Knowledge catalogue
People ask me what my AI agent team does. The reality is that it is incredibly horizontal, incredibly open and flexible, and fully driven by a flywheel of verbalized, documented, and reviewed goals-co
arXiv:2605.01945v1 Announce Type: new Abstract: Tandem mass spectrometry provides a high-throughput framework for identifying and quantifying proteins in complex biological samples. In computational p
arXiv:2605.02730v1 Announce Type: new Abstract: Despite the success of Large-Vision Language Models (LVLMs), general optimization objectives (e.g., standard MLE) fail to constrain visual trajectories,
This Databricks blog post likely discusses how predictive analytics and machine learning can improve insurance payouts and risk assessment in volatile market conditions. The article probably covers te
Perplexity and Computer now connect to premium health sources, starting with NEJM and BMJ Group, with 9 more medical journals and clinical databases on the way. Ask health questions and get answers ci
arXiv:2605.02143v1 Announce Type: new Abstract: Personalized federated learning (pFL) aims to adapt models to client specific data distributions, yet it often fails to reliably preserve personalized i
arXiv:2605.01632v1 Announce Type: new Abstract: Models that are indistinguishable on in-distribution data can behave very differently under distribution shift. We introduce Perturb-and-Correct (P&C),
arXiv:2605.02236v1 Announce Type: cross Abstract: Recursive language-model loops often settle into recognizable attractor-like patterns. The practical question is how much injected text is needed to m
arXiv:2605.01185v1 Announce Type: new Abstract: Accelerated magnetic resonance imaging (MRI) enabled by the training of deep learning (DL)-based image recon. models requires large and diverse raw k-sp
arXiv:2605.00929v1 Announce Type: new Abstract: Multivariate time series anomaly detection in ICS has attracted growing attention due to the increasing threat of cyber-physical attacks on critical inf
arXiv:2512.07578v3 Announce Type: replace-cross Abstract: Global SHAP explanations are typically presented as feature-importance rankings, which identify variables that matter to a black-box model but
arXiv:2605.01948v1 Announce Type: new Abstract: Collecting diverse, high-quality manipulation data for Vision-Language-Action (VLA) model training remains prohibitively expensive for many research gro
arXiv:2605.02524v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have recently emerged as a promising framework for integrating data-driven learning with physical knowledge. In
arXiv:2605.00973v1 Announce Type: new Abstract: Biosignals acquired from different locations on the body often provide temporally ordered views of the same underlying physiological process. However, m
arXiv:2605.01003v1 Announce Type: cross Abstract: Statistical change point (CP) detection methods typically rely on likelihood-based inference and ignore contextual information about plausible CP loca
arXiv:2603.26816v2 Announce Type: replace Abstract: High-dimensional low-sample-size (HDLSS) datasets constrain reliable environmental model development, where labeled data remain sparse. Reinforcemen
Pinecone launched a new cloud region in Frankfurt, Germany to expand its vector database infrastructure in Europe and better serve Central European enterprises building AI applications. This regional
Pinecone Marketplace, live today. Until now, building an internal app that answers questions over a company's own docs, contracts, tickets, and wikis meant months of retrieval pipeline work: embedding
arXiv:2605.02863v1 Announce Type: new Abstract: Traditional image quality assessment (IQA) methods rely on mean opinion scores (MOS), which are resource-intensive to collect and fail to provide interp
arXiv:2601.00678v2 Announce Type: replace Abstract: Humans excel at forecasting the future dynamics of a scene given just a single image. Video generation models that can mimic this ability are an ess
arXiv:2605.02168v1 Announce Type: cross Abstract: Language model (LM)-based agents have demonstrated promising capabilities in automating complex tasks from natural language instructions, yet they con
arXiv:2605.01759v1 Announce Type: new Abstract: Scene-level point cloud self-supervised learning (PC-SSL) has demonstrated potential in enhancing the generalization capability of 3D vision models. Des
arXiv:2605.00834v1 Announce Type: new Abstract: The algebraic diversity framework replaces temporal averaging over multiple observations with algebraic group action on a single observation for second-
arXiv:2512.05525v2 Announce Type: replace-cross Abstract: Businesses increasingly rely on large language models (LLMs) to automate simple repetitive tasks instead of developing custom machine learning
arXiv:2605.00942v1 Announce Type: cross Abstract: Developing effective test cases capable of thoroughly exercising large-scale software systems is inherently difficult, especially if such systems have
arXiv:2605.01669v1 Announce Type: cross Abstract: External priors of unknown reliability create a brittle trade-off in causal discovery: blind trust amplifies errors, blind rejection wastes signal. Re
arXiv:2605.02358v1 Announce Type: new Abstract: Current models for predicting social media virality rely heavily on static textual and structural features, effectively ignoring the highly dynamic natu
arXiv:2605.01640v1 Announce Type: cross Abstract: Training compute is increasingly outpacing the availability of high-quality data. This shifts the central challenge from optimal compute allocation to
arXiv:2602.11543v2 Announce Type: replace Abstract: Pretraining large language models (LLMs) typically requires centralized clusters with thousands of high-memory GPUs (e.g., H100/A100). Recent decent
