๐ Original Info Title: ArXiv ID: 2512.18593 Date: Authors: Unknown ๐ Abstract In multilingual nations like India, access to legal information is often hindered by language barriers, as much of the legal and judicial documentation remains in English. โฆ
๐ Original Info Title: ArXiv ID: 2512.18389 Date: Authors: Unknown ๐ Abstract This informal contribution presents an ongoing line of research that is pursuing a new approach to the construction of sound proofs for the formal verification and control โฆ
๐ Original Info Title: ArXiv ID: 2512.18132 Date: Authors: Unknown ๐ Abstract Edge AI inference is becoming prevalent thanks to the emergence of small yet high-performance microprocessors. This shift from cloud to edge processing brings several โฆ
๐ Original Info Title: ArXiv ID: 2512.18815 Date: Authors: Unknown ๐ Abstract AI weather prediction ensembles with latent noise injection and optimized with the continuous ranked probability score (CRPS) have produced both accurate and โฆ
๐ Original Info Title: ArXiv ID: 2512.17851 Date: Authors: Unknown ๐ Abstract Text-to-image (T2I) diffusion models generate high-quality images but often fail to capture the spatial relations specified in text prompts. This limitation can be traced โฆ
๐ Original Info Title: ArXiv ID: 2512.18483 Date: Authors: Unknown ๐ Abstract Insider threat detection (ITD) remains a significant challenge in cybersecurity due to the concealed nature of malicious activities by trusted insiders. This paper โฆ
๐ Original Info Title: ArXiv ID: 2512.17920 Date: Authors: Unknown ๐ Abstract Background: Large language models (LLMs) exhibit degraded performance under prompt compression, but the mechanisms underlying this degradation remain poorly understood. โฆ
๐ Original Info Title: ArXiv ID: 2512.18399 Date: Authors: Unknown ๐ Abstract Tokenization is a critical preprocessing step for large language models (LLMs), directly impacting training efficiency and downstream performance. General-purpose โฆ
๐ Original Info Title: ArXiv ID: 2512.18809 Date: Authors: Unknown ๐ Abstract The rapid growth of short-form video platforms increases the need for privacy-preserving moderation, as cloudbased pipelines expose raw videos to privacy risks, high โฆ
๐ Original Info Title: ArXiv ID: 2512.18878 Date: Authors: Unknown ๐ Abstract Automating crash video analysis is essential to leverage the growing availability of driving video data for traffic safety research and accountability attribution in โฆ
๐ Original Info Title: ArXiv ID: 2512.17979 Date: Authors: Unknown ๐ Abstract Industrial symbiosis fosters circularity by enabling firms to repurpose residual resources, yet its emergence is constrained by socio-spatial frictions that shape costs, โฆ
๐ Original Info Title: ArXiv ID: 2512.19960 Date: Authors: Unknown ๐ Abstract Intra-class variability is given according to the significance in the degree of dissimilarity between images within a class. In that sense, depending on its intensity, โฆ
๐ Original Info Title: ArXiv ID: 2512.18554 Date: Authors: Unknown ๐ Abstract Medical Large Vision-Language Models (Med-LVLMs) have shown promising results in clinical applications, but often suffer from hallucinated outputs due to misaligned visual โฆ
๐ Original Info Title: ArXiv ID: 2512.18525 Date: Authors: Unknown ๐ Abstract Understanding learning as a dynamic process is challenging due to the interaction of multiple factors, including cognitive load, internal state change, and subjective โฆ
๐ Original Info Title: ArXiv ID: 2512.19743 Date: Authors: Unknown ๐ Abstract Loss functions are fundamental to learning accurate 3D point cloud models, yet common choices trade geometric fidelity for computational cost. Chamfer Distance is efficient โฆ
๐ Original Info Title: ArXiv ID: 2512.18309 Date: Authors: Unknown ๐ Abstract We introduce Embedded Safety-Aligned Intelligence (ESAI), a theoretical framework for multi-agent reinforcement learning that embeds alignment constraints directly into โฆ
