arXiv:2607.14947v1 Announce Type: cross Abstract: Modern generative models are increasingly trained using model-generated signals, creating both opportunities for self-improvement and risks of collapse. We study optimal…
arXiv:2607.14127v1 Announce Type: new Abstract: Representative clutter height (RCH) is a key parameter in radio propagation and interference analysis because it captures the dominant height of…
arXiv:2607.14157v1 Announce Type: new Abstract: Retrieval over corpora that mix several domains often returns relevant but wrong-domain evidence that ranking metrics miss and that conformal risk…
arXiv:2607.14160v1 Announce Type: new Abstract: Wildfire detection from satellite imagery is a semantic image segmentation problem that has proven to be difficult due to challenges such…
arXiv:2607.14174v1 Announce Type: new Abstract: Financial sentiment extraction has largely relied on news text and supervised extraction against return labels alone, leaving 10-K filings -- and…
arXiv:2607.14171v1 Announce Type: new Abstract: Reinforcement learning has emerged as the dominant paradigm for training large language model (LLM) agents that interact with executable sandboxes. State-of-the-art…
arXiv:2607.14180v1 Announce Type: new Abstract: World models are widely used in offline reinforcement learning (RL) to improve sample efficiency and generate experience beyond a fixed dataset.…
arXiv:2607.14176v1 Announce Type: new Abstract: Reliable, low-latency uplink connectivity is a key requirement for C-V2X networks in dense urban environments, where fast channel variations and blockages…
arXiv:2512.18390v2 Announce Type: replace Abstract: Organizations often have an incumbent predictive model in production when new data sources become available. Because historical training data lack the…
arXiv:2408.12548v3 Announce Type: replace Abstract: Machine Learning (ML) has become central to Autonomous Vehicles (AVs), supporting perception, prediction, planning, control, and decision-making in dynamic environments. However,…
arXiv:2505.23869v4 Announce Type: replace-cross Abstract: A proposition that connects randomness and compression is put forward via Gibbs entropy over set of measurement vectors associated with a…
arXiv:2605.21751v2 Announce Type: replace Abstract: Text-to-optimization requires two separable capabilities: modeling -- choosing the right optimization structure -- and binding -- grounding every coefficient, index, and…
arXiv:2607.11624v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency. In robotics, a recent line of work has emerged addressing this…
arXiv:2604.04977v2 Announce Type: replace-cross Abstract: Software supply chain security compromises often stem from cascaded interactions of vulnerabilities, for example, between multiple vulnerable components. Yet, Software Bill…
arXiv:2607.14682v1 Announce Type: cross Abstract: Efficient multimodal document question answering with explicit visual grounding, locating the precise document region that supports each answer remains an open…
arXiv:2607.14423v1 Announce Type: new Abstract: Frozen self-supervised vision models can align parts of generic objects, but it remains unclear whether this correspondence extends to human faces,…
arXiv:2607.14179v1 Announce Type: cross Abstract: Ovarian cancer is the deadliest gynecological malignancy; accurate and objective segmentation of adnexal masses and functional ovaries in ultrasound (US) remains…
arXiv:2607.14415v1 Announce Type: new Abstract: Computed Tomography (CT) carries significant ionizing radiation risks, driving the need for sparse-view reconstruction. Implicit scene representations (ISRs) address this by…
arXiv:2607.15278v1 Announce Type: new Abstract: Video models are evolving into vision foundation models, yet they still lack human-like multi-step reasoning. Streaming autoregressive diffusion models are efficient…
arXiv:2607.14350v1 Announce Type: new Abstract: Fine-grained manipulation recognition requires modeling evolving relations among hands, objects, tools, and supporting surfaces. Conventional graph-based methods use pairwise edges that…
arXiv:2603.26842v3 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems. Existing methods require training…
arXiv:2607.14338v1 Announce Type: new Abstract: Region-based loss functions, such as the Dice loss, have established themselves as the de facto standard for highly class- and region-imbalanced…
arXiv:2606.29059v2 Announce Type: replace Abstract: World modeling requires forecasting uncertain futures while preserving information useful for downstream perception. Existing visual world models often struggle to satisfy…
arXiv:2607.14334v1 Announce Type: new Abstract: Learned image compression (LIC) is bottlenecked by the need to store independent models for each rate-distortion operating point. Existing variable bit-rate…