Knowledge catalogue
arXiv:2605.01330v1 Announce Type: new Abstract: Low-bit quantization is a practical route for efficiently deploying vision Transformers, yet activation outliers complicate fully quantized deployment.
arXiv:2601.04376v3 Announce Type: replace Abstract: Reliable stress recognition is critical in applications such as medical monitoring and safety-critical systems, including real-world driving. While
arXiv:2605.02159v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) has delivered strong results in domains such as Atari and Go, but it still suffers from high sample cost and weak tran
arXiv:2605.01048v1 Announce Type: new Abstract: Counterfactual prompting (i.e., perturbing a single factor and measuring output change) is widely used to evaluate things like LLM bias and CoT faithful
arXiv:2605.00071v1 Announce Type: cross Abstract: Agentic payment systems extend delegated action to financial transfers, but scaling them on stablecoin rails in regulated settings requires safeguards
arXiv:2509.00673v2 Announce Type: replace Abstract: We investigate the efficacy of Large Language Models (LLMs) in detecting implicit and explicit hate speech, examining how models with minimal safety
arXiv:2505.19607v2 Announce Type: replace Abstract: Test-time adaptation (TTA) enhances model robustness by enabling adaptation to target distributions that differ from training distributions, improvi
arXiv:2605.01309v1 Announce Type: new Abstract: Long-tailed distributions are common in real-world recognition tasks, where a few head classes have many samples while most tail classes have very few.
arXiv:2605.01485v1 Announce Type: new Abstract: Autonomous vehicles (AVs) are widely known to follow conservative, rule-based motion policies that surrounding drivers can learn to anticipate. A direct
arXiv:2603.15013v2 Announce Type: replace Abstract: Autonomous bicycles offer a promising agile solution for urban mobility and last-mile logistics. However, conventional control strategies often stru
arXiv:2605.01468v1 Announce Type: new Abstract: Long-tailed data bias decision boundaries toward head classes and degrade tail class accuracy. Diffusion-based generative augmentation address this prob
arXiv:2603.16969v2 Announce Type: replace-cross Abstract: This paper presents DeepStage, a deep reinforcement learning (DRL) framework for adaptive and stage-aware defense against Advanced Persistent
arXiv:2604.03641v2 Announce Type: replace Abstract: Reinforcement learning in real-world systems often involves delayed feedback, which breaks the Markov assumption and impedes both learning and contr
arXiv:2112.11279v4 Announce Type: replace Abstract: With AI systems widely applied to assist humans in decision-making processes such as talent hiring, school admission, and loan approval; there is an
arXiv:2605.01113v1 Announce Type: new Abstract: Text-to-image (T2I) diffusion models have the ability to build high-quality pictures from text prompts, but they pose safety concerns because they can g
arXiv:2605.01896v1 Announce Type: new Abstract: Emerging multi-modal world models attempt to jointly generate videos across diverse modalities (e.g., RGB, depth, and mask), yet they fail to fully expl
arXiv:2605.01846v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to generate multiple-choice questions (MCQs), where correct answers should ideally be uniformly distr
arXiv:2605.02060v1 Announce Type: new Abstract: Dimensionality reduction methods such as t-SNE are designed to preserve local neighborhood structure but do not explicitly account for how probability m
arXiv:2605.01227v1 Announce Type: new Abstract: Quadrupedal locomotion plays a critical role in enabling agile, versatile movement across complex terrains. Understanding and estimating the underlying
arXiv:2605.01516v1 Announce Type: new Abstract: Robust control policy learning for autonomous driving requires training environments to be both physically realistic and computationally scalable, prope
arXiv:2410.08491v2 Announce Type: replace Abstract: Automated vehicles promise to enhance transportation safety and efficiency. However, ensuring their reliability in real-world conditions remains cha
Elon: Let’s settle. Greg: Nope. Elon: Ok, let’s talk about your diaries, then. On the eve of trial, Elon Musk reached out to Greg Brockman about a potential settlement, according to court documents fi
arXiv:2605.01432v1 Announce Type: new Abstract: Autonomous landing in cluttered or unstructured environments remains a safety-critical challenge for unmanned aerial vehicles (UAVs), particularly under
arXiv:2605.02165v1 Announce Type: new Abstract: Conventional federated learning assumes that greater learner participation improves training performance, by leveraging abundant, independently generate
arXiv:2605.01929v1 Announce Type: new Abstract: Video diffusion models leveraging step distillation or causal distillation have achieved remarkable performance. However, adapting existing LoRAs to the
arXiv:2605.01706v1 Announce Type: new Abstract: Active learning (AL) has emerged as a crucial strategy for reducing the prohibitive costs associated with medical image segmentation. However, standard
arXiv:2605.01266v1 Announce Type: new Abstract: Zero-shot vision-language models (VLMs) offer a promptable alternative to task-specific training for gross tumor volume (GTV) delineation in non-small-c
arXiv:2605.02464v1 Announce Type: new Abstract: Single-image HDR reconstruction aims to recover high dynamic range radiance from a single low dynamic range (LDR) input, but remains highly ill-posed du
arXiv:2603.12612v2 Announce Type: replace Abstract: Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a fundamental challenge, as the ''curse of dimensio
arXiv:2505.06831v2 Announce Type: replace Abstract: Achieving group-robust generalization in the presence of spurious correlations remains a significant challenge, particularly when bias annotations a
