Knowledge catalogue
arXiv:2606.24996v1 Announce Type: new Abstract: Forecasting leaderboards rank models by predictive quality, but their winners are often read as deployment-ready top-1 advice. That reading can fail whe
arXiv:2505.13731v4 Announce Type: replace Abstract: Worldwide image geolocalization-the task of predicting GPS coordinates from images taken anywhere on Earth-poses a fundamental challenge due to the
arXiv:2606.24889v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems, despite low overall word error rates, produce residual lexical errors that disproportionately affect semanti
arXiv:2606.25404v1 Announce Type: new Abstract: Large Language Models (LLMs) can reason over complex instructions but often fail to satisfy the physical and spatial constraints required for robotic ta
arXiv:2606.24948v1 Announce Type: new Abstract: Knowledge graph embedding (KGE) models predict single-hop links well but have no mechanism for zero-shot compositional queries: multi-hop questions whos
PUBG Ally is an interactive co-playable character for PUBG: BATTLEGROUNDS powered by an on-device small language model built with NVIDIA ACE technology, equipped to communicate and cooperate with play
arXiv:2606.25338v1 Announce Type: new Abstract: Large language models (LLMs) have shown promising performance across a wide range of biomedical applications, including medical question answering (QA),
I think this means you can collect ~10k hours just from open source datasets, which means basically anyone should be able to build a decent robot foundation model: 500 from the new BitRobot dataset 50
arXiv:2606.25971v1 Announce Type: new Abstract: Modern neural network training relies on optimizers such as Adam and Muon which act on each weight matrix as a single object. Yet every weight matrix ca
arXiv:2606.24897v1 Announce Type: cross Abstract: Biomedical text mining, scientometrics, and the construction of training corpora for biomedical large language models (LLMs) all assume that the abstr
arXiv:2506.18414v3 Announce Type: replace Abstract: Melanoma is a highly aggressive skin cancer, making early and accurate diagnosis critical. While deep learning excels in skin lesion classification,
arXiv:2606.25362v1 Announce Type: cross Abstract: Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose conse
arXiv:2606.25437v1 Announce Type: new Abstract: Existing Vision Foundation Model (VFM)-based iterative stereo pipelines under-exploit three information pathways: multi-scale backbone features are coll
arXiv:2606.25057v1 Announce Type: new Abstract: The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large languag
arXiv:2501.18916v2 Announce Type: replace Abstract: Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We pr
arXiv:2606.25297v1 Announce Type: new Abstract: Generative models have significantly advanced image generation, resulting in synthesized images that are increasingly indistinguishable from authentic o
Mirendil Inc., a startup developing artificial intelligence models for scientists, has raised 200 million in funding at a 1 billion valuation. The seed round was led by Andreessen Horowitz. Mirendil s
arXiv:2606.26087v1 Announce Type: new Abstract: Synthesizing a novel-view video from a monocular reference video along a target camera trajectory requires both geometric consistency and motion fidelit
A major overhaul of the Model Context Protocol due next month removes several longstanding protocol-level security risks but hands developers a fresh set of attack surfaces to defend, according to res
arXiv:2606.25757v1 Announce Type: new Abstract: Reinforcement Learning (RL) has enabled LLMs to excel in objective reasoning tasks such as mathematics and code generation. However, applying RL to open
arXiv:2606.15053v2 Announce Type: replace Abstract: Surrogate models are central to scientific machine learning, where they enable fast prediction, simulation, inference, and control for complex physi
arXiv:2507.16863v2 Announce Type: replace-cross Abstract: A common belief in multimodal research is that the perceptual weaknesses of vision--language models can be compensated by stronger language re
arXiv:2602.11575v3 Announce Type: replace-cross Abstract: Visual navigation models often struggle in real-world dynamic environments due to limited robustness to the sim-to-real gap and the difficulty
arXiv:2606.25215v1 Announce Type: new Abstract: Most vision-language-action (VLA) models are reactive: they predict the next action from the current instruction and observation, implicitly assuming th
arXiv:2606.25366v1 Announce Type: new Abstract: Deep-space missions need onboard autonomy that is both capable and certifiable. Rule-based autonomy is certifiable but brittle, while learned autonomy i
DigitalOcean has launched a cloud-based service enabling users to run OpenAI's Codex, an AI code generation model, on DigitalOcean's infrastructure. This offering allows developers to access Codex cap
arXiv:2606.25006v1 Announce Type: new Abstract: Target-specific peptide design requires sequence and structure co-design under full atom geometric constraints. Latent generative frameworks offer an ef
NVIDIA TensorRT's multi-device inference feature scales inference across multiple GPUs, enabling deployment of models that exceed single-GPU memory or reducing latency for memory-bound workloads. Each
arXiv:2601.23147v2 Announce Type: replace Abstract: The integrity of time in distributed Internet of Things (IoT) devices is crucial for reliable operation in energy cyber-physical systems, such as sm
arXiv:2606.25818v1 Announce Type: new Abstract: Shift-variant image degradation is frequently encountered in practical imaging systems where the point spread function (PSF) varies across the image fie