๐ Original Info Title: ArXiv ID: 2512.18209 Date: Authors: Unknown ๐ Abstract Empirical power-law scaling has been widely observed across modern deep learning systems, yet its theoretical origins and scope of validity remain incompletely understood. โฆ
๐ Original Info Title: ArXiv ID: 2512.17898 Date: Authors: Unknown ๐ Abstract Over a billion users across the globe interact with AI systems engineered with increasing sophistication to mimic human traits. This rapid adoption of humanlike AI has โฆ
๐ Original Info Title: ArXiv ID: 2512.22421 Date: Authors: Unknown ๐ Abstract We present a latent diffusion-based differentiable inversion method (LD-DIM) for PDEconstrained inverse problems involving high-dimensional spatially distributed โฆ
๐ Original Info Title: ArXiv ID: 2512.22883 Date: Authors: Unknown ๐ Abstract Cybersecurity is being fundamentally reshaped by foundation-model-based artificial intelligence. Large language models now enable autonomous planning, tool orchestration, โฆ
๐ Original Info Title: ArXiv ID: 2512.21241 Date: Authors: Unknown ๐ Abstract In hard-label black-box adversarial attacks, where only the top-1 predicted label is accessible, the prohibitive query complexity poses a major obstacle to practical โฆ
๐ Original Info Title: ArXiv ID: 2512.21099 Date: Authors: Unknown ๐ Abstract Figure 1 . We propose a high-fidelity head avatar method that combines analytic rigging with texel-space neural regression. Gaussian attributes are predicted in UV space โฆ
๐ Original Info Title: ArXiv ID: 2512.20664 Date: Authors: Unknown ๐ Abstract Large Language Models (LLMs) frequently produce hallucinated statements that are assigned high likelihood by the model itself, exposing a fundamental limitation of โฆ
๐ Original Info Title: ArXiv ID: 2512.20631 Date: Authors: Unknown ๐ Abstract We present a comprehensive zero-training temporal drift analysis of transformer-based sentiment models validated on authentic social media data from major real-world โฆ
๐ Original Info Title: ArXiv ID: 2512.20959 Date: Authors: Unknown ๐ Abstract Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) have โฆ
๐ Original Info Title: ArXiv ID: 2512.22337 Date: Authors: Unknown ๐ Abstract Although parameter-efficient fine-tuning methods, such as LoRA, only modify a small subset of parameters, they can have a significant impact on the model. Our โฆ
๐ Original Info Title: ArXiv ID: 2512.20623 Date: Authors: Unknown ๐ Abstract Smart home lighting systems consume 15-20% of residential energy but lack adaptive intelligence to optimize for user comfort and energy efficiency simultaneously. We โฆ
๐ Original Info Title: ArXiv ID: 2512.21526 Date: Authors: Unknown ๐ Abstract Large language models (LLMs) provide rich semantic priors and strong reasoning capabilities, making them promising auxiliary signals for recommendation. However, prevailing โฆ
๐ Original Info Title: ArXiv ID: 2512.13154 Date: Authors: Unknown ๐ Abstract Conversational agents often encounter ambiguous user requests, requiring an effective clarification to successfully complete tasks. While recent advancements in real-world โฆ
๐ Original Info Title: ArXiv ID: 2512.20363 Date: Authors: Unknown ๐ Abstract Federated learning (FL) supports privacypreserving, decentralized machine learning (ML) model training by keeping data on client devices. However, non-independent and โฆ
๐ Original Info Title: ArXiv ID: 2512.20416 Date: Authors: Unknown ๐ Abstract For most of its history, cosmology was a qualitatively constrained discourse on the universe, shaped by limited observational access and the absence of global dynamical โฆ
๐ Original Info Title: ArXiv ID: 2512.21613 Date: Authors: Unknown ๐ Abstract In this paper, we propose AMS-IO-Agent, a domainspecialized LLM-based agent for structure-aware input/output (I/O) subsystem generation in analog and mixed-signal (AMS) โฆ
๐ Original Info Title: ArXiv ID: 2512.21648 Date: Authors: Unknown ๐ Abstract Monte Carlo Tree Search (MCTS) has profoundly influenced reinforcement learning (RL) by integrating planning and learning in tasks requiring long-horizon reasoning, โฆ
