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arXiv:2509.23986v2 Announce Type: replace Abstract: Scientific discovery is often slowed by the manual development of computational tools needed to analyze complex experimental data. Building such too

arXiv:2605.16443v1 Announce Type: cross Abstract: Despite the success of deep neural networks in vision, medical diagnosis, and IoT scenarios, their deployment on resource-limited platforms poses seri

two years ago we started building agents to automate work. turns out these are really useful, so there’s a LOT of runs and long traces that are hard to reason about now, use an ambient agent (engine)

arXiv:2605.17942v1 Announce Type: new Abstract: Feed-forward 3D reconstruction has recently demonstrated strong generalization across diverse scenes, yet its performance in UAV imagery remains underex

arXiv:2605.16690v1 Announce Type: new Abstract: Heterogeneous LoRA-rank methods address system heterogeneity in federated fine-tuning of foundation models by assigning client-specific ranks based on c

arXiv:2601.12696v2 Announce Type: replace Abstract: Current guardian models are predominantly Western-centric and optimized for high-resource languages, leaving low-resource African languages vulnerab

arXiv:2605.17140v1 Announce Type: cross Abstract: Brain tumor diagnosis is largely dependent on Magnetic Resonance Imaging (MRI) evaluation, which requires radiologists to synthesize thousands of imag

arXiv:2605.17788v1 Announce Type: cross Abstract: A fundamental challenge in recommender systems is balancing reliability for Low-Active Users (LAUs) with diversity for High-Active Users (HAUs). The k

arXiv:2502.17007v2 Announce Type: replace-cross Abstract: In this paper we argue that, to its detriment, transparency research overlooks many foundational concepts of artificial intelligence. As an il

arXiv:2605.18008v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is critical for safety-critical domains like healthcare, yet it is rarely evaluated under realistic out-of-distribution

arXiv:2506.10959v3 Announce Type: replace-cross Abstract: While in-context learning (ICL) has achieved remarkable success in natural language and vision domains, its theoretical understanding-particul

Throughout 2025, HPE observed significant changes in how cybercriminals operate. Analyzing real-world threats, our HPE Threat Labs highlighted an industrialization of the cyber criminals’ methods in i

arXiv:2605.17575v1 Announce Type: cross Abstract: Network traffic classification (NTC) models often suffer severe performance degradation when deployed in real-world environments due to distribution s

arXiv:2605.16750v1 Announce Type: cross Abstract: Personalized exercise recommendation dynamically aligns pedagogical resources with individual knowledge mastery, which is crucial for satisfying stude

arXiv:2605.18611v1 Announce Type: new Abstract: We propose a unified reinforcement learning framework that enables a single policy to perform walking, running, and fall recovery on the Unitree G1 huma

arXiv:2603.02667v2 Announce Type: replace Abstract: Unifying text-image contrastive learning and text-to-image (T2I) generation in a single end-to-end model is challenging because the two objectives d

arXiv:2605.17356v1 Announce Type: new Abstract: Existing works typically focus on presentation generation under isolated input settings, whereas real-world use cases span diverse scenarios, including

arXiv:2605.17936v1 Announce Type: new Abstract: Recent works have illustrated that modern NLP models trained for diverse tasks ranging from sentiment analysis to language generation succumb to univers

arXiv:2605.16719v1 Announce Type: cross Abstract: Scientific and technological frontiers advance through punctuated dynamics, yet the principles governing these dynamics remain poorly understood. Here

arXiv:2605.16815v1 Announce Type: cross Abstract: Graph neural networks (GNNs) have achieved remarkable success in relational learning. However, their vulnerability to graph backdoor attacks (GBAs) po

arXiv:2509.22459v4 Announce Type: replace-cross Abstract: While achieving exceptional generative quality, modern diffusion, flow, and other matching models suffer from slow inference, as they require

arXiv:2602.19710v2 Announce Type: replace Abstract: Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low training efficiency because they entangle high-level percept

arXiv:2401.03717v4 Announce Type: replace-cross Abstract: Time-series data exists in every corner of real-world systems and services, ranging from satellites in the sky to wearable devices on human bo