arXiv:2602.17104v2 Announce Type: replace-cross Abstract: We propose a streamlined spectral algorithm for community detection in the two-community stochastic block model (SBM) under constant edge dens
arXiv:2606.25407v1 Announce Type: new Abstract: Chest X-ray visual question answering (CXR VQA) requires models not only to predict correct answers, but also to produce reliable medical reasoning. How
arXiv:2606.25143v1 Announce Type: new Abstract: Objectives: To develop a codebook for self-stigma across cognitive, affective, and behavioral domains, and to estimate the prevalence, co-occurrence, an
arXiv:2606.25986v1 Announce Type: new Abstract: We study whether a scaling-law-style inference-compute frontier appears in limit order book prediction. Using FI-2010 and a suite of models ranging from
arXiv:2601.14945v2 Announce Type: replace Abstract: Large-scale Vision-Language-Action (VLA) models offer semantic generalization but suffer from high inference latency, limiting them to low-frequency
arXiv:2606.25627v1 Announce Type: new Abstract: Distributed intelligent systems increasingly need to train across data silos without centralizing raw data. Federated learning keeps data local but can
arXiv:2606.25161v1 Announce Type: new Abstract: Large language model (LLM) agents rely on long-term memory to support extended interactions and personalized assistance beyond finite context windows. E
arXiv:2601.22615v3 Announce Type: replace Abstract: Streaming recurrent models enable efficient 3D reconstruction by maintaining persistent state representations. However, they suffer from catastrophi
Researchers introduce generative causal testing, which translates black box models into clear hypotheses and verifies them in the scanner, revealing what specific brain regions respond to in language.
arXiv:2606.23977v1 Announce Type: new Abstract: Efficient sorter diversion control of automated material handling systems (MHS) is critical for optimizing operational efficiency in large-scale warehou
arXiv:2606.24177v1 Announce Type: cross Abstract: Large language models are making research production scalable, shifting the bottleneck from producing artifacts to judging claims. We present extsc{Ag
AI is quickly moving beyond emulating the human mind to predicting an increasingly large percentage of all observations in the world. General world models will become a new foundational infrastructure
arXiv:2509.14659v3 Announce Type: replace-cross Abstract: Current audio captioning relies on supervised learning with paired audio-caption data, which is costly to curate and may not reflect human pre
Yohei Nakajima proposes that sports organizations should make their data openly accessible through Model Context Protocol (MCP) to enable broader public creation of visualizations and data analyses. T
arXiv:2606.24160v1 Announce Type: new Abstract: Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of co
arXiv:2606.23898v1 Announce Type: cross Abstract: Distilling conditional diffusion models aims to transfer the behavior of a large teacher to a smaller student while preserving alignment across condit
Build b9784 is a release of llama.cpp, a C/C++ project that enables large language model inference with minimal setup on a wide range of hardware. The release includes pre-compiled binaries for multip
arXiv:2606.24164v1 Announce Type: cross Abstract: Recent end-to-end models for EEG-guided target speech extraction report impressive results, underscoring potential for neuro-steered hearing technolog
COMPANY BUILT ON IP THEFT RAGES AGAINST THEFT OF ITS OWN IP ANTHROPIC ACCUSED ALIBABA OF UNAUTHORIZED ACCESS TO ITS AI MODELS, OUTLINING ITS ALLEGATIONS IN LETTERS SENT TO U.S. SENATORS AND THE WHITE
arXiv:2606.24467v1 Announce Type: new Abstract: Long-context large language model (LLM) inference is increasingly constrained by the memory footprint and decoding cost of key-value (KV) caches, limiti
arXiv:2606.19791v2 Announce Type: replace-cross Abstract: The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired artic
arXiv:2606.24579v1 Announce Type: new Abstract: Parametric knowledge in Large Language Models is not equally accessible across languages. As a result, standard inference techniques often struggle to s
arXiv:2606.24418v1 Announce Type: new Abstract: Data augmentation is a simple and model-agnostic approach for exploiting known invariances in learning problems. Given a group acting on the input space
arXiv:2606.24271v1 Announce Type: cross Abstract: In this paper, we introduce two neural-network-based numerical schemes for solving systems of coupled ergodic Backward Stochastic Differential Equatio
arXiv:2606.23950v1 Announce Type: new Abstract: Subject-driven image generation faces an 'Identity-Diversity Paradox', where strong identity preservation often leads to rigid and low-diversity outputs
A Tesla Model 3 crashed into a residential home in Katy, Texas, killing a 76-year-old grandmother, with the driver claiming Autopilot was active at the time. Elon Musk disputed the claim, and Tesla's
arXiv:2606.23001v1 Announce Type: cross Abstract: On-device LLM inference is increasingly attractive for privacy-preserving, reliable, and cost-effective deployment, yet its energy and thermal costs r
arXiv:2606.24428v1 Announce Type: new Abstract: Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experien
arXiv:2606.24716v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are increasingly used to extract interpretable concepts from vision and vision language models, yet existing evaluation met
arXiv:2606.23724v1 Announce Type: cross Abstract: Large language models are increasingly used to answer questions over annual reports, earnings decks, and analyst notes, yet their outputs remain diffi