๐ Original Info Title: ArXiv ID: 2512.20687 Date: Authors: Unknown ๐ Abstract Transformers operate as horizontal token-bytoken scanners; at each generation step, attending to an ever-growing sequence of tokenlevel states. This access pattern โฆ
๐ Original Info Title: ArXiv ID: 2512.22608 Date: Authors: Unknown ๐ Abstract Due to the high value and high failure rate of startups, predicting their success has become a critical challenge across interdisciplinary research. Existing approaches โฆ
๐ Original Info Title: ArXiv ID: 2512.21711 Date: Authors: Unknown ๐ Abstract Latent tokens are gaining attention for enhancing reasoning in large language models (LLMs), yet their internal mechanisms remain unclear. This paper examines the problem โฆ
๐ Original Info Title: ArXiv ID: 2512.12534 Date: Authors: Unknown ๐ Abstract detailed motion than state-of-the-art baselines while maintaining high visual integrity. Code will be released at https://qiisun.github.io/animus3d_page. ๐ Full Content We โฆ
๐ Original Info Title: ArXiv ID: 2512.20569 Date: Authors: Unknown ๐ Abstract Distilling pretrained softmax attention Transformers into more efficient hybrid architectures that interleave softmax and linear attention layers is a promising approach โฆ
๐ Original Info Title: ArXiv ID: 2512.20666 Date: Authors: Unknown ๐ Abstract Text-to-image diffusion models have drawn significant attention for their ability to generate diverse and highfidelity images. However, when generating from multiconcept โฆ
๐ Original Info Title: ArXiv ID: 2512.21351 Date: Authors: Unknown ๐ Abstract Building on the affective dream-replay reinforcement learning framework of CosmoCore, we introduce CosmoCore-Evo, an extension that incorporates evolutionary algorithms to โฆ
๐ Original Info Title: ArXiv ID: 2512.20605 Date: Authors: Unknown ๐ Abstract Google, * Core contributor. Large-scale autoregressive models pretrained on next-token prediction and finetuned with reinforcement learning (RL) have achieved unprecedented โฆ
๐ Original Info Title: ArXiv ID: 2512.21185 Date: Authors: Unknown ๐ Abstract Figure 1 High-quality 3D assets generated by UltraShape 1.0. Best viewed with zoom-in. ๐ Full Content holes, and thickening thin structures, while preserving fine-grained โฆ
๐ Original Info Title: ArXiv ID: 2512.20292 Date: Authors: Unknown ๐ Abstract Automatic presentation slide generation can greatly streamline content creation. However, since preferences of each user may vary, existing under-specified formulations โฆ
๐ Original Info Title: ArXiv ID: 2512.22540 Date: Authors: Unknown ๐ Abstract This paper argues that the traditional opposition between determinism and indeterminism in physics is representational rather than ontological. Deterministic-stochastic โฆ
๐ Original Info Title: ArXiv ID: 2512.20957 Date: Authors: Unknown ๐ Abstract Locating files and functions requiring modification in large software repositories is challenging due to their scale and structural complexity. Existing LLM-based methods โฆ
๐ Original Info Title: ArXiv ID: 2512.21316 Date: Authors: Unknown ๐ Abstract This paper derives "Scaling Laws for Economic Impacts"-empirical relationships between the training compute of Large Language Models (LLMs) and professional productivity. โฆ
๐ Original Info Title: ArXiv ID: 2512.20650 Date: Authors: Unknown ๐ Abstract The choice of attention mechanism in Transformer models involves a critical trade-off between modeling quality and inference efficiency. Multi-Head Attention (MHA) offers โฆ
๐ Original Info Title: ArXiv ID: 2512.21204 Date: Authors: Unknown ๐ Abstract Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compared to the data-hungry โฆ
๐ Original Info Title: ArXiv ID: 2512.20926 Date: Authors: Unknown ๐ Abstract The rapid advancement of large language models (LLMs) has enabled significant strides in various fields. This paper introduces a novel approach to evaluate the โฆ
๐ Original Info Title: ArXiv ID: 2512.21135 Date: Authors: Unknown ๐ Abstract Text-guided medical segmentation enhances segmentation accuracy by utilizing clinical reports as auxiliary information. However, existing methods typically rely on โฆ
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