Knowledge catalogue
arXiv:2604.27562v1 Announce Type: new Abstract: This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graph
arXiv:2602.13559v2 Announce Type: replace Abstract: To fulfill user instructions, autonomous web agents must contend with the inherent complexity and volatile nature of real-world websites. Convention
arXiv:2412.16720v2 Announce Type: replace Abstract: The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provi
arXiv:2604.27118v1 Announce Type: cross Abstract: We present a priority-aware intelligent lane change advisory system based on multi-agent federated reinforcement learning, namely PALCAS, for autonomo
arXiv:2410.15272v3 Announce Type: replace-cross Abstract: Quantum annealers offer a promising hardware platform for solving combinatorial optimization problems, especially those formulated as Quadrati
arXiv:2507.14719v2 Announce Type: replace Abstract: As large language models (LLMs) become increasingly integrated into real-world applications, scalable and rigorous safety evaluation is essential. T
arXiv:2604.27633v1 Announce Type: new Abstract: Large language models (LLMs) are commonly evaluated for political bias based on their responses to fixed questionnaires, which typically place frontier
arXiv:2604.27182v1 Announce Type: cross Abstract: Time-series data augmentation plays a crucial role in regression-oriented forecasting tasks, where limited data restricts the performance of deep lear
arXiv:2604.27193v1 Announce Type: new Abstract: Automatic Emergency Braking (AEB) systems represent a safety-critical national interest, with the National Highway Traffic Safety Administration (NHTSA)
arXiv:2604.27552v1 Announce Type: new Abstract: While 3D Gaussian splatting (3DGS) offers explicit and efficient scene representations for cone-beam computed tomography reconstruction, conventional ph
arXiv:2604.28056v1 Announce Type: new Abstract: Large language models (LLMs) make reward design in reinforcement learning substantially more scalable, but generated rewards are not automatically relia
arXiv:2604.27621v1 Announce Type: cross Abstract: A critical bottleneck hindering further advancement in embodied AI and robotics is the challenge of scaling robot data. To address this, the field of
arXiv:2604.27358v1 Announce Type: new Abstract: As large language model (LLM) agents are deployed in high-stakes environments, the question of how safely to delegate subtasks to specialized sub-agents
Sam Altman is a master at insincerity, Author @GaryMarcus claims. 'He is a master of projecting insincerity, a master at telling the room what it wants to hear and not always truthful'. “Altman, sitti
arXiv:2602.20549v2 Announce Type: replace-cross Abstract: The choice of prior is central to solving ill-posed imaging inverse problems, making it essential to select one consistent with the measuremen
SCOOP: A pro-AI dark money group backed by a powerful super PAC funded by execs tied to Palantir and OpenAI, has been secretly paying influencers to push pro-AI, anti-China propaganda on TikTok and IG
arXiv:2604.28048v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used as proxies for human perception in urban analysis, yet it remains unclear whether persona prompting p
Stating that women do not have penises is conservative. Stating that the scientific method is superior to ancestral tribal dances for seeking truth is conservative. Supporting a rational immigration p
arXiv:2602.16516v2 Announce Type: replace Abstract: This paper introduces ParlaCAP, a large-scale dataset for analyzing parliamentary agenda setting across Europe, and proposes a cost-effective method
arXiv:2601.08418v2 Announce Type: replace-cross Abstract: Tax code prediction is a crucial yet underexplored task in automating invoicing and compliance management for large-scale e-commerce platforms
arXiv:2604.27789v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used as core dependencies in software systems. However, the hosted LLM services evolve continuously thro
arXiv:2506.02671v3 Announce Type: replace Abstract: Deep neural networks often suffer performance degradation upon deployment due to distribution shifts. Continual Test-Time Adaptation (CTTA) aims to
arXiv:2604.27953v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) become increasingly integrated into decision-making systems, it is essential to understand how visual inputs influence
arXiv:2604.27168v1 Announce Type: new Abstract: We present the Field of Safe Motion (FSM), a quantitative safety model for determining whether a driver maintains a collision-free escape route, or 'out
arXiv:2604.27282v1 Announce Type: cross Abstract: Pretrial risk assessment tools are used on over one million U.S. defendants each year, yet their use for predicting rare violent re-offense faces a ba
arXiv:2604.27292v1 Announce Type: new Abstract: Every system that performs effects has two boundaries: what it can do (expressiveness) and what governance covers (governance). In nearly all deployed A
Gary Marcus critiques the AI industry's focus on scaling model parameters and training data (tokenmaxxing) as an inefficient approach to advancing AI capabilities. He argues that simply increasing tok
arXiv:2601.20239v4 Announce Type: replace Abstract: Fine-grained and contact-rich manipulation remain challenging for robots, largely due to the underutilization of tactile feedback. To address this,
arXiv:2604.28087v1 Announce Type: cross Abstract: Rule-based systems remain central in safety-critical domains but often struggle with scalability, brittleness, and goal misspecification. These limita
arXiv:2604.27132v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) and Multi-Agent Systems (MAS) in high-stakes domains demand reliable verification, yet centralized approaches suffer four
