Knowledge catalogue
arXiv:2509.10746v3 Announce Type: replace Abstract: Large language models in healthcare often produce emotionally flat or opaque responses, failing to provide the transparent reasoning required for cl
arXiv:2605.02015v1 Announce Type: new Abstract: Machine learning-based intrusion detection requires complex models to capture patterns in high-dimensional, noisy, and class-imbalanced raw network traf
arXiv:2605.01489v1 Announce Type: cross Abstract: Frontier scientific reasoning is rapidly emerging as a key foundation for advancing AI agents in automated scientific discovery. Deep research agents
arXiv:2605.01135v1 Announce Type: new Abstract: Recent progress in generative models has significantly advanced image editing capabilities, yet precise and intuitive user control remains difficult. Sp
arXiv:2605.01720v1 Announce Type: cross Abstract: Existing large-scale sign language resources typically provide supervision only at the level of raw video-text alignment and are often produced in lab
Some things never change. If you don’t understand this one, you don’t understand what’s happening AI. Marcus, 1998: neural nets have trouble generalizing far beyond the data. Marcus, 2001, 2012, 2019,
arXiv:2605.01856v1 Announce Type: cross Abstract: Stabilized regression aims to identify a set of predictors whose conditional relationship with a response variable remains invariant across different
arXiv:2601.08510v3 Announce Type: replace Abstract: Movie screenplays are rich long-form narratives that interleave complex character relationships, temporally ordered events, and dialogue-driven inte
arXiv:2605.00924v1 Announce Type: new Abstract: AI-generated content (AIGC) detectors are increasingly deployed in high-stakes settings such as academic integrity screening, yet their reliability rest
arXiv:2605.01060v1 Announce Type: cross Abstract: We present SURGE, a streaming GPU encoding system deployed in production to generate embeddings for over 800 million texts across 40,000 logical parti
arXiv:2603.04299v3 Announce Type: replace Abstract: Large Language Models (LLMs) often exhibit highly agreeable and reinforcing conversational styles, also known as AI-sycophancy. Although this behavi
arXiv:2605.01771v1 Announce Type: new Abstract: An auditor instructs an AI assistant: 'open each file individually using the Read tool -- no scripts, no agents.' The AI replies 'Yes' -- then issues a
arXiv:2411.04696v5 Announce Type: replace Abstract: Learning correlations from data forms the foundation of today's machine learning (ML) and artificial intelligence research. While contemporary metho
arXiv:2605.01498v1 Announce Type: new Abstract: Visual query localization (VQL) aims to predict the spatio-temporal response of the most recent occurrence in a sequence given a query. Currently, most
arXiv:2602.07262v3 Announce Type: replace Abstract: Second-order feature statistics are central to texture recognition, yet existing mechanisms exhibit a structural tension: bilinear pooling and Gram
arXiv:2605.02126v1 Announce Type: new Abstract: Ultrasound foundation models have achieved strong performance on structured prediction tasks but remain exclusively vision-based, limiting zero-shot and
arXiv:2605.02134v1 Announce Type: new Abstract: Video Variational Autoencoder (VAE) enables latent video generative modeling by mapping the visual world into compact spatiotemporal latent spaces, impr
arXiv:2605.02356v1 Announce Type: new Abstract: We introduce the Z-Domain Neural Operator (ZNO), a causal neural operator whose layers are stable low-rank multiple-input multiple-output (MIMO) rationa
arXiv:2508.06361v4 Announce Type: replace Abstract: Large Language Models (LLMs) are widely deployed in reasoning, planning, and decision-making tasks, making their trustworthiness critical. A signifi
arXiv:2601.22816v2 Announce Type: replace Abstract: Advances in generative modeling have recently been adapted to tabular data containing discrete and continuous features. However, generating mixed-ty
arXiv:2605.00768v1 Announce Type: new Abstract: The transformer is the most popular neural architecture for language modeling. The cornerstone of the transformer is its global attention mechanism, whi
arXiv:2605.00330v1 Announce Type: new Abstract: Operator learning enables fast surrogate modeling of high-dimensional dynamical systems, but existing approaches face two fundamental limitations: quadr
arXiv:2602.18757v2 Announce Type: replace Abstract: Human driving behavior is inherently diverse, yet most end-to-end autonomous driving (E2E-AD) systems learn a single average driving style, neglecti
arXiv:2507.14201v3 Announce Type: replace-cross Abstract: We present ExCyTIn-Bench, the first benchmark to Evaluate an LLM agent X on the task of Cyber Threat Investigation through security questions
arXiv:2605.00605v1 Announce Type: new Abstract: Most recent extreme rescaling methods struggle to preserve semantically consistent structures and produce realistic details, due to the severely ill-pos
arXiv:2605.00400v1 Announce Type: cross Abstract: Table Retrieval (TR) has traditionally been formulated as an ad-hoc retrieval problem, where relevance is primarily determined by topical semantic sim
arXiv:2605.00184v1 Announce Type: new Abstract: With the growing integration of human-computer interaction into everyday life, advances in machine learning have enabled systems to better perceive and
arXiv:2602.07744v3 Announce Type: replace Abstract: Diffusion and flow models have become the dominant paradigm for generative modeling on Riemannian manifolds, with successful applications in protein
arXiv:2605.00392v1 Announce Type: new Abstract: DeepSeek-OCR leverages visual-text compression to reduce long-text processing costs and accelerate inference, yet visual tokens remain prone to redundan
