Knowledge catalogue
arXiv:2606.00278v1 Announce Type: new Abstract: For many real-world systems, causal ground truth is difficult to obtain, making claims about causal effects hard to assess. We develop methods for evalu
arXiv:2606.00112v1 Announce Type: cross Abstract: Evolutionary systems have demonstrated remarkable results in creative domains, with recent applications in generative typography, design, and music. H
arXiv:2606.00442v1 Announce Type: new Abstract: Many machine learning techniques rely on approximating a loss function's curvature, but this is notoriously hard to do at the scale of modern deep netwo
arXiv:2602.23197v2 Announce Type: replace Abstract: Transformer-based large language models exhibit in-context learning, enabling adaptation to downstream tasks via few-shot prompting with demonstrati
For organizations already using Azure and Fabric, this means a much faster path from governed data to internal software. • Build apps securely on top of governed Fabric data • Use existing authenticat
arXiv:2606.02167v1 Announce Type: new Abstract: Engineers designing production systems need to verify that a given layout supports all required production sequences. Automated planning techniques can
arXiv:2606.00054v1 Announce Type: cross Abstract: Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most exi
arXiv:2606.00202v1 Announce Type: cross Abstract: Standard machine learning pipelines often admit many near-optimal models. These 'Rashomon sets' pose a range of challenges and opportunities for uncer
arXiv:2606.02251v1 Announce Type: cross Abstract: Robust state estimation is central to robotic autonomy, yet classical Kalman filters struggle with frequency-dependent disturbances and model mismatch
arXiv:2503.15371v2 Announce Type: replace-cross Abstract: Robotic manipulation of unfamiliar objects in new environments is challenging due to limited generalisation capabilities. We propose a new ski
arXiv:2606.00062v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become foundational for grounding large language models in domain-specific corpora, yet conventional vector-bas
arXiv:2606.00998v1 Announce Type: new Abstract: We study cross-embodiment 6-DOF robot grasping. Unlike prior works, we require the model not only to generalize to novel objects / scenes but also to no
Here's the paper, which confirms that big changes are happening very quickly. Some really interesting approaches to teasing out what is happening in the software development cycle as well: https://pap
Hewlett Packard Enterprise Co.’s artificial intelligence servers sold like crazy during its fiscal second quarter, powering the company to an impressive earnings and revenue beat, and the company’s st
arXiv:2606.02145v1 Announce Type: new Abstract: Accurate prediction of polymerization dynamics is essential for process design, control, and optimization. Yet, purely mechanistic models require labor-
I have seen senior managers outpace ICs, and I have seen ICs run circles around the org chart. Titles tell you almost nothing about who is actually going to operate well in the next few years (and mig
IBM announced a $10 billion investment in quantum computing to advance the development and commercialization of quantum technologies. The investment aims to accelerate quantum hardware improvements, e
arXiv:2606.01205v1 Announce Type: new Abstract: Vision-language navigation (VLN) for UAVs demands grounding free-form instructions into 6-DoF flight under partial observability. While Vision-Language-
arXiv:2606.00041v1 Announce Type: cross Abstract: This study analyzes COVID-19 care pathways using the COVID Data for Shared Learning dataset. We build a transparent, reproducible pipeline that transf
arXiv:2603.28825v2 Announce Type: replace-cross Abstract: Using a stylised coordination problem drawn from inpatient capacity management, three archetypal forms of AI deployment are described: effort-
arXiv:2606.00241v1 Announce Type: cross Abstract: Measuring statistical dependency between high-dimensional random variables is a fundamental task in data science and machine learning. Neural mutual i
arXiv:2606.02161v1 Announce Type: cross Abstract: Video Large Language Models (Video-LLMs) achieve strong performance in video understanding, but their excessive visual tokens bring substantial comput
Intel Corp. today debuted a line of central processing units based on its latest manufacturing process and shared new details about its next-generation graphics cards. Both product families are geared
arXiv:2606.00157v1 Announce Type: cross Abstract: We consider establishing the interpretability theory of deep learning through constructing a corresponding relationship between the renormalization gr
It is difficult to know how good MAI-Thinking-1 is from the scores alone (like weirdly low GPQA & Terminal Bench 2.0) But Microsoft makes it really hard to try its models upon release (a general issue
arXiv:2606.01702v1 Announce Type: cross Abstract: Deep learning in computer-aided design (CAD) remains fundamentally constrained by the data scarcity challenge: authentic CAD data is difficult to coll
arXiv:2606.00328v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for knowledge base question answering (KBQA), where answering requires selecting entities from a ques
arXiv:2601.22328v2 Announce Type: replace Abstract: Real-world scientific systems are rarely observed through complete, regularly sampled state trajectories. Instead, measurements are often partial, n
arXiv:2602.20019v2 Announce Type: replace-cross Abstract: Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies.