arXiv:2605.02137v1 Announce Type: new Abstract: Accurate flood water mapping is critical for disaster management, yet current methods struggle to fully exploit the potential of spaceborne imagery. Opt
Folks, AFAIK this is *literally not a possible outcome of the trial*, as Elon has waived the right to any cash payment to him, instead assigning any monetary damages to OpenAI’s nonprofit. @PursueOpti
arXiv:2605.02740v1 Announce Type: cross Abstract: Evidence derived from large-scale real-world data (RWD) is increasingly informing regulatory evaluation and healthcare decision-making. Administrative
arXiv:2605.01995v1 Announce Type: new Abstract: The perception of an Autonomous Driving System (ADS) critically depends on relevant, comprehensive, and diverse datasets to ensure its safety while oper
arXiv:2511.03828v2 Announce Type: replace Abstract: Offline-to-online reinforcement learning (O2O RL) faces a central challenge between retaining offline conservatism and adapting to online feedback u
arXiv:2410.16636v2 Announce Type: replace-cross Abstract: We study the problem of conditional two-sample testing, which aims to determine whether two populations have the same distribution after accou
arXiv:2605.02435v1 Announce Type: new Abstract: The Distributional Alignment Game framework provides a powerful variational perspective on Answer-Level Fine-Tuning (ALFT). However, standard algorithms
arXiv:2605.02108v1 Announce Type: new Abstract: Deep residual architectures are modeled as products of near-identity Jacobians. This paper proves deterministic quotient-geometric estimates for singula
arXiv:2605.02111v1 Announce Type: new Abstract: This paper develops the angular and static-channel component of Geometric and Spectral Alignment for residual Jacobian chains. Starting from Cartan-coor
arXiv:2605.02086v1 Announce Type: new Abstract: 3D Gaussian splatting (3DGS) is a state-of-the-art representation for real-time photorealistic novel-view synthesis, yet a single high-fidelity scene ty
arXiv:2605.01529v1 Announce Type: new Abstract: Imitation learning offers a promising framework for enabling robots to acquire diverse skills from human users. However, most imitation learning algorit
arXiv:2605.02626v1 Announce Type: new Abstract: Preference optimization has become a central paradigm for aligning large language models with human feedback. Direct Preference Optimization (DPO) simpl
arXiv:2507.21153v2 Announce Type: replace Abstract: The exponential growth of digital services has positioned data centers among the most energy-intensive infrastructures in the modern economy, raisin
arXiv:2603.12243v3 Announce Type: replace Abstract: Mastering dexterous manipulation with multi-fingered hands has been a grand challenge in robotics for decades. Despite its potential, the difficulty
arXiv:2603.03584v2 Announce Type: replace Abstract: Maintaining situational awareness in complex driving scenarios is challenging. It requires continuously prioritizing attention among extensive scene
arXiv:2508.12778v2 Announce Type: replace Abstract: Medical large vision-language Models (Med-LVLMs) have shown promise in clinical applications but suffer from factual inaccuracies and unreliable out
arXiv:2511.22581v3 Announce Type: replace Abstract: We prove that in any Dec-POMDP, sufficiently high entropy regularization ensures that the policy gradient flow with tabular softmax parametrization
arXiv:2605.02007v1 Announce Type: cross Abstract: In recent years, several advances have been observed in Deep Learning with surprising results. Models in this area have been increasingly used in nume
arXiv:2605.02784v1 Announce Type: new Abstract: Accurately recovering human pose and appearance from video is an essential component of scene reconstruction, with applications to motion capture, motio
arXiv:2605.01574v1 Announce Type: new Abstract: Vehicle Routing Problem (VRP) is one of the most complex NP-hard combinatorial optimization problem in transportation and logistics that requires a dyna
arXiv:2605.01581v1 Announce Type: new Abstract: Diffusion-based visuomotor policies perform well in robotic manipulation, yet current methods still inherit image-generation-style decoders and multi-st
arXiv:2605.02580v1 Announce Type: new Abstract: Recognizing unknown objects is crucial for safety-critical applications such as autonomous driving and robotics. Open-Set Panoptic Segmentation (OPS) ai
Gary Marcus reflects on changing public perception of Sam Altman, noting that skepticism toward Altman's claims was once dismissed as hatred but has become more mainstream. The post suggests a shift i
I predict this will be the most embarrassing cover in the history of this magazine. This is like putting Enron executives on your cover, or doing a gauzy special report on Bernie Madoff. Sam Altman is
arXiv:2510.25426v2 Announce Type: replace Abstract: The rapid advancement of Large Language Models (LLMs) is positioning language at the core of human-computer interaction (HCI). We argue that advanci
Important new development. AI company employees have an enormous amount of power — far more than they realize. Absent legislation, AI co worker power is one of the key levers to shaping what the indus
arXiv:2605.02544v1 Announce Type: cross Abstract: The widespread adoption of machine learning in critical applications demands techniques to mitigate high-consequence errors. Our method utilizes a dua
In vividly explaining how he deceived Musk about his commitment to the nonprofit, without a trace of remorse, OpenAI’s Brockman has done Musk’s counsel quite a service. Brockman’s also been pretty gre
arXiv:2605.01402v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) struggle with numerical regression under long-tailed target distributions. Token-level supervised fine-tuning (
arXiv:2601.06035v2 Announce Type: replace-cross Abstract: The release of SAM 3D Body is a recent development in human mesh recovery, demonstrating improved performance in producing clean, topologicall