified frameworks that learn holistic representations for advanced tasks like 3D object detection and semantic occupancy prediction. Furthermore, we investigate the integration of textual inputs and occupancy representations to facilitate open-world perception and planning. Finally, we identify critical bottlenecks, such as computational efficiency and model scalability, and propose a roadmap toward general-purpose multi-modal foundation models capable of achieving robust Spatial Intelligence for real-world deployment.
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https://arxiv.org/abs/2512.24385
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Academic Papers
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63be561eeeafa1a6a7d8025b167836f684370fc55e0886c9d9b5c7b92aed656d
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2026-01-01T00:00:00-05:00
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RedunCut: Measurement-Driven Sampling and Accuracy Performance Modeling for Low-Cost Live Video Analytics
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arXiv:2512.24386v1 Announce Type: new Abstract: Live video analytics (LVA) runs continuously across massive camera fleets, but inference cost with modern vision models remains high. To address this, dynamic model size selection (DMSS) is an attractive approach: it is content-aware but treats models as black boxes, and could potentially reduce cost by up to 10x without model retraining or modification. Without ground truth labels at runtime, we observe that DMSS methods use two stages per segment: (i) sampling a few models to calculate prediction statistics (e.g., confidences), then (ii) selection of the model size from those statistics. Prior systems fail to generalize to diverse workloads, particularly to mobile videos and lower accuracy targets. We identify that the failure modes stem from inefficient sampling whose cost exceeds its benefit, and inaccurate per-segment accuracy prediction. In this work, we present RedunCut, a new DMSS system that addresses both: It uses a measurement-driven planner that estimates the cost-benefit tradeoff of sampling, and a lightweight, data-driven performance model to improve accuracy prediction. Across road-vehicle, drone, and surveillance videos and multiple model families and tasks, RedunCut reduces compute cost by 14-62% at fixed accuracy and remains robust to limited historical data and to drift.
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https://arxiv.org/abs/2512.24386
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Academic Papers
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d399eced34a27bfb5f7d380622bbe0ba89301dd00aa827adc91487c10f391d80
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2026-01-01T00:00:00-05:00
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FAST-IDS: A Fast Two-Stage Intrusion Detection System with Hybrid Compression for Real-Time Threat Detection in Connected and Autonomous Vehicles
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arXiv:2512.24391v1 Announce Type: new Abstract: We have implemented a multi-stage IDS for CAVs that can be deployed to resourec-constrained environments after hybrid model compression.
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https://arxiv.org/abs/2512.24391
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Academic Papers
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b08ee448928037bbe19707858af9375216cac1d295c69976ec0efff81b0d9ef3
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2026-01-01T00:00:00-05:00
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SourceBroken: A large-scale analysis on the (un)reliability of SourceRank in the PyPI ecosystem
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arXiv:2512.24400v1 Announce Type: new Abstract: SourceRank is a scoring system made of 18 metrics that assess the popularity and quality of open-source packages. Despite being used in several recent studies, none has thoroughly analyzed its reliability against evasion attacks aimed at inflating the score of malicious packages, thereby masquerading them as trustworthy. To fill this gap, we first propose a threat model that identifies potential evasion approaches for each metric, including the URL confusion technique, which can affect 5 out of the 18 metrics by leveraging a URL pointing to a legitimate repository potentially unrelated to the malicious package. Furthermore, we study the reliability of SourceRank in the PyPI ecosystem by analyzing the SourceRank distributions of benign and malicious packages in the state-of-the-art MalwareBench dataset, as well as in a real-world dataset of 122,398 packages. Our analysis reveals that, while historical data suggests a clear distinction between benign and malicious packages, the real-world distributions overlap significantly, mainly due to SourceRank's failure to timely reflect package removals. As a result, SourceRank cannot be reliably used to discriminate between benign and malicious packages in real-world scenarios, nor to select benign packages among those available on PyPI. Finally, our analysis reveals that URL confusion represents an emerging attack vector, with its prevalence increasing from 4.2% in MalwareBench to 7.0% in our real-world dataset. Moreover, this technique is often used alongside other evasion techniques and can significantly inflate the SourceRank metrics of malicious packages.
