arXiv:2607.14107v1 Announce Type: new Abstract: The inference efficiency of diffusion large language models (dLLMs) is constrained by two challenges: bidirectional attention precludes efficient KV-cache reuse, while…
arXiv:2607.15265v1 Announce Type: new Abstract: We present SceneBind, an omni-modal representation of realistic scenes with joint semantic and 3D spatial understanding across vision, audio and language.…
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:2607.14157v1 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:2607.14936v1 Announce Type: new Abstract: Real-world optimization problems often involve black-box functions and uncertainties in their evaluation, widely referred to as noisy optimization problems (NOPs). Evolutionary…
arXiv:2607.14371v1 Announce Type: cross Abstract: Large Language Models (LLMs) have revolutionized AI services, but a critical tension emerges: while personalization improves model performance, it consumes scarce…
arXiv:2607.15123v1 Announce Type: cross Abstract: Recent FPGAs have improved deep learning (DL) inference efficiency through dedicated tensor blocks and in-BRAM computation. ReRAM-based analog in-memory computing (IMC)…
arXiv:2607.14967v1 Announce Type: cross Abstract: Most existing approaches to AI-Generated Text Detection (AIGTD) treat documents as static objects and base their decisions on aggregate statistics or…
arXiv:2607.14252v1 Announce Type: cross Abstract: Long-horizon robot planning requires more than predicting what actions will do next; it also requires memory of the embodied experience that…
arXiv:2607.14305v1 Announce Type: new Abstract: In this paper we propose DCVC-Mamba (DCVC-MB), a neural video codec framework for B-frame coding. Our approach incorporates an IBP frame…
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:2607.14127v1 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:2607.14630v1 Announce Type: new Abstract: Layer-wise post-training quantization of large language models minimizes each layer's reconstruction error in isolation, allowing quantization errors to accumulate across depth…
arXiv:2607.15229v1 Announce Type: cross Abstract: We develop data-driven algorithms for maintaining $N$ independent identical machines under a textit{block replacement policy}, in which each machine is replaced…
arXiv:2607.14702v1 Announce Type: cross Abstract: Automatic recognition of ambivalence and hesitancy is challenging because these states may be expressed through inconsistent linguistic, acoustic, facial, and contextual…
arXiv:2607.14178v1 Announce Type: new Abstract: Recent advances in Large Language Models have fueled autonomous AI agents capable of tackling complex scientific tasks, yet existing automated research…
arXiv:2607.14108v1 Announce Type: new Abstract: This paper introduces tool efficiency, a new quantitative metric to evaluate the rate of useful tool calls in an LLM agent…
arXiv:2603.29759v3 Announce Type: replace Abstract: Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment. However, existing benchmarks suffer from three…
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:2607.14947v1 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:2607.14278v1 Announce Type: new Abstract: The paper develops the approach to the runtime analysis of evolutionary algorithms on the basis of limit theorems from probability theory.…
arXiv:2607.14460v1 Announce Type: cross Abstract: We study the sample covariance error of centered Gaussians. A remarkable breakthrough [66] established the correct error scaling order and explicitly…
arXiv:2607.14144v1 Announce Type: new Abstract: The Platonic Representation Hypothesis (PRH) holds that as models scale, representations of heterogeneous networks converge toward a shared model of reality.…
arXiv:2607.15263v1 Announce Type: cross Abstract: Security-agent evaluations commonly measure peak offensive capability under generous inference budgets, emphasizing vulnerability discovery, exploit development, penetration testing, and CTF completion.…