Knowledge catalogue
arXiv:2608.29256v1 Announce Type: cross Abstract: Integrated sensing and communication (ISAC) networks can serve compatible requests through shared sensing sessions, but consolidation couples admissio
arXiv:2608.29265v1 Announce Type: cross Abstract: Kernel density estimation (KDE) is one of the most fundamental statistical estimators of density functions. Its direct implementation on a dataset of
arXiv:2608.24888v2 Announce Type: replace Abstract: Agents that act on a user's behalf must plan differently for different users, and increasingly do so from some structured representation of user con
The Information: Source: OpenAI's Astra model uses “recurrent depth”, a technique that improves cost and performance but obscures the AI's reasoning, making it harder to monitor — OpenAI says its fort
arXiv:2608.30050v1 Announce Type: new Abstract: Building a blockchain digital twin largely requires translating domain knowledge and specific system descriptions into a simulator architecture, calibra
arXiv:2608.28708v1 Announce Type: cross Abstract: Prospective silent trials provide an important bridge between retrospective validation of artificial intelligence (AI) models and their use in clinica
arXiv:2608.30258v1 Announce Type: cross Abstract: Neurosymbolic reasoning has shown promising success in addressing complex reasoning tasks by combining large language models (LLMs) and symbolic solve
arXiv:2608.30956v1 Announce Type: cross Abstract: Counterfactual explanations (CEs) are widely used in explainable artificial intelligence (AI) to show how a model's outputs would change if the input
arXiv:2608.29999v1 Announce Type: cross Abstract: Large audio-language models (LALMs) describe audio at the clip level but cannot assign timestamps to the events, speakers, or sounds they identify. De
arXiv:2606.21803v2 Announce Type: replace Abstract: Next-token prediction is the self-supervised signal that trains language models, and every observed prompt token provides the same signal at test ti
This video captures the crux of the debate: Cybersecurity experts are pissed off by the METR/Redwood Research report because AI alignment has sucked the oxygen out of AI security, even though the Open
arXiv:2608.30923v1 Announce Type: cross Abstract: Stream learning is commonly evaluated through predictive performance and adaptation to concept drift. However, sustained operation of a stream learner
arXiv:2608.30472v1 Announce Type: new Abstract: Molecular toxicity prediction is increasingly used to prioritise compounds before experimental testing, but conventional benchmark performance can overs
arXiv:2511.19811v2 Announce Type: replace-cross Abstract: Image diversity remains a fundamental challenge for text-to-image diffusion models. Low-diversity generation often leads to repetitive outputs
arXiv:2506.17251v3 Announce Type: replace-cross Abstract: Although large language models (LLMs) have achieved remarkable performance, the inherent stochasticity of their reasoning processes and varyin
arXiv:2608.30567v1 Announce Type: new Abstract: We present Turing-20B-A2B, a 20B-parameter Mixture-of-Experts language model that activates approximately 2B parameters per token, designed for long-con
arXiv:2510.22588v2 Announce Type: replace-cross Abstract: Spoken dialogue models currently lack the ability for fine-grained speech style control, a critical capability for human-like interaction that
arXiv:2511.20022v3 Announce Type: replace-cross Abstract: Recent advancements in multimodal large language models (MLLMs) have shown strong understanding of driving scenes, drawing interest in their a
arXiv:2608.31059v1 Announce Type: cross Abstract: When do text embeddings work as inputs to empirical analysis? Their use rests on an assumption: that we can trade text for its low-dimensional embeddi
arXiv:2608.30757v1 Announce Type: new Abstract: Before an intelligent system can decide whether an action is allowed, it must first know which rules the action has approached. A single proposed action
arXiv:2608.28552v1 Announce Type: new Abstract: As a precursor to high-dimensional biomedical data modeling, reliable feature selection can reduce computational expense, improve modeling performance,
arXiv:2608.28274v1 Announce Type: new Abstract: Let nge2 be even, let lambda=(lambda_1,ldots,lambda_n)inR^n have pairwise distinct coordinates, and define the difference-power matrix [ A_d(lambda) :=
arXiv:2601.16425v2 Announce Type: replace Abstract: Designing experiments that systematically gather data from complex physical systems is central to accelerating scientific discovery. While Bayesian
arXiv:2602.01335v2 Announce Type: replace Abstract: A visual metaphor constitutes a high-order form of human creativity, employing cross-domain semantic fusion to transform abstract concepts into impa
arXiv:2608.27774v1 Announce Type: cross Abstract: Efficiently and robustly analyzing shape data is critical across many scientific disciplines. While chirality is a fundamental property in numerous ap
arXiv:2608.27549v1 Announce Type: new Abstract: Physical understanding and reasoning depend on forming compact and generalizable representations of the world. While modern vision-language models can r
arXiv:2608.27726v1 Announce Type: new Abstract: Multi-agent motion planning for high-degree-of-freedom robotics manipulators in shared workspaces remains a fundamental yet challenging problem. Central
Debian voted to allow developers to use AI tools in their contributions to the Linux distribution's 'development, maintenance, [and] documentation.' The new policy on AI acknowledges that 'responsible
arXiv:2206.09012v4 Announce Type: replace-cross Abstract: We consider the problem of inferring high-dimensional data mathbf{x} in a model that consists of a prior p(mathbf{x}) and an auxiliary differe