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https://arxiv.org/abs/2512.24400
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Academic Papers
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64291c3d651888476754791ebf15f5e89a282441393c86b6cfed08443bfa84ca
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2026-01-01T00:00:00-05:00
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Fast and Realistic Automated Scenario Simulations and Reporting for an Autonomous Racing Stack
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arXiv:2512.24402v1 Announce Type: new Abstract: In this paper, we describe the automated simulation and reporting pipeline implemented for our autonomous racing stack, ur.autopilot. The backbone of the simulation is based on a high-fidelity model of the vehicle interfaced as a Functional Mockup Unit (FMU). The pipeline can execute the software stack and the simulation up to three times faster than real-time, locally or on GitHub for Continuous Integration/- Continuous Delivery (CI/CD). As the most important input of the pipeline, there is a set of running scenarios. Each scenario allows the initialization of the ego vehicle in different initial conditions (position and speed), as well as the initialization of any other configuration of the stack. This functionality is essential to validate efficiently critical modules, like the one responsible for high-speed overtaking maneuvers or localization, which are among the most challenging aspects of autonomous racing. Moreover, we describe how we implemented a fault injection module, capable of introducing sensor delays and perturbations as well as modifying outputs of any node of the stack. Finally, we describe the design of our automated reporting process, aimed at maximizing the effectiveness of the simulation analysis.
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https://arxiv.org/abs/2512.24402
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Academic Papers
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svg
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6987bb7ce6d5f52b971936090027b70be4fb85cc579ac5c1b2e75ddb17c2513e
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2026-01-01T00:00:00-05:00
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Lifting Vision: Ground to Aerial Localization with Reasoning Guided Planning
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arXiv:2512.24404v1 Announce Type: new Abstract: Multimodal intelligence development recently show strong progress in visual understanding and high level reasoning. Though, most reasoning system still reply on textual information as the main medium for inference. This limit their effectiveness in spatial tasks such as visual navigation and geo-localization. This work discuss about the potential scope of this field and eventually propose an idea visual reasoning paradigm Geo-Consistent Visual Planning, our introduced framework called Visual Reasoning for Localization, or ViReLoc, which performs planning and localization using only visual representations. The proposed framework learns spatial dependencies and geometric relations that text based reasoning often suffer to understand. By encoding step by step inference in the visual domain and optimizing with reinforcement based objectives, ViReLoc plans routes between two given ground images. The system also integrates contrastive learning and adaptive feature interaction to align cross view perspectives and reduce viewpoint differences. Experiments across diverse navigation and localization scenarios show consistent improvements in spatial reasoning accuracy and cross view retrieval performance. These results establish visual reasoning as a strong complementary approach for navigation and localization, and show that such tasks can be performed without real time global positioning system data, leading to more secure navigation solutions.
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https://arxiv.org/abs/2512.24404
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Academic Papers
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svg
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d1ba2074445dd33be5555c59c736ccd577f7c7ef5e9fb05d181972a0776f5d8f
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2026-01-01T00:00:00-05:00
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Sufficient and Necessary Conditions for Eckart-Young-like Result for Tubal Tensors
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arXiv:2512.24405v1 Announce Type: new Abstract: A valuable feature of the tubal tensor framework is that many familiar constructions from matrix algebra carry over to tensors, including SVD and notions of rank. Most importantly, it has been shown that for a specific family of tubal products, an Eckart-Young type theorem holds, i.e., the best low-rank approximation of a tensor under the Frobenius norm is obtained by truncating its tubal SVD. In this paper, we provide a complete characterization of the family of tubal products that yield an Eckart-Young type result. We demonstrate the practical implications of our theoretical findings by conducting experiments with video data and data-driven dynamical systems.
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https://arxiv.org/abs/2512.24405
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Academic Papers
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svg
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89605785828c2c389cc8a99e46f348c35a66fe6d17b0093e5820bad467d2b98a
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2026-01-01T00:00:00-05:00
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Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models
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arXiv:2512.24407v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) and dynamic discrete choice (DDC) models explain sequential decision-making by recovering reward functions that rationalize observed behavior. Flexible IRL methods typically rely on machine learning but provide no guarantees for valid inference, while classical DDC approaches impose restrictive parametric specifications and often require repeated dynamic programming. We develop a semiparametric framework for debiased inverse reinforcement learning that yields statistically efficient inference for a broad class of reward-dependent functionals in maximum entropy IRL and Gumbel-shock DDC models. We show that the log-behavior policy acts as a pseudo-reward that point-identifies policy value differences and, under a simple normalization, the reward itself. We then formalize these targets, including policy values under known and counterfactual softmax policies and functionals of the normalized reward, as smooth functionals of the behavior policy and transition kernel, establish pathwise differentiability, and derive their efficient influence functions. Building on this characterization, we construct automatic debiased machine-learning estimators that allow flexible nonparametric estimation of nuisance components while achieving $\sqrt{n}$-consistency, asymptotic normality, and semiparametric efficiency. Our framework extends classical inference for DDC models to nonparametric rewards and modern machine-learning tools, providing a unified and computationally tractable approach to statistical inference in IRL.