arXiv:2605.02500v1 Announce Type: new Abstract: Sleep foundation models have recently demonstrated strong performance on in-domain polysomnography tasks, including sleep staging, apnea detection, and
Sam Altman expresses enthusiasm about the advancement of voice AI models and their potential to improve human-computer interaction. He notes the emerging trend of users already adapting their communic
arXiv:2605.01625v1 Announce Type: new Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial
arXiv:2605.02050v1 Announce Type: cross Abstract: This work establishes a foundational framework for standardizing AI evaluation RCTs (sometimes called human uplift studies). Drawing on established ex
arXiv:2512.00748v2 Announce Type: replace Abstract: Lesion segmentation is inherently influenced by imaging uncertainty, arising from ill-defined lesion boundaries and inter-observer variability in di
arXiv:2605.01699v1 Announce Type: new Abstract: Recent attacks show that behavioural unlearning of large language models leaves internal traces recoverable by adversarial probes. We characterise where
arXiv:2605.01746v1 Announce Type: new Abstract: Single-image 3D face reconstruction is a core problem in computer vision, with important clinical applications such as cephalometric landmark analysis i
arXiv:2512.06938v2 Announce Type: replace Abstract: Modern neural language models achieve high accuracy in text generation, yet precise control over generation length remains underdeveloped. In this p
arXiv:2605.02144v1 Announce Type: new Abstract: Self-attention in Transformers is typically implemented as softmax(QK^op/sqrt{d})V, where Q=XW_Q, K=XW_K, and V=XW_V are learned linear projections of t
arXiv:2605.02703v1 Announce Type: cross Abstract: Effective pair programming depends on coordination of attention, cognitive effort, and joint regulation over time, yet most adaptive learning systems
arXiv:2605.01630v1 Announce Type: new Abstract: Rankings produced by holistic LLM-as-a-judge scoring are sensitive to the bias of the chosen judge model. We show that switching to binary rubric scorin
arXiv:2605.01513v1 Announce Type: new Abstract: Designing therapeutic messenger RNA (mRNA) requires creating full-length transcripts that carefully balance stability, translation efficiency, and immun
arXiv:2605.01971v1 Announce Type: new Abstract: Self-supervised learning methods learn high-quality visual representations, yet recent studies show that these representations often capture demographic
Proud of my analyst, Hermes This is what he has achieved after 1.5+ months of internship with me - Got me off X (doomscrolling cut from 2 hrs → 30 mins) - Practically paid for his salary via PMs b
arXiv:2511.07372v3 Announce Type: replace Abstract: Recent curriculum techniques in the post-training stage of LLMs have been empirically observed to outperform non-curriculum approaches in improving
arXiv:2601.16873v2 Announce Type: replace Abstract: We study the problem of learning Transformer-based sequence models with black-box access to their outputs. In this setting, a learner may adaptively
arXiv:2405.10271v4 Announce Type: replace Abstract: The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large mode
arXiv:2605.01017v1 Announce Type: new Abstract: We introduce Xiaohongshu Social Comparison Reader Elicitation (XHS-SCoRE), a reader-grounded benchmark for detecting if a text-only Xiaohongshu (RedNote
The agentic era is here, and the public sector is at the forefront of leading this transformation.At Google Cloud Next ’26, it was clear that academia and public sector organizations are no longer jus
arXiv:2605.02720v1 Announce Type: cross Abstract: Vision-language models hold considerable promise for ophthalmology, but their development depends on large-scale, high-quality image-text datasets tha
arXiv:2602.04787v2 Announce Type: replace-cross Abstract: We introduce PuppetAI, a modular soft robot interaction platform. This platform offers a scalable cable-driven actuation system and a customiz
arXiv:2511.07328v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and
arXiv:2605.01862v1 Announce Type: new Abstract: Offline goal-conditioned RL (GCRL) learns goal-reaching policies from static datasets, but real-world datasets are often partially observable and histor
arXiv:2602.02958v4 Announce Type: replace Abstract: Despite rapid progress in autoregressive video diffusion, an emerging system algorithm bottleneck limits both deployability and generation capabilit
arXiv:2605.01168v1 Announce Type: new Abstract: It is increasingly recognized that human annotators do not always agree, and such disagreement is inherent in many annotation tasks. However, not all in
arXiv:2605.01424v1 Announce Type: new Abstract: Multimodal learning leverages the integration of diverse data modalities to enhance performance in complex tasks. Yet, it frequently encounters incomple
IBM researchers demonstrated a quantum-centric supercomputing approach that successfully simulated a protein containing 12,635 atoms in collaboration with Cleveland Clinic and RIKEN, combining classic
arXiv:2404.11277v2 Announce Type: replace-cross Abstract: In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for ind
Netherlands-based quantum computing hardware company QuantWare B.V. said today it has raised 178 million in funding to accelerate its plans of creating an “open architecture” for the industry. The rou
arXiv:2605.01395v1 Announce Type: cross Abstract: In this paper, we design feedback control laws for soft robots modelled using the Cosserat rod, which is spatially discretised using the Piecewise Con
arXiv:2605.01467v1 Announce Type: cross Abstract: Tensor completion has emerged as a powerful framework for recovering missing data in multidimensional signals by exploiting low-rank tensor structures