arXiv:2504.20734v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external kn

arXiv:2505.16831v3 Announce Type: replace-cross Abstract: Unlearning in large language models (LLMs) aims to remove specified data, but its efficacy is typically assessed with task-level metrics like

arXiv:2605.17976v1 Announce Type: new Abstract: Scientific discovery is increasingly constrained by costly experiments and limited resources, underscoring the need for efficient optimization in AI for

arXiv:2602.22801v2 Announce Type: replace-cross Abstract: Diffusion models have become a popular choice for decision-making tasks in robotics, and more recently, are also being considered for solving

arXiv:2605.17822v1 Announce Type: new Abstract: Infrared object detection is crucial for perception in autonomous driving and surveillance but remains vulnerable to physical adversarial attacks. Unlik

arXiv:2605.17748v1 Announce Type: new Abstract: In the field of Blind Image Quality Assessment (BIQA), accurately predicting the perceptual quality of authentically distorted images remains highly cha

arXiv:2510.13870v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) have emerged as a promising alternative to Autoregressive Language Models, yet their inference strategies rem

arXiv:2605.17285v1 Announce Type: cross Abstract: Node representation learning, such as Graph Neural Networks (GNNs), has emerged as a pivotal method in machine learning. The demand for reliable expla

arXiv:2605.18022v1 Announce Type: cross Abstract: Highly over-parameterized models can simultaneously memorize noisy labels and generalize well, yet how these behaviors coexist remains poorly understo

arXiv:2510.02590v2 Announce Type: replace Abstract: The use of target networks is a popular approach for estimating value functions in deep Reinforcement Learning (RL). While effective, the target net

Use your LM Studio models to code locally in @zeddotdev 🚀 Local model usage grew 3x in Zed's agent in the last 10 weeks. Cameron Mcloughlin on why he prefers local: 'I worry about over-reliance on pro

arXiv:2304.10726v3 Announce Type: replace-cross Abstract: We introduce the Deep Learning Vulnerability Analyzer (DLVA) for Ethereum smart contracts based on neural networks. We train DLVA to judge byt

arXiv:2605.17742v1 Announce Type: new Abstract: Manually annotating accurate 3D hand poses is extremely time-consuming and labor-intensive. Existing self-supervised hand pose estimation methods levera

arXiv:2605.18188v1 Announce Type: new Abstract: Anomaly detection in batch processes is hindered by transient dynamics, scarce fault labels, and reliance on single-modality sensor data. This work intr

arXiv:2605.16306v1 Announce Type: cross Abstract: In industrial design, N-sided hole filling is typically formulated as the construction of a single trimmed B-spline surface by minimizing a fairness e

v0.30.0-rc21 is a pre-release version that changes Ollama's architecture to directly support llama.cpp instead of building on top of GGML, enabling GGUF file format compatibility. MLX is used to accel

arXiv:2510.06809v3 Announce Type: replace Abstract: Echocardiography is a critical tool for detecting heart diseases, yet its steep operational difficulty causes a shortage of skilled personnel. Probe

arXiv:2605.17691v1 Announce Type: cross Abstract: Automating the classification of negative treatment in legal precedent is a critical yet nuanced NLP task where misclassification carries significant

arXiv:2602.10989v2 Announce Type: replace-cross Abstract: We construct and analyze generative diffusions that transport a point mass to a prescribed target distribution over a finite time horizon usin

arXiv:2605.18490v1 Announce Type: new Abstract: We preregistered a comparison of two ways to help an LLM answer questions over a small research corpus: a single-round Vector RAG system and an LLM-comp

arXiv:2602.22941v2 Announce Type: replace Abstract: Pacing strategies, defined by velocity and stroke rate profiles, are essential for peak performance in canoe sprint. While GPS is the gold standard

arXiv:2605.17605v1 Announce Type: new Abstract: Modern generative modeling has grown into a broad collection of related but often separately implemented paradigms, including denoising diffusion models

arXiv:2605.17613v1 Announce Type: cross Abstract: The large size of the KV cache has become a major bottleneck for serving LLMs with increasing context lengths. In response, many KV cache compression