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https://arxiv.org/abs/2512.24407
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Academic Papers
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a59a6ee39f031cf06b8aaef3159905643f6a0734b2576f8592587a910a420788
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2026-01-01T00:00:00-05:00
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DyStream: Streaming Dyadic Talking Heads Generation via Flow Matching-based Autoregressive Model
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arXiv:2512.24408v1 Announce Type: new Abstract: Generating realistic, dyadic talking head video requires ultra-low latency. Existing chunk-based methods require full non-causal context windows, introducing significant delays. This high latency critically prevents the immediate, non-verbal feedback required for a realistic listener. To address this, we present DyStream, a flow matching-based autoregressive model that could generate video in real-time from both speaker and listener audio. Our method contains two key designs: (1) we adopt a stream-friendly autoregressive framework with flow-matching heads for probabilistic modeling, and (2) We propose a causal encoder enhanced by a lookahead module to incorporate short future context (e.g., 60 ms) to improve quality while maintaining low latency. Our analysis shows this simple-and-effective method significantly surpass alternative causal strategies, including distillation and generative encoder. Extensive experiments show that DyStream could generate video within 34 ms per frame, guaranteeing the entire system latency remains under 100 ms. Besides, it achieves state-of-the-art lip-sync quality, with offline and online LipSync Confidence scores of 8.13 and 7.61 on HDTF, respectively. The model, weights and codes are available.
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https://arxiv.org/abs/2512.24408
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Academic Papers
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svg
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4572b4d5f62228b501b707a1dbbe5fff8a0c734c154f36f784aaced94bca96c2
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2026-01-01T00:00:00-05:00
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Comparing Approaches to Automatic Summarization in Less-Resourced Languages
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arXiv:2512.24410v1 Announce Type: new Abstract: Automatic text summarization has achieved high performance in high-resourced languages like English, but comparatively less attention has been given to summarization in less-resourced languages. This work compares a variety of different approaches to summarization from zero-shot prompting of LLMs large and small to fine-tuning smaller models like mT5 with and without three data augmentation approaches and multilingual transfer. We also explore an LLM translation pipeline approach, translating from the source language to English, summarizing and translating back. Evaluating with five different metrics, we find that there is variation across LLMs in their performance across similar parameter sizes, that our multilingual fine-tuned mT5 baseline outperforms most other approaches including zero-shot LLM performance for most metrics, and that LLM as judge may be less reliable on less-resourced languages.
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https://arxiv.org/abs/2512.24410
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Academic Papers
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svg
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a2603719568b0e9f1a2fa4bb2219275be5a1fae7fabacac2449a670e0a03eb73
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2026-01-01T00:00:00-05:00
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AI-Driven Evaluation of Surgical Skill via Action Recognition
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arXiv:2512.24411v1 Announce Type: new Abstract: The development of effective training and evaluation strategies is critical. Conventional methods for assessing surgical proficiency typically rely on expert supervision, either through onsite observation or retrospective analysis of recorded procedures. However, these approaches are inherently subjective, susceptible to inter-rater variability, and require substantial time and effort from expert surgeons. These demands are often impractical in low- and middle-income countries, thereby limiting the scalability and consistency of such methods across training programs. To address these limitations, we propose a novel AI-driven framework for the automated assessment of microanastomosis performance. The system integrates a video transformer architecture based on TimeSformer, improved with hierarchical temporal attention and weighted spatial attention mechanisms, to achieve accurate action recognition within surgical videos. Fine-grained motion features are then extracted using a YOLO-based object detection and tracking method, allowing for detailed analysis of instrument kinematics. Performance is evaluated along five aspects of microanastomosis skill, including overall action execution, motion quality during procedure-critical actions, and general instrument handling. Experimental validation using a dataset of 58 expert-annotated videos demonstrates the effectiveness of the system, achieving 87.7% frame-level accuracy in action segmentation that increased to 93.62% with post-processing, and an average classification accuracy of 76% in replicating expert assessments across all skill aspects. These findings highlight the system's potential to provide objective, consistent, and interpretable feedback, thereby enabling more standardized, data-driven training and evaluation in surgical education.
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https://arxiv.org/abs/2512.24411
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Academic Papers
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svg
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c3aa23eb9be43a4a78b7e40e288625aa904d75878f2cbe7ddea1533b6db9fc5c
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2026-01-01T00:00:00-05:00
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Language Model Agents Under Attack: A Cross Model-Benchmark of Profit-Seeking Behaviors in Customer Service
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arXiv:2512.24415v1 Announce Type: new Abstract: Customer-service LLM agents increasingly make policy-bound decisions (refunds, rebooking, billing disputes), but the same ``helpful'' interaction style can be exploited: a small fraction of users can induce unauthorized concessions, shifting costs to others and eroding trust in agentic workflows. We present a cross-domain benchmark of profit-seeking direct prompt injection in customer-service interactions, spanning 10 service domains and 100 realistic attack scripts grouped into five technique families. Across five widely used models under a unified rubric with uncertainty reporting, attacks are highly domain-dependent (airline support is most exploitable) and technique-dependent (payload splitting is most consistently effective). We release data and evaluation code to support reproducible auditing and to inform the design of oversight and recovery workflows for trustworthy, human centered agent interfaces.