arXiv:2506.05412v3 Announce Type: replace-cross Abstract: Where someone looks is a nonverbal communication cue that children and adults readily use. How well can Vision-Language Models (VLMs) infer ga
arXiv:2604.27656v1 Announce Type: cross Abstract: To preserve previously learned representations, continual learning systems must strike a balance between plasticity, the ability to acquire new knowle
who's ready to dance? 😆🕺🎶 uploaded 11 new favorite @suno songs to @spotify [search: 'captain yohei'] 💿 Captain Yohei Vol 1. 💿 ♫ Don't Go in the Office [hard rock] ♫ Nine-Nine-Six [hip-hop/rap] ♫ Fog F
arXiv:2604.26379v1 Announce Type: new Abstract: Reliable seizure detection in mouse models is essential for preclinical epilepsy research, yet manual review of synchronized video-EEG recordings is lab
arXiv:2604.25963v1 Announce Type: new Abstract: Vehicle platooning has attracted increasing attention as a promising approach to improve traffic efficiency, energy consumption, and roadway safety thro
arXiv:2604.25933v1 Announce Type: cross Abstract: As large language models (LLMs) increasingly generate and process clinical text, scalable evaluation has become critical. LLM-as-a-Judge (LaaJ), which
arXiv:2510.08049v3 Announce Type: replace-cross Abstract: Although Large Language Models (LLMs) exhibit advanced reasoning ability, conventional alignment remains largely dominated by outcome reward m
arXiv:2505.17342v2 Announce Type: replace Abstract: Safe Reinforcement Learning (SafeRL) is the subfield of reinforcement learning that explicitly deals with safety constraints during the learning and
arXiv:2505.10924v4 Announce Type: replace-cross Abstract: Recently, AI-driven interactions with computing devices have advanced from basic prototype tools to sophisticated, LLM-based systems that emul
arXiv:2604.26779v1 Announce Type: cross Abstract: RL post-training of frontier language models is increasingly bottlenecked by autoregressive rollout generation, making rollout acceleration a central
arXiv:2604.25965v1 Announce Type: cross Abstract: Deep learning models are widely deployed in safety-critical domains, but remain vulnerable to adversarial attacks. In this paper, we study the adversa
alignment in 2016: obviously any real AI will be made inside a faraday cage magnetically suspended in a 10×10×10 cube of telekill alloy alignment in 2026: yeah we can not make it stop talking about go
arXiv:2604.26637v1 Announce Type: cross Abstract: Annotating long-horizon robotic demonstrations with precise temporal action boundaries is crucial for training and evaluating action segmentation and
arXiv:2604.26689v1 Announce Type: cross Abstract: Skill libraries in deployed robotic systems are continually updated through fine-tuning, fresh demonstrations, or domain adaptation, yet existing type
arXiv:2604.26453v1 Announce Type: new Abstract: Audio-visual deepfakes have reached a level of realism that makes perceptual detection unreliable, threatening media integrity and biometric security. W
arXiv:2604.26577v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly considered for deployment as the control component of robotic health attendants, yet their safety in this
arXiv:2604.26250v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) have achieved state-of-the-art performance in general visual tasks, their perceptual robustness remains remarkably b
Big Tech companies are projected to spend approximately $700 billion on AI infrastructure and development in the current year, a figure that AI researcher and entrepreneur Gary Marcus has criticized a
arXiv:2604.26312v1 Announce Type: new Abstract: Public opinion towards the Free Nutritious Meal Program (MBG) on YouTube social media reflects diverse community responses. This study applies the Long
arXiv:2604.26172v1 Announce Type: cross Abstract: We develop a physics-informed learning framework for energy-shaping control of port-Hamiltonian (pH) systems from trajectory data. The proposed approa
arXiv:2512.08982v2 Announce Type: replace-cross Abstract: Retinex-based low-light image enhancement benefits from separating reflectance and illumination, yet recent generative approaches often rely o
arXiv:2604.26024v1 Announce Type: cross Abstract: Class-level evaluation can conceal substantial performance disparities across subconcepts within the same class, causing models that perform well on a
arXiv:2506.13116v2 Announce Type: replace-cross Abstract: Crime hotspot prediction is critical for ensuring urban safety and effective law enforcement, it remains challenging due to complex spatial de
This post discusses the liability and accountability questions that arise when problems occur in AI-automated workplaces, exploring who bears responsibility—whether the AI developer, the employer, the
arXiv:2604.26494v1 Announce Type: cross Abstract: Generative AI tools are widely used by youth and have introduced new privacy and safety challenges. While prior research has explored youth's safety i
arXiv:2401.02458v3 Announce Type: replace-cross Abstract: The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. Th
arXiv:2604.03905v2 Announce Type: replace-cross Abstract: Heterogeneity is a defining feature of deployed multi-robot teams: platforms often differ in sensing modalities, ranges, fields of view, and f
Dear @elonmusk, If you still genuinely care about AI safety, you can’t let the Trump administration leave the AI industry almost entirely unregulated. You just can’t. - Gary The judge just instructed
arXiv:2604.26503v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success in synthesizing complex static and temporal visuals, a breakthrough largely driven by Classifier-Free
arXiv:2604.26256v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time --