arXiv:2605.00482v1 Announce Type: new Abstract: Mobile network operators must monitor thousands of heterogeneous network elements across the radio access network and the packet core, each exposing hig
arXiv:2505.06698v4 Announce Type: replace Abstract: Evaluating Large Language Models (LLMs) has become increasingly important, with automatic evaluation benchmarks gaining prominence as alternatives t
Sentinel is a local-first AI coding desktop application built with Rust and Tauri that automatically routes coding tasks to appropriate models based on task complexity. Instead of swapping between mul
arXiv:2605.00206v1 Announce Type: cross Abstract: Current transformers discard their rich latent residual stream between positions, reconstructing latent reasoning context at each new position and lea
arXiv:2605.00684v1 Announce Type: new Abstract: Temporal Video Grounding (TVG) aims to localize temporal moments in an untrimmed video that semantically correspond to given natural language queries. R
arXiv:2605.00738v1 Announce Type: new Abstract: With the proliferation of Electronic Health Records (EHRs), a critical challenge in building predictive models is determining the optimal historical dat
arXiv:2605.00536v1 Announce Type: cross Abstract: Scaling laws for Large Language Models (LLMs) establish that model quality improves with computational scale, yet edge deployment imposes strict const
arXiv:2506.11989v3 Announce Type: replace Abstract: Test-time scaling offers a promising way to improve the reasoning performance of vision-language large models (VLLMs) without additional training. I
arXiv:2505.11329v5 Announce Type: replace-cross Abstract: Distributed inference of large language models (LLMs) using tensor parallelism can introduce communication overheads of 20% even over GPUs con
arXiv:2605.00658v1 Announce Type: new Abstract: Recent progress has shown that video diffusion models (VDMs) can be repurposed for diverse multimodal graphics tasks. However, existing methods often tr
NVIDIA's Instant Splat is a breakthrough AI technique that synthesizes missing information from a few images to create immersive 3D models of virtual worlds. The method produces high-quality 3D models
ZIT (Z-Image Turbo) is a compact, fast distilled model tuned for photorealism that uses 8 inference steps with a fixed CFG of 1, which limits creativity . ZIB (Z-Image Base) is an undistilled model id
arXiv:2501.07451v4 Announce Type: replace Abstract: Model compression is essential in the deployment of large Computer Vision models on embedded devices. However, static optimization techniques (e.g.
arXiv:2211.06762v2 Announce Type: replace Abstract: Omnidirectional micro aerial vehicles (OMAVs) are more capable of doing environmentally interactive tasks due to their ability to exert full wrenche
arXiv:2602.11897v3 Announce Type: replace-cross Abstract: Cybersecurity decision-making increasingly occurs in environments characterized by uncertainty, partial observability, and adversarial manipul
All the Doomers and hawks are lining up behind this distillation 'attack' farce because they want to see open source banned. It's really as simple as that. They want to take away your right to choose,
arXiv:2604.27582v1 Announce Type: new Abstract: Surgical resection remains the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC), and eligibility depends on accurate asse
arXiv:2603.01444v2 Announce Type: replace Abstract: Synthetic data generation is an important capability for privacy-preserving data sharing, system benchmarking and test data provisioning. For mixed-
arXiv:2604.28122v1 Announce Type: new Abstract: Modern visual world modeling systems increasingly rely on high-capacity architectures and large-scale data to produce plausible motion, yet they often f
arXiv:2504.19342v3 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm in artificial intelligence to align large models with human pr
arXiv:2509.05291v2 Announce Type: replace-cross Abstract: Large language models (LLMs) learn non-trivial abstractions during pretraining, such as detecting irregular plural noun subjects. However, bec
arXiv:2604.27977v1 Announce Type: new Abstract: Despite recent progress in language models and agents for scientific data-driven discovery, further advancing their capabilities is held back by the abs
arXiv:2604.28176v1 Announce Type: cross Abstract: Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as b
arXiv:2604.27085v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) on consumer-grade GPUs is highly cost-effective, yet constrained by limited GPU memory and slow PCIe intercon
This discussion thread covers how to adjust temperature parameters in Ollama, the local language model platform. The post likely addresses configuration methods for controlling model sampling temperat
arXiv:2604.27335v1 Announce Type: new Abstract: Web filtering systems rely on accurate web content classification to block cyber threats, prevent data exfiltration, and ensure compliance. However, cla
arXiv:2604.27052v1 Announce Type: cross Abstract: Nonlinear models and optimization methods have successfully tackled a rapidly growing set of problems in recent years. Indeed, a relatively small tool
arXiv:2604.27374v1 Announce Type: new Abstract: As LLMs become credible readers of earnings calls, investor-relations Q&A, guidance, and disclosure language, supervised financial NLP benchmarks increa
arXiv:2504.15564v3 Announce Type: replace-cross Abstract: Existing class-level code generation datasets are either synthetic (ClassEval: 100 classes) or insufficient in scale for modern training needs
arXiv:2604.27201v1 Announce Type: cross Abstract: Hybrid-thinking language models expose explicit think and no-think modes, but current designs do not separate them cleanly. Even in no-think mode, mod
arXiv:2604.27721v1 Announce Type: cross Abstract: This paper presents a physically-informed fuzzy clustering of vertical sounding ionograms for automatically separating the ionogram into tracks suitab