arXiv:2608.27685v1 Announce Type: new Abstract: Simultaneously optimizing the trajectories of multiple agents is a challenging problem plagued by nonlinearity, nonconvexity, and the curse of dimension
arXiv:2602.18154v3 Announce Type: replace Abstract: Jailbreaking poses a significant risk to the deployment of Large Language Models (LLMs) and Vision Language Models (VLMs). VLMs are particularly vul
arXiv:2608.28155v1 Announce Type: new Abstract: Professional financial examinations require models to combine domain knowledge, calculation, and judgment, yet no benchmark covers the full CFA and FRM
arXiv:2608.28288v1 Announce Type: new Abstract: Existing feed-forward 3D reconstruction methods typically process a bounded number of images and recover cameras and geometry in local or internally nor
arXiv:2305.19366v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied to the p
arXiv:2608.28429v1 Announce Type: new Abstract: Event cameras generate asynchronous, sparse data streams with microsecond temporal resolution, but in moderate-to-high motion scenes they can produce as
arXiv:2608.27483v1 Announce Type: cross Abstract: Objective: Recent studies demonstrate that functional connectomes contain subject-specific signatures, or extit{fingerprints}, that can identify indiv
arXiv:2608.27794v1 Announce Type: new Abstract: As neural networks are increasingly deployed on resource constrained edge devices, accurate prediction of latency and energy is critical for efficient n
arXiv:2608.28272v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, yet its high bitrate requirements pose significant challenges
arXiv:2411.10997v2 Announce Type: replace-cross Abstract: We consider the problem of learning models of spatial density functions, representing the steady-state density of mobile nodes moving on a two
arXiv:2608.28406v1 Announce Type: new Abstract: Human mistakes are inevitable when following instructions, yet they can lead to severe consequences. As such, there has been an increased interest in de
arXiv:2608.28214v1 Announce Type: new Abstract: Estimating environmental states with multi-robot systems becomes particularly challenging when robots are equipped with uncalibrated and therefore heter
arXiv:2608.28589v1 Announce Type: new Abstract: We propose QGPINNs, a physics-informed neural network framework developed in PyTorch for the numerical solution of nonlocal differential equations on qu
arXiv:2608.27626v1 Announce Type: cross Abstract: Quantum DeepONet accelerates neural-operator inference by evaluating an orthogonally parameterized network on a quantum computer, reproducing in ideal
arXiv:2507.12297v2 Announce Type: replace-cross Abstract: Multi-sensorial media systems, including AR/VR, remote operation, and embodied AI, require visual grounding modules that remain reliable as se
arXiv:2608.28267v1 Announce Type: new Abstract: Randomized neural networks enable fast and analytically tractable training by fixing the input to hidden layer parameters at random and learning the out
NVIDIA Omniverse NuRec reconstructs real‑world driving scenes through 3D Gaussian splatting to produce the Physical AI NuRec Dataset—over 1,500 neural‑reconstructed videos (~20 s each) captured from s
arXiv:2608.28262v1 Announce Type: new Abstract: Entropic optimal transport (EOT) has been shown to offer a computationally tractable approximation to exact optimal transport. However, the standard Sin
arXiv:2511.14684v2 Announce Type: replace Abstract: Large language models (LLMs) often make reasoning errors when solving mathematical problems, and how to automatically detect and correct these error
As I've been saying for months, we are truly not ready for persistent agents. 3 comments I want to make about this article: 1) We don't have the full picture It's a great summary of what happened in t
arXiv:2608.26883v1 Announce Type: new Abstract: Robotic grippers face substantial challenges in grasping and manipulating thin objects. Most existing grippers rely on highly precise approach and grasp
arXiv:2608.26198v1 Announce Type: new Abstract: Operating a scientific instrument such as an atomic force microscope (AFM) requires continuous expert decision-making. A trained user defines the experi
arXiv:2608.26309v1 Announce Type: new Abstract: Modern machine learning models rely on large amounts of labeled data. However, manual annotation of large-scale datasets is expensive and time-consuming
arXiv:2608.26375v1 Announce Type: cross Abstract: Standard machine-learning training minimizes a loss function over a dataset, but does not guarantee that the resulting model will satisfy predefined r
arXiv:2608.26114v1 Announce Type: new Abstract: Calculation-intensive financial question answering requires exact reasoning over structured rates, temporal conditions, numerical formulas, and rule-bas
arXiv:2608.27122v1 Announce Type: new Abstract: Directed graph learning naturally leads to trainable nonsymmetric propagation operators with distinct right and left spectral structures. Building on th
NVIDIA TensorRT Model Connect delivers a set of reference implementations for deploying open‑source models from Hugging Face checkpoints to native C++ inference in two steps: a Python CLI builds a dep
arXiv:2608.27034v1 Announce Type: new Abstract: This paper proposes a fully differentiable jitter correction method for X-ray phase-contrast micro computed tomography using a deep learning-based image
arXiv:2608.26546v1 Announce Type: new Abstract: Autonomous agents are increasingly adopted to complete complex, multi-tool workflows in real-world settings. However, existing benchmarks typically sepa
arXiv:2608.27256v1 Announce Type: cross Abstract: Operator learning in scientific machine learning is concerned with approximation of maps between infinite-dimensional function spaces; such maps frequ
arXiv:2608.26154v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly being integrated into digital health platforms to generate summaries of complex medical data. Although t