Knowledge catalogue
arXiv:2610.05051v1 Announce Type: new Abstract: Time-series data are often sampled irregularly at high frequencies and exhibit long-range dependencies, which makes long-horizon modelling difficult. Co
arXiv:2610.04785v1 Announce Type: new Abstract: Camera-to-motion-capture (mocap) calibration is essential for using mocap as ground truth in robotics, AR/VR, and other computer vision tasks. However,
arXiv:2610.05195v1 Announce Type: new Abstract: The next generation of multimodal research agents must reason over long-lived research histories rather than short model completions. During a single ta
arXiv:2610.05023v1 Announce Type: new Abstract: We introduce Self-Saliency, a method for training Vision-Language Models (VLMs) to increase the alignment between their visual attention and the image r
arXiv:2610.05109v1 Announce Type: new Abstract: Recently, unified high-definition image restoration has attracted considerable attention; however, existing models tend to excessively increase their de
arXiv:2610.05265v1 Announce Type: new Abstract: Looped language models provide a parameter-efficient way to scale iterative test-time computation by repeatedly executing a shared recurrent core. Post-
arXiv:2610.03771v1 Announce Type: new Abstract: We propose a fine-tuning method for flow-matching diffusion models aimed at realistic artificial light modeling without the need for a large training da
arXiv:2610.04507v1 Announce Type: new Abstract: Double descent has sparked considerable interest, with recent work relating it to the data, the model and the learning configuration. Practical fine-tun
arXiv:2610.06195v1 Announce Type: cross Abstract: Learned feature representations in neural networks often contain structure beyond that directly used by the final task output. We study this structure
arXiv:2610.06095v1 Announce Type: new Abstract: Operator-learning benchmarks are stored at full precision and have grown to terabyte scale. Rate-distortion theory says how many bits the stored field n
arXiv:2610.04695v1 Announce Type: new Abstract: Operator eigenvalue problems appear throughout science. Classical methods usually discretize the operator into a matrix and then solve the resulting mat
arXiv:2610.04326v1 Announce Type: new Abstract: Pretrained flow models are now widely used as generative priors in science and vision, where inference-time guidance enables test-time constraints witho
arXiv:2603.09737v2 Announce Type: replace Abstract: Semantic occupancy prediction enables dense 3D geometric and semantic understanding for autonomous driving. However, existing camera-based approache
arXiv:2610.06133v1 Announce Type: new Abstract: Journal entry anomaly detectors are commonly evaluated on the full population with ROC-AUC, precision and recall, ignoring the review budget and which a
arXiv:2606.17345v2 Announce Type: replace Abstract: Although pitch sequencing is a central topic in baseball analytics, previous studies have primarily focused on improving predictive performance for
arXiv:2610.04419v1 Announce Type: new Abstract: Generative models are often trained with isotropic objectives such as mean-squared error. For data concentrated near a low-dimensional manifold, however
arXiv:2610.04036v1 Announce Type: new Abstract: Vision Transformers (ViTs) have shown strong performance in volumetric segmentation, but their effectiveness on clinical CT is limited by an isotropic E
arXiv:2507.07297v2 Announce Type: replace Abstract: Recent advances in large vision-language models have led to impressive performance in visual question answering and multimodal reasoning. However, i
arXiv:2610.04963v1 Announce Type: new Abstract: Graph few-shot class-incremental learning (GFSCIL) requires a model to continually recognize emerging classes from only a few labeled nodes while preser
arXiv:2610.06046v1 Announce Type: new Abstract: Magnetic navigation provides contact-free and line-of-sight-independent feedback for robotic systems, yet existing approaches typically rely on explicit
Malware reverse-engineering: solving an out-of-distribution investigation task. When faced with an unknown binary, Mistral Large 4 reverse-engineers it end-to-end. In this case, it concludes the sampl
arXiv:2602.06017v2 Announce Type: replace Abstract: Video fusion aims to integrate complementary information from multiple source videos while preserving temporal consistency. Effective modeling of te
Data scientists and ML engineers can now create, configure, start, stop, and open Amazon SageMaker Spaces on SageMaker HyperPod EKS clusters directly from SageMaker Studio. Launch JupyterLab and Code
As customers modernize to lakehouse architectures, they are standardizing on open formats such as Apache Iceberg to create a shared data estate across compatible engines. This enables you to build AI-
arXiv:2610.04510v1 Announce Type: cross Abstract: Diffusion models for structured scientific generation must produce samples satisfying hard geometric constraints imposed by physics, chemistry, or bio
arXiv:2510.25239v2 Announce Type: replace Abstract: Trees Outside Forests (TOF) play an important role in agricultural landscapes by supporting biodiversity, sequestering carbon, and regulating microc
arXiv:2610.06472v1 Announce Type: new Abstract: We address the challenge of accurate 3D object reconstruction from multi-view images in Gaussian Splatting. Existing object-level 3DGS methods reconstru
arXiv:2610.06510v1 Announce Type: new Abstract: When searching for an object, people choose their next move by considering both likely target locations and places already explored. The current view ca
arXiv:2610.03991v1 Announce Type: new Abstract: Multimodal skin lesion classification combines clinical images with patient metadata to improve diagnostic accuracy. However, complete metadata availabl
arXiv:2610.06751v1 Announce Type: new Abstract: Matrix completion underlies problems from tabular imputation to causal inference, yet existing tabular foundation models treat it as entry-by-entry pred
