Knowledge catalogue
FDA Commissioner Dr. Marty Makary resigned after a rocky tenure marked by complaints from health industry executives, anti-abortion activists, and vaping lobbyists, stepping down after just over a yea
arXiv:2509.21000v2 Announce Type: replace Abstract: Integer Linear Programs (ILPs) are central to real-world optimizations but notoriously difficult to solve. Learning to Optimize (L2O) has emerged as
arXiv:2605.08119v1 Announce Type: cross Abstract: Tian (2025) proves a repulsion theorem (Theorem 6) for the matrix B = (widetilde{F}^op widetilde{F} + eta I)^{-1} during the interactive feature-learn
arXiv:2605.08149v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) decompose large language model representations into interpretable features, but how these features interact under uncertain
arXiv:2603.16231v2 Announce Type: replace-cross Abstract: Numerical optimal control has long been split between globally structured but dimensionally intractable Hamilton--Jacobi--Bellman (HJB) method
arXiv:2601.22204v2 Announce Type: replace Abstract: Federated learning (FL) encounters substantial challenges due to heterogeneity, leading to gradient noise, client drift, and partial client particip
arXiv:2605.09428v1 Announce Type: new Abstract: Graph-level anomaly detection (GLAD) is crucial for ensuring the reliability of graph-driven applications by identifying abnormal graphs that deviate fr
arXiv:2602.04093v2 Announce Type: replace Abstract: Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept ann
arXiv:2605.09986v1 Announce Type: cross Abstract: Training a language model on data scattered across bandwidth-limited nodes that cannot be centralized is a setting that arises in clinical networks, e
arXiv:2605.08760v1 Announce Type: new Abstract: Federated Learning (FL) facilitates collaborative model training across decentralized clients while preserving data privacy by avoiding raw data exchang
arXiv:2605.09144v1 Announce Type: new Abstract: Sharpness-aware minimization (SAM) is an effective method for improving the generalization of federated learning (FL) by steering local training toward
arXiv:2605.10082v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong reasoning capabilities when guided by high-quality demonstrations, yet such data is often distributed across
arXiv:2605.09750v1 Announce Type: new Abstract: This article presents a novel approach to keyframe detection in ultrasound videos, with a particular focus on fetal brain imaging. The proposed model is
arXiv:2605.08925v1 Announce Type: new Abstract: Interactive segmentation allows efficient label generation by leveraging user-provided clicks to progressively refine predictions, which is critical whe
arXiv:2605.09089v1 Announce Type: cross Abstract: Digital identity verification systems used in remote onboarding rely on document images to authenticate users, making them vulnerable to localized man
arXiv:2605.10439v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a widely used mechanism for customizing diffusion models, enabling users to inject new visual concepts or styles t
arXiv:2605.09106v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in financial contexts, raising critical concerns about reliability, alignment, and susceptibility
arXiv:2602.07126v2 Announce Type: replace Abstract: Synthetic tabular data has gained attention for enabling privacy-preserving data sharing. While substantial progress has been made in single-table s
arXiv:2605.08565v1 Announce Type: new Abstract: Microscaling is a critical technique for preserving the quality of Large Language Models (LLMs) quantized to ultra-low precision formats. Intuitively, f
arXiv:2605.09469v1 Announce Type: new Abstract: This paper explores the use of emojis in financial sentiment analysis, focusing on the social media platform StockTwits. Emojis, increasingly prevalent
arXiv:2502.18834v2 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can
arXiv:2508.09042v3 Announce Type: replace Abstract: The most dangerous mistakes a novice counselor makes are not the obvious ones: they are utterances that sound caring while quietly violating profess
arXiv:2605.09277v1 Announce Type: new Abstract: We revisit combinatorial Thompson sampling (CTS) for semi-bandits with sleeping arms, where arm availability varies over time and actions must satisfy c
arXiv:2605.08894v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance but incur high deployment costs, motivating extremely low-bit but lossy quantization. Existing
arXiv:2605.08657v1 Announce Type: cross Abstract: We study learnable logic gate networks that stack layers of 2-input Boolean gates to build combinational circuits. Every 2-input gate has a unique mul
arXiv:2605.10183v1 Announce Type: new Abstract: Sharpness-Aware Minimization (SAM) improves generalization by minimizing the worst-case loss within a fixed parameter-space radius neighborhood. SAM and
arXiv:2605.10792v1 Announce Type: cross Abstract: We propose an implicit neural formulation of optimal transport that eliminates adversarial min--max optimization and multi-network architectures commo
arXiv:2605.09861v1 Announce Type: cross Abstract: Alignment, the tendency of adjacent weight matrices in deep networks to develop compatible subspace orientations, underlies gradient flow, Neural Coll
arXiv:2605.09355v1 Announce Type: new Abstract: Real-world model deployment across multiple domains requires multimodal models to operate under two complementary regimes: (1) multi-task pretraining, t
arXiv:2605.09218v1 Announce Type: cross Abstract: 3D scene understanding spans reasoning about free space, object grounding, hypothetical object insertions, complex geometric relationships, and integr