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https://arxiv.org/abs/2512.24415
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Academic Papers
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svg
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62d0e0919e8a5d0e6d8921b87a62f3c638c16d4feb8562edf30c035aabeadd4c
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2026-01-01T00:00:00-05:00
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GateChain: A Blockchain Based Application for Country Entry Exit Registry Management
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arXiv:2512.24416v1 Announce Type: new Abstract: Recording entry and exit records for a country, with properties such as confidentiality, integrity, and auditability, is increasingly important due to rising international mobility and security requirements. Traditional border control systems, which rely on centralised databases, are vulnerable to data manipulation and have limited interoperability between institutions. This study presents GateChain, a blockchain-based application that addresses these vulnerabilities. GateChain aims to enhance data integrity, reliability, and transparency by recording entry and exit events on a distributed, immutable, and cryptographically verifiable ledger. The application provides real-time access control and verification for authorised institutions. This paper describes the architecture and security components of GateChain and evaluates its performance and security features.
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https://arxiv.org/abs/2512.24416
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Academic Papers
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svg
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6aa8d6ab959decb333d603e40feef3e868e453e3d1036220a9a8e1f1ab119743
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2026-01-01T00:00:00-05:00
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Counterfactual VLA: Self-Reflective Vision-Language-Action Model with Adaptive Reasoning
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arXiv:2512.24426v1 Announce Type: new Abstract: Recent reasoning-augmented Vision-Language-Action (VLA) models have improved the interpretability of end-to-end autonomous driving by generating intermediate reasoning traces. Yet these models primarily describe what they perceive and intend to do, rarely questioning whether their planned actions are safe or appropriate. This work introduces Counterfactual VLA (CF-VLA), a self-reflective VLA framework that enables the model to reason about and revise its planned actions before execution. CF-VLA first generates time-segmented meta-actions that summarize driving intent, and then performs counterfactual reasoning conditioned on both the meta-actions and the visual context. This step simulates potential outcomes, identifies unsafe behaviors, and outputs corrected meta-actions that guide the final trajectory generation. To efficiently obtain such self-reflective capabilities, we propose a rollout-filter-label pipeline that mines high-value scenes from a base (non-counterfactual) VLA's rollouts and labels counterfactual reasoning traces for subsequent training rounds. Experiments on large-scale driving datasets show that CF-VLA improves trajectory accuracy by up to 17.6%, enhances safety metrics by 20.5%, and exhibits adaptive thinking: it only enables counterfactual reasoning in challenging scenarios. By transforming reasoning traces from one-shot descriptions to causal self-correction signals, CF-VLA takes a step toward self-reflective autonomous driving agents that learn to think before they act.
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https://arxiv.org/abs/2512.24426
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Academic Papers
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svg
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f486de5600d2228161a01370406e040cb9a9360ca5321f055ca11bf9d5746d7f
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2026-01-01T00:00:00-05:00
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Subsecond 3D Mesh Generation for Robot Manipulation
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arXiv:2512.24428v1 Announce Type: new Abstract: 3D meshes are a fundamental representation widely used in computer science and engineering. In robotics, they are particularly valuable because they capture objects in a form that aligns directly with how robots interact with the physical world, enabling core capabilities such as predicting stable grasps, detecting collisions, and simulating dynamics. Although automatic 3D mesh generation methods have shown promising progress in recent years, potentially offering a path toward real-time robot perception, two critical challenges remain. First, generating high-fidelity meshes is prohibitively slow for real-time use, often requiring tens of seconds per object. Second, mesh generation by itself is insufficient. In robotics, a mesh must be contextually grounded, i.e., correctly segmented from the scene and registered with the proper scale and pose. Additionally, unless these contextual grounding steps remain efficient, they simply introduce new bottlenecks. In this work, we introduce an end-to-end system that addresses these challenges, producing a high-quality, contextually grounded 3D mesh from a single RGB-D image in under one second. Our pipeline integrates open-vocabulary object segmentation, accelerated diffusion-based mesh generation, and robust point cloud registration, each optimized for both speed and accuracy. We demonstrate its effectiveness in a real-world manipulation task, showing that it enables meshes to be used as a practical, on-demand representation for robotics perception and planning.
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https://arxiv.org/abs/2512.24428
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Academic Papers
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