arXiv:2606.01198v1 Announce Type: new Abstract: Strategic classification studies settings in which agents respond to a deployed classifier by modifying observable features at a cost. Classical models
arXiv:2505.14752v3 Announce Type: replace Abstract: Macro-aligned micro-records are crucial for credible simulations in social science and urban studies. For example, epidemic models are only reliable
arXiv:2606.01336v1 Announce Type: new Abstract: As real-world applications increasingly require processing inputs of 100k+ tokens, the gap between context length and inference efficiency has become a
arXiv:2408.01653v4 Announce Type: replace Abstract: Omnidirectional depth estimation presents a significant challenge due to the inherent distortions in panoramic images. Despite notable advancements,
Clare Duffy / CNN: Microsoft and Mayo Clinic partner for an AI model trained on Mayo's medical data, with plans to build an AI healthcare assistant and AI tools for clinicians — People have been seeki
arXiv:2606.01099v1 Announce Type: cross Abstract: Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience. However, while they perf
arXiv:2606.01891v1 Announce Type: cross Abstract: Mid-surface abstraction is essential for finite element analysis of thin-walled CAD models. Existing face pairing-based methods rely on handcrafted ge
arXiv:2606.00240v1 Announce Type: new Abstract: Effective real-world assistance requires AI agents with robust Theory of Mind (ToM): inferring human mental states from their behavior. Despite recent a
arXiv:2603.18016v2 Announce Type: replace-cross Abstract: Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to propose draft tokens that are subsequen
Move from test to production by running high-performance inference directly on Foundry. At #MSBuild, we demoed an end-to-end workflow showing how unified infrastructure improves latency, reduces cost,
arXiv:2510.14025v2 Announce Type: replace Abstract: Adversarial purification has achieved great success in combating adversarial image perturbations, which are usually assumed to be additive. However,
arXiv:2606.00809v1 Announce Type: new Abstract: Many real-world conversational settings for knowledge discovery, including podcasts, hiring screens, and marketplaces, require a purpose-driven understa
arXiv:2606.01527v1 Announce Type: new Abstract: Machine unlearning is motivated by legal and user-facing requirements to remove the influence of individuals' data from trained models, such as the righ
arXiv:2606.02223v1 Announce Type: new Abstract: Estimating the generative mechanism of large-scale networks is a fundamental challenge in statistical machine learning. It requires the identification o
arXiv:2606.02292v1 Announce Type: new Abstract: We present a method to acquire and estimate the sub-surface scattering properties of light transport at a highly detailed level by learning the pixel fo
arXiv:2510.03745v2 Announce Type: replace Abstract: Low-discrepancy points are designed to efficiently fill the space in a uniform manner. This uniformity is highly advantageous in many problems in sc
arXiv:2602.22101v3 Announce Type: replace-cross Abstract: Many real-world applications generate continuous data streams for regression. Hoeffding trees and their variants have a long-standing traditio
arXiv:2606.00009v1 Announce Type: new Abstract: Bayesian Optimization (BO) is widely and successfully adopted for solving optimization problems having an expensive-to-evaluate, black-box, and non-conv
arXiv:2606.00515v1 Announce Type: cross Abstract: Contact-rich manipulation demands both high-level semantic reasoning and the safe regulation of high-frequency contact dynamics. While Vision-Language
arXiv:2603.16142v2 Announce Type: replace Abstract: Large language models (LLMs) have recently been adopted as synthetic agents for public opinion simulation, offering a promising alternative to costl
arXiv:2606.01179v1 Announce Type: cross Abstract: Entropy production governs irreversibility and uncertainty in both physical and information-theoretic systems. While Physics-Informed Neural Networks
arXiv:2606.02073v1 Announce Type: new Abstract: Generating objects with specific symmetries is essential in various real-world scenarios. However, adapting existing 2D continuous representations to en
arXiv:2606.02156v1 Announce Type: cross Abstract: Anastomotic leak remains one of the most serious complications following colorectal cancer surgery, substantially affecting patient outcomes, recovery
arXiv:2606.02117v1 Announce Type: cross Abstract: Probabilistic time series forecasting has attracted increasing attention in financial applications due to the need to quantify risk and uncertainty in
arXiv:2606.01012v1 Announce Type: new Abstract: AI for materials science is a critical topic within AI for science, aiming to accelerate materials discovery and produce accurate property predictions.
arXiv:2606.02576v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, but real-world deployment requires them to continually a
arXiv:2606.00206v1 Announce Type: new Abstract: Post-training quantization (PTQ) is widely used to deploy large language models efficiently, but its effect on reasoning models is not well understood.
arXiv:2606.01948v1 Announce Type: cross Abstract: The growing popularity of group activities has increased the need for methods that provide recommendations to groups of users given their individual p
arXiv:2606.01172v1 Announce Type: new Abstract: Modeling unknown latent functions from finite, irregularly sampled measurements is a recurring challenge across science and engineering. Neural processe
arXiv:2606.00981v1 Announce Type: new Abstract: LLMs can plan by either generating action sequences directly as a Planner or translating tasks into domain specific language for an external solver as a
arXiv:2606.00808v1 Announce Type: new Abstract: Source-free graph domain adaptation (SF-GDA) aims to adapt source-trained graph models to unlabeled target graphs when source graphs are no longer acces