arXiv:2605.08594v1 Announce Type: cross Abstract: Systolic arrays are the dominant compute fabric for neural network inference. Prior work has addressed column-level fault detection efficiently with u
arXiv:2605.09430v1 Announce Type: new Abstract: Large-scale autoregressive models have demonstrated remarkable capabilities in image generation. However, their sequential raster-scan decoding relies o
arXiv:2605.09003v1 Announce Type: new Abstract: Recently, diffusion-based object removal models have achieved impressive results in eliminating objects and their associated visual effects. However, th
arXiv:2605.08520v1 Announce Type: new Abstract: LLM-based evolution has emerged as a promising way to improve agents by refining non-parametric artifacts, but its wall-clock cost remains a major bottl
arXiv:2605.08314v1 Announce Type: cross Abstract: SVD-based Low-rank compression reduces transformer parameters and nominal FLOPs, but these savings often translate poorly into real LLM serving speedu
arXiv:2605.08640v1 Announce Type: new Abstract: Plug-and-play (PnP) methods for solving inverse problems have recently achieved strong performance by leveraging denoising priors based on powerful gene
arXiv:2605.08648v1 Announce Type: new Abstract: Many biological systems evolve through continuous local dynamics while switching between latent regimes defined by learning, stimulus context, internal
arXiv:2605.09438v1 Announce Type: new Abstract: Many features in pretrained Transformers span multiple layers: they emerge through stages of inference, persist in the residual stream, or are built joi
arXiv:2605.09932v1 Announce Type: new Abstract: Large language models can now process increasingly long inputs, yet their ability to effectively use information spread across long contexts remains lim
arXiv:2605.10302v1 Announce Type: new Abstract: Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching admits
For a driver born without arms, FSD Supervised is life-changing accessibility “I was born without arms and have driven with my feet my entire life. I’m a fully licensed driver, and traditionally I dro
arXiv:2602.22088v2 Announce Type: replace Abstract: Contact-rich manipulation demands human-like integration of perception and force feedback: vision should guide task progress, while high-frequency i
arXiv:2605.09681v1 Announce Type: new Abstract: Autoregressive (AR) video diffusion models adopt a streaming generation framework, enabling long-horizon video generation with real-time responsiveness,
arXiv:2605.08140v1 Announce Type: cross Abstract: The Karlsruhe Tritium Neutrino Experiment (KATRIN) aims to measure the absolute neutrino mass with unprecedented sensitivity, requiring precise monito
arXiv:2605.10230v1 Announce Type: new Abstract: Molecular optimization seeks to improve a molecule through small structural edits while preserving similarity to the starting compound. Recent language-
arXiv:2605.08905v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success on reasoning benchmarks through Reinforcement Learning with Verifiable Rewards (RLVR), exc
arXiv:2602.16708v3 Announce Type: replace-cross Abstract: Security policy enforcement in contemporary agentic systems predominantly consists of embedding natural-language policies within an agent's sy
arXiv:2605.10474v1 Announce Type: cross Abstract: Analog neural networks are gaining attention due to their efficiency in terms of power consumption and processing speed. However, since analog neural
arXiv:2605.10141v1 Announce Type: new Abstract: Recent neural theorem provers use reinforcement learning with verifiable rewards (RLVR), where proof assistants provide binary correctness signals. Whil
arXiv:2605.09163v1 Announce Type: new Abstract: Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution. This
Found my limit: ~8 simultaneous code generation/review tasks. Beyond that, context-switching turns from leverage into chaos. AI coding doesn’t remove the need for WIP limits. It might even make them m
arXiv:2605.10351v1 Announce Type: new Abstract: Reliable inference requires that artificial intelligence (AI) models provide trustworthy uncertainty estimates, not merely accurate predictions. Recent
Foundry Local 1.1 adds live transcription, embeddings, Responses API, WebGPU plugin, and download cancellation. The post Foundry Local 1.1: Live Transcription, Embeddings, and Responses API appeared f
arXiv:2512.02010v5 Announce Type: replace Abstract: As large language models have grown larger, interest has grown in low-precision numerical formats such as NVFP4 as a way to improve speed and reduce
arXiv:2605.05743v2 Announce Type: replace-cross Abstract: Gaussian process (GP) marginal likelihood scores and kernel conditional independence tests are theoretically appealing for nonlinear causal di
Lily Hay Newman / Wired: Foxconn says some of its North American factories suffered a cyberattack in recent days; ransomware group Nitrogen claims it stole 8TB of data — Famous for helping build Apple
arXiv:2605.09581v1 Announce Type: new Abstract: This paper presents a hardware architecture that implements the Contrast Maximization (CM) algorithm in Field-Programmable Gate Array (FPGA) resources f
arXiv:2605.08324v1 Announce Type: cross Abstract: Diabetic Retinopathy (DR) is a common complication of diabetes that can lead to blindness of people. Detecting DR at the earliest stage is essential t
arXiv:2605.08833v1 Announce Type: new Abstract: Effective sequence modeling fundamentally requires balancing the retention of unbounded history with the high-resolution detection of abrupt short-term
arXiv:2605.08896v1 Announce Type: cross Abstract: Robust adaptation of LLMs and VLMs is often evaluated by average accuracy or average consistency under perturbations. However, these averages can hide