Knowledge catalogue
arXiv:2604.14765v1 Announce Type: new Abstract: We present a geometric framework for Reinforcement Learning (RL) that views policies as maps into the Wasserstein space of action probabilities. First,
Siladitya Ray / Forbes: What some leaders think of using universal income to mitigate AI-fueled layoffs: Musk calls it the “best way”, OpenAI's policy doc mentions a Public Wealth Fund — Topline — Elo
arXiv:2604.15038v1 Announce Type: cross Abstract: The evaluation of fairness in machine learning systems has become a central concern in high-stakes applications, including biometric recognition, heal
arXiv:2604.14333v1 Announce Type: new Abstract: Key Opinion Leader (KOL) discourse on social media is widely consumed as investment guidance, yet turning it into executable trading strategies without
arXiv:2604.15280v1 Announce Type: new Abstract: Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans. Vision-language models (VLMs) have made trem
arXiv:2510.23853v3 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly used to interact with and execute tasks in dynamic environments. However, a critical yet overlook
arXiv:2604.13658v1 Announce Type: new Abstract: Advanced deep learning methods have shown remarkable