The Serial Scaling Hypothesis
arXiv:2507.12549v4 Announce Type: replace Abstract: While machine learning has advanced through massive parallelization, we identify a critical blind spot: some problems are fundamentally sequential.
Knowledge catalogue
arXiv:2507.12549v4 Announce Type: replace Abstract: While machine learning has advanced through massive parallelization, we identify a critical blind spot: some problems are fundamentally sequential.
arXiv:2604.26317v1 Announce Type: new Abstract: The vulnerabilities of deep neural networks against singularities have raised serious concerns regarding their deployment in the physical world. One of
arXiv:2604.26460v1 Announce Type: new Abstract: Stylistic personalization - making LLMs write in a specific individual's style, rather than merely adapting to task preferences - lacks evaluation groun
arXiv:2602.11731v2 Announce Type: replace Abstract: Existing multimodal large language models have achieved high-fidelity visual perception and exploratory visual generation. However, a precision para
arXiv:2604.26946v1 Announce Type: new Abstract: Breakthrough progress in vision-based navigation through unknown environments has been achieved by using multimodal large language models (MLLMs). These
arXiv:2505.13963v3 Announce Type: replace Abstract: Quantization methods are widely used to accelerate inference and streamline the deployment of large language models (LLMs). Although quantization's
arXiv:2603.08182v2 Announce Type: replace-cross Abstract: Large language models often underperform in many European languages due to the dominance of English and a few high-resource languages in train
arXiv:2505.24867v2 Announce Type: replace-cross Abstract: Recent advances in vision-language models (VLMs) have made impressive strides in understanding spatio-temporal relationships in videos. Howeve
arXiv:2409.01115v5 Announce Type: replace Abstract: This article presents a new approach based on MiniRocket, called SelF-Rocket, for fast time series classification (TSC). Unlike existing approaches
arXiv:2604.26247v1 Announce Type: cross Abstract: Multimodal recommendation improves user modeling by integrating collaborative signals with heterogeneous item content. In real applications, user inte
arXiv:2503.04872v3 Announce Type: replace-cross Abstract: The challenge of reducing the size of Large Language Models (LLMs) while maintaining their performance has gained significant attention. Howev
arXiv:2604.26553v1 Announce Type: cross Abstract: Large language models (LLMs) demonstrate strong multilingual capabilities, yet often fail to consistently generate responses in the intended language,
arXiv:2510.14703v2 Announce Type: replace Abstract: Large language models (LLMs) excel at function calling, but inference scaling has been explored mainly for unstructured generation. We propose an in
arXiv:2604.26370v1 Announce Type: new Abstract: Vision-language models have shown strong performance, but they often generalize poorly to specialized domains. While semi-supervised vision-language lea
arXiv:2509.23980v2 Announce Type: replace Abstract: Diffusion models have recently shown promising results for video super-resolution (VSR). However, directly adapting generative diffusion models to V
arXiv:2604.26020v1 Announce Type: cross Abstract: Usability testing with experts and potential users can assess the effectiveness, efficiency, and user satisfaction of graphical user interfaces (GUIs)
arXiv:2601.03423v3 Announce Type: replace-cross Abstract: Adapting language models to the clinical domain through continued pretraining and instruction tuning requires costly retraining for each new m
arXiv:2511.22972v3 Announce Type: replace Abstract: Large language models (LLMs) achieve strong performance across diverse tasks but suffer from high inference latency due to their autoregressive gene
arXiv:2604.26597v1 Announce Type: cross Abstract: Timely and reliable multilingual communication is critical during natural and human-induced disasters, but developing effective solutions for crisis c
arXiv:2604.07692v2 Announce Type: replace Abstract: Large Multimodal Models (LMMs) achieve state-of-the-art performance in high-stakes domains like healthcare, yet their reasoning remains opaque. Curr
arXiv:2604.26501v1 Announce Type: cross Abstract: Generating sports game reports from structured tables is a complex table-to-text task that demands both precise data interpretation and fluent narrati
arXiv:2604.26951v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters f
arXiv:2412.10679v3 Announce Type: replace Abstract: Blood pressure (BP) measurement is crucial for daily health assessment. Remote photoplethysmography (rPPG), which extracts pulse waves from face vid
arXiv:2506.16742v3 Announce Type: replace Abstract: To be adopted in safety-critical domains like medical image analysis, AI systems must provide human-interpretable decisions. Variational Information
arXiv:2604.26873v1 Announce Type: new Abstract: We propose UAPAR, an Uncertainty-Aware Pedestrian Attribute Recognition framework. To the best of our knowledge, this is the first EDL-based uncertainty
arXiv:2604.26836v1 Announce Type: new Abstract: Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploratio
arXiv:2604.26360v1 Announce Type: cross Abstract: Reinforcement learning (RL) systems typically optimize scalar reward functions that assume precise and reliable evaluation of outcomes. However, real-
arXiv:2604.26489v1 Announce Type: new Abstract: DNNs have gained widespread adoption in feature interaction recommendation models. However, there has been a longstanding debate on their roles. On one
arXiv:2604.26694v1 Announce Type: cross Abstract: We propose X-WAM, a Unified 4D World Model that unifies real-time robotic action execution and high-fidelity 4D world synthesis (video + 3D reconstruc
arXiv:2604.26411v1 Announce Type: new Abstract: Runtime monitoring is essential to ensure the safety of ML applications in safety-critical domains. However, current research is fragmented, with indepe
arXiv:2604.26837v1 Announce Type: new Abstract: Long-context LLM serving is bottlenecked by the cost of attending over ever-growing KV caches. Dynamic sparse attention promises relief by accessing onl
arXiv:2604.26216v1 Announce Type: new Abstract: This paper addresses the problem of anomaly detection in accounting subject association structures, proposing a structured modeling and unsupervised dis
arXiv:2512.03992v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are essential for embodied AI and safety-critical applications, such as robotics and autonomous systems. However
arXiv:2601.00545v3 Announce Type: replace Abstract: Many problems in robotics involve both continuous and discrete components, and modeling them together for estimation tasks has been a long standing
arXiv:2602.03412v2 Announce Type: replace Abstract: As large language model agents tackle increasingly complex long-horizon tasks, effective post-training becomes critical. Prior work faces fundamenta
arXiv:2508.07852v2 Announce Type: replace-cross Abstract: Recent research on learnable neural representations has been widely adopted in the field of 3D scene reconstruction and neural rendering appli
arXiv:2509.09870v2 Announce Type: replace-cross Abstract: Large language models (LLMs) enable conversational agents (CAs) to express distinctive personalities, raising new questions about how such des
arXiv:2604.26218v1 Announce Type: new Abstract: Brain encoding models not only serve to decipher how visual stimuli are transformed into neural responses, but also represent a critical step toward vis
arXiv:2604.26806v1 Announce Type: cross Abstract: Transformer-based architectures have established a dominant paradigm in global semantic perception; however, they remain fundamentally constrained by
arXiv:2603.05811v2 Announce Type: replace Abstract: Current video generation models suffer from high computational latency, making real-time applications prohibitively costly. In this paper, we addres
arXiv:2505.22897v2 Announce Type: replace Abstract: While bias in large language models (LLMs) is well-studied, similar concerns in vision-language models (VLMs) have received comparatively less atten
arXiv:2604.26781v1 Announce Type: new Abstract: Surgical training involves didactic teaching, mentor-led learning, surgical skills laboratories, and direct exposure to surgery; however, increasing cli
arXiv:2505.20032v3 Announce Type: replace Abstract: Tactile sensing provides local essential information that is complementary to visual perception, such as texture, compliance, and force. Despite rec
arXiv:2603.07080v3 Announce Type: replace-cross Abstract: Vision-and-Language Navigation (VLN) increasingly relies on large vision-language models, but their inference cost conflicts with real-time de
arXiv:2604.26313v1 Announce Type: cross Abstract: We present VulStyle, a multi-modal software vulnerability detection model that jointly encodes function-level source code, non-terminal Abstract Synta
arXiv:2604.26839v1 Announce Type: new Abstract: Assisting humans in open-world outdoor environments requires robots to translate high-level natural-language intentions into safe, long-horizon, and soc
arXiv:2510.14438v2 Announce Type: replace Abstract: The hallmark of Deep Research agents lies in compositional reasoning, the capacity to aggregate distributed, heterogeneous information into coherent
arXiv:2604.26844v1 Announce Type: new Abstract: Many of the thousands of attested languages share common configurations of features, creating a spectrum from typologically very rare (e.g., object-verb
arXiv:2510.17548v2 Announce Type: replace Abstract: Language models are often evaluated with scalar metrics like accuracy, but such measures fail to capture how models internally represent ambiguity,
arXiv:2604.26412v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference, but SOTA hidden-state-based drafters suffer from long-range decay: draft accuracy degrades as the specul
arXiv:2604.26649v1 Announce Type: cross Abstract: Large reasoning models such as DeepSeek-R1 and OpenAI o1 generate extended chains of thought spanning thousands of tokens, yet their integration with
arXiv:2604.26644v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on mathematical reasoning tasks but remain unreliable on challenging instances. Existing test-t
arXiv:2604.26342v1 Announce Type: new Abstract: Unlike single-face forgeries, deepfakes in complex multi-person interaction scenarios (such as group photos and multi-person meetings) more closely refl
arXiv:2604.26604v1 Announce Type: new Abstract: Federated learning (FL) trains a shared model from updates contributed by distributed clients, often implicitly assuming that contributing clients are r
arXiv:2604.03260v2 Announce Type: replace-cross Abstract: Standard attention scales quadratically with sequence length. Efficient attention methods reduce this O(n^2) cost, but when retrofitted into p
arXiv:2604.26174v1 Announce Type: new Abstract: Domain shift, where deviations between training and deployment data distributions degrade model performance, is a key challenge in underwater environmen
arXiv:2604.26934v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown strong performance on static visual understanding, yet they still struggle with dynamic spatial reasoning that
arXiv:2604.26619v1 Announce Type: new Abstract: Aspect-based Sentiment Analysis (ABSA) extracts fine-grained opinions toward specific aspects within text but remains largely English-focused despite ma
arXiv:2604.25129v1 Announce Type: cross Abstract: High-fidelity 3D assets exhibit intriguing global illumination effects like subsurface scattering, glossy interreflections, and fine-scale fiber scatt
arXiv:2505.13766v5 Announce Type: replace-cross Abstract: Software Quality Assurance (SQA) is critical for delivering reliable, secure, and efficient software products. The Software Quality Assurance