arXiv:2610.06801v1 Announce Type: new Abstract: Sparse attention is a primary approach to reducing the latency of diffusion transformers in long-sequence generation tasks, such as video and high-resol
arXiv:2603.23533v3 Announce Type: replace-cross Abstract: Markdown carries structure a parser reads for free: headers, section paths, and block boundaries. Many RAG pipelines also spend LLM calls per
arXiv:2610.05458v1 Announce Type: new Abstract: Running the same language model on different graphics processing unit (GPU) vendors can produce different logits, even when the model weights and inputs
arXiv:2610.04422v1 Announce Type: new Abstract: Guided diffusion samplers are increasingly used to reconstruct physical fields from sparse observations, but standard diagnostics do not say how much of
arXiv:2610.05196v1 Announce Type: new Abstract: Risk regulation imposes directional constraints on scores; we adopt their strict per-input form -- the score monotone non-decreasing in every exposure i
arXiv:2602.01081v2 Announce Type: replace Abstract: Medical Anomaly Detection (MedAD) offers a promising direction for medical image analysis with Large Multimodal Models (LMMs). However, progress is
arXiv:2610.04800v1 Announce Type: cross Abstract: Graphical consoles offer a practical interface for medical acquisition assistance, allowing agents to work through the controls and visual feedback us
Meet Mistral Large 4, aka Le Chonk. • 1T parameters, natively multimodal. 49B active. It is the best open weights model from US or Europe on aggregated benchmarks. • State-of-the-art on critical workl
arXiv:2610.04195v1 Announce Type: cross Abstract: Personal AI agents in enterprise multi-tenant deployments share a common vector store for long-term memory. Shared embedding spaces create a surface f
I’ve been building this for a long fucking time, and tonight I finally released Memoria 1.0.0. I built it because I actually wanted to use it. I wanted a real memory layer for local LLM applications t
arXiv:2610.03830v1 Announce Type: new Abstract: In partially observable Markov decision processes, the optimal policy generally depends on the history of observations and past actions. Asymmetric acto
arXiv:2610.06830v1 Announce Type: cross Abstract: Memory has become integral to the LLM agent ecosystem, supporting information retention and reuse across interactions. However, most existing agent me
arXiv:2610.05954v1 Announce Type: cross Abstract: Reward post-training of flow models either reweights the model's own samples under a KL penalty or a frozen reference, often for thousands of updates,
arXiv:2610.06039v1 Announce Type: new Abstract: Recent work has shown that probabilistic flow matching for time series forecasting benefits from a data-matched prior. The resulting prior introduces lo
arXiv:2610.06342v1 Announce Type: new Abstract: While reinforcement learning with verifiable rewards provides reliable outcome supervision for VideoLLMs, sequence-level rewards offer limited token-lev
Kate Rooney / CNBC: Meta, Walmart, Instinct, Shopify, Sierra, Stripe, and others publish the Personal Agent Protocol to standardize and secure how AI bots interact with businesses — A month after Meta
arXiv:2610.05014v1 Announce Type: new Abstract: Recent GPU kernel optimization agents retain what they learn in knowledge bases or as distilled skills. Kernel benchmarks score each attempt's implement
arXiv:2610.05100v1 Announce Type: new Abstract: State-of-the-art neural operators scale to complex meshes via slice-and-process architectures, yet many rely on linear compatibility scores for latent t
arXiv:2610.05252v1 Announce Type: new Abstract: Diffusion-based dataset distillation (DD) suffers from a fundamental objective mismatch: likelihood-driven diffusion models prioritize density approxima
Microsoft confirms on publicly accessible web page that OpenAI has been using Looped Transformers in the GPT-6 series, proving The Information's reporting was correct all along. GPT-6.1 Sol uses 2 inf
Introducing InferBench: A benchmark testing how well frontier LLMs infer a user's priorities from their instructions. We tested 12 LLMs across 20 scenarios with 2.8k conversations to see which models
arXiv:2502.02607v2 Announce Type: replace Abstract: The inverse design of microstructures plays a pivotal role in optimizing metamaterials with specific, targeted physical properties. While traditiona
arXiv:2610.06120v1 Announce Type: new Abstract: Large learning rates can qualitatively change the trajectory of neural network training, often pushing optimization into regimes far from classical grad
arXiv:2610.06582v1 Announce Type: cross Abstract: World-model controllers rely on action-conditioned dynamics for prediction and planning, yet real control systems often execute commands asynchronousl
arXiv:2610.05277v1 Announce Type: cross Abstract: Code language models must be maintained like the software around them: when a library evolves, a model keeps writing the interface that it saw during
arXiv:2508.15374v3 Announce Type: replace Abstract: Machine learning models often preserve biases present in training data, leading to unfair treatment of certain minority groups. Despite an array of
arXiv:2610.06559v1 Announce Type: cross Abstract: Modern online platforms commonly rank ads and organic content separately before blending them into a feed displayed to the user, overlooking externali
Mistral 4 is pretty good at lawyer bench and cyber security bench, but give it's European heritage, one can only assume it's also good at regulation bench Meet Mistral Large 4, aka Le Chonk. • 1T para
Mistral can pelican now! https://tools.simonwillison.net/markdown-svg-renderer?url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F0db8b8196e57711b352f6c5857cf5b35#response-1 Meet Mistral Large 4, aka Le Cho
My comment on Mistral Large 4 — Hacker News. wren6991: The benchmark is saturated. Frontier models are tested with an armadillo in fishnet tights jaywalking on Mars. OK well I couldn't resist this one