Knowledge catalogue
arXiv:2609.05565v1 Announce Type: cross Abstract: Large language model (LLM) sustainability is increasingly a serving-systems problem, not only a training problem. In production, energy and carbon imp
arXiv:2512.00311v3 Announce Type: replace Abstract: Knowledge Tracing (KT) aims to model student's knowledge state and predict future performance to enable personalized learning in Intelligent Tutorin
arXiv:2609.06665v1 Announce Type: new Abstract: Diffusion-based models enable monocular geometry estimation, yet their pixel-space precision is limited by a shared, under-studied error source: VAE rec
arXiv:2609.07051v1 Announce Type: new Abstract: World models increasingly serve as the predictive core of model-based reinforcement learning agents, enabling them to simulate future dynamics and reaso
arXiv:2307.02719v5 Announce Type: replace Abstract: Uncertainty sampling is a classical active-learning strategy, yet the statistical objective induced by its query rule is often implicit. We introduc
arXiv:2511.03272v2 Announce Type: replace Abstract: Diffusion-based text-to-video models are increasingly capable, but mask-based editing over hundreds of frames remains challenging: naive long-video
arXiv:2609.07420v1 Announce Type: new Abstract: Vision cues are available and informative for pedestrian action prediction, but obtaining stable target-centric representations from video frames remain
arXiv:2609.05888v1 Announce Type: new Abstract: In this paper, we address the challenging problem of 4D reconstruction from sparse-view videos. This setup usually relies on monocular depth estimation
arXiv:2609.06591v1 Announce Type: new Abstract: Developing unified physics-based humanoid controllers that can navigate complex 3D scenes and manipulate objects remains a longstanding challenge. Exist
arXiv:2602.19322v2 Announce Type: replace Abstract: Ultrasound (US) imaging poses unique challenges for representation learning due to its inherently noisy acquisition process. The low signal-to-noise
arXiv:2609.06100v1 Announce Type: new Abstract: Verifiable outcome rewards guide language-model post-training, but sequence-level advantages do not identify which token-level decisions should be prese
arXiv:2609.05541v1 Announce Type: new Abstract: Implicit Neural Representations (INR) provide a compact and content-adaptive paradigm for video compression, typically representing a video through shar
arXiv:2609.05579v1 Announce Type: new Abstract: Predicting postoperative spinal morphology from preoperative radiographs could provide valuable support for scoliosis surgical planning, but remains cha
arXiv:2609.07815v1 Announce Type: new Abstract: Current text-to-image systems typically employ a 'text encoder plus diffusion decoder' paradigm, in which text semantics directly modulate continuous la
arXiv:2411.08727v2 Announce Type: replace Abstract: Robots in human-centered environments require accurate scene understanding to perform high-level tasks effectively. This understanding can be achiev
arXiv:2609.07178v1 Announce Type: new Abstract: Deep learning models for CT scan analysis are often limited by the scarcity of precise pixel-level annotations, which require significant radiologist ef
arXiv:2609.07313v1 Announce Type: cross Abstract: Segmentation of complex structures in X-ray tomographic data is a fundamental task in biomedical research, but it often requires large amounts of prec
We’re entering an era of agent-first tools: stronger feedback loops between agents, with human judgment where it matters. We keep hearing from senior engineers that their mental load is growing, even
arXiv:2503.04666v2 Announce Type: replace Abstract: High-quality benchmarks are crucial for driving progress in machine learning research. However, despite the growing interest in video generation, th
arXiv:2609.09116v1 Announce Type: new Abstract: Normalization renders large parts of neural networks effectively scale invariant, inducing a hidden feedback loop in which learning-rate schedules and w
arXiv:2609.07002v1 Announce Type: new Abstract: Generalizable and robust dexterous in-hand manipulation requires a policy to infer object pose, geometry, contact, and potential slip from partial and n
NVIDIA introduced two Rust projects for GPU development in September 2026: `cuda‑oxide`, a nightly‑only backend that compiles SIMT‑style kernels to PTX via LLVM and uses DisjointSlice and launch contr
I don't think anyone posted about this here, but Qwen released a finetuned version of 3.5 4 for driving. The full Bf16 checkpoint is 9B. This is a very interesting development of Chinese AI labs tackl
arXiv:2609.05228v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures provide an efficient paradigm for scaling large language models (LLMs), yet fixed top-k routing activates the sam
arXiv:2609.05320v1 Announce Type: new Abstract: Deepfake detection models often rely on high-quality inputs, fixed inference paths, and computationally expensive architectures, limiting their use in l
arXiv:2609.05016v1 Announce Type: cross Abstract: Scaling laws guide the design choices for training large foundation models, but deriving them involves training an exhaustive grid over hyperparameter
arXiv:2602.14941v2 Announce Type: replace-cross Abstract: Maintaining spatial world consistency over long horizons remains a central challenge for camera-controllable video generation. Existing memory
arXiv:2609.04709v1 Announce Type: new Abstract: Text-guided diffusion editing raises disinformation concerns, making reliable image provenance essential. While watermarks are commonly used for this pu
arXiv:2609.04738v1 Announce Type: new Abstract: In this paper, we study the problem of personalized survey response prediction using fine-tuned large language models (LLMs). This task poses unique cha
arXiv:2609.04965v1 Announce Type: new Abstract: Cross-view localization (CVL) estimates the pose of a ground image by matching it to a geo-referenced satellite image. To bridge the extreme viewpoint g
arXiv:2609.05258v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to formulate optimization models from natural-language problem descriptions, yet realistic operatio
arXiv:2609.04513v1 Announce Type: cross Abstract: Compound AI workflows are increasingly used to serve complex AI tasks by coordinating multiple AI models and software components. This approach enable
arXiv:2609.04891v1 Announce Type: cross Abstract: Four-dimensional (4D) flow magnetic resonance imaging (MRI) is a powerful non-invasive technique for visualizing and quantifying complex blood flow pa
arXiv:2603.23723v3 Announce Type: replace-cross Abstract: Deep spatially selective filters achieve high-quality enhancement with real-time capable architectures for stationary speakers of known direct
arXiv:2609.04747v1 Announce Type: cross Abstract: To enable accurate and rapid photon control point and proton beamlet dose calculation in the DoseRAD2026 challenge, we present BEAM3R, a dose estimati
arXiv:2609.05289v1 Announce Type: new Abstract: Automated reference-based evaluation methods play a critical role in assessing natural language generation systems. Existing meta-evaluation primarily m
arXiv:2609.04995v1 Announce Type: new Abstract: Deep Imbalanced Regression (DIR) is pervasive in continuous prediction tasks across diverse modalities, such as age estimation, depth prediction, and pr
arXiv:2609.05150v1 Announce Type: cross Abstract: This paper introduces Regime-aware Constraint-Based and Noise-Based causal discovery with Markov Blankets (RCBNB-MB), a novel causal discovery algorit
arXiv:2609.04504v1 Announce Type: new Abstract: Cardiac, neural, behavioral, and speech measurements from wearable and mobile devices provide partial, noise-sensitive views of physiological state. Bio
arXiv:2609.04999v1 Announce Type: new Abstract: This paper describes the participation of the BIT.UA team from the University of Aveiro in the 14th edition of the BioASQ Task B challenge on biomedical
arXiv:2609.04356v1 Announce Type: cross Abstract: This paper analyzes the implementation of blockchain-based integrity mechanisms in Greek Fiscal Electronic Mechanisms (FEMs) and the central tax infor
arXiv:2609.05227v1 Announce Type: new Abstract: Recent reports during the AAAI-27 review cycle highlight the risk of reviewers coordinating bids for reciprocal assignment advantage. Prior work treats
arXiv:2609.04895v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models activate only a small subset of experts per token, but the full expert set often exceeds GPU memory, causing repeated we
arXiv:2609.04647v1 Announce Type: new Abstract: Traditional Retrieval-Augmented Generation (RAG) systems score each passage independently against the query, assembling context sets that may be individ
arXiv:2609.04792v1 Announce Type: cross Abstract: Activation steering has shown promise for controlling LLM generation along well-defined attributes, but it remains unclear whether it can handle the m
arXiv:2609.04870v1 Announce Type: new Abstract: Multiplayer Online Battle Arena (MOBA) games rely on matchmaking to maintain competitive balance. Our prior work, CUPID, framed matchmaking as an assign
arXiv:2609.04653v1 Announce Type: new Abstract: Multilingual large language models often struggle to reason in low- to mid-resource languages. Prior work has shown that translation can improve multili
arXiv:2601.21712v3 Announce Type: replace Abstract: Vision Language Action (VLA) models enable instruction-following manipulation, yet their deployment on coordinated dual-arm platforms remains severe
arXiv:2609.04807v1 Announce Type: cross Abstract: Multi-vehicle cooperative autonomous driving enhances the safety and reliability of autonomous driving systems through information sharing among conne
arXiv:2609.04830v1 Announce Type: new Abstract: Personalized federated learning (PFL) is a promising paradigm for collaborative learning over distributed devices, where edge nodes collaboratively trai
arXiv:2609.05255v1 Announce Type: new Abstract: Explicit primitive-based radiance fields such as 3D Gaussian Splatting typically model view-dependent appearance using low-order spherical harmonics (SH
arXiv:2609.05028v1 Announce Type: new Abstract: Acquiring high quality annotated medical image data is critical for training deep learning models; however, annotation is expensive, time consuming, and
arXiv:2506.07309v3 Announce Type: replace Abstract: Can Large Language Models (LLMs) be trained to avoid hallucinating factual statements, and can Retrieval-Augmented Generation (RAG) be triggered onl
arXiv:2510.12368v2 Announce Type: replace-cross Abstract: Shallow Recurrent Decoder networks are a novel data-driven methodology able to provide accurate state estimation in engineering systems, such
arXiv:2609.05090v1 Announce Type: new Abstract: Evaluation of medical artificial intelligence agents remains predominantly answer-centric, assessing only the correctness of final outputs while overloo
arXiv:2609.04651v1 Announce Type: new Abstract: This paper studies adaptive recommendation under intent drift, where feedback from each recommendation outcome can reveal whether the relational evidenc
arXiv:2609.05269v1 Announce Type: cross Abstract: LLM agent systems increasingly combine provenance tracking, authorization, policy enforcement, protocol adapters, and execution controls. However, ind
arXiv:2609.04770v1 Announce Type: new Abstract: Autonomous robots continuously encounter objects, changes, and situations, and every event admitted into cognition should receive an appropriate cogniti
arXiv:2609.04377v1 Announce Type: new Abstract: Enterprise AI deployments fail not from model inadequacy, but because organizations lack a structured substrate encoding how they decide, negotiate, and
arXiv:2609.04865v1 Announce Type: new Abstract: Skill libraries improve the sample efficiency of agentic reinforcement learning (RL) by enabling large language model (LLM) agents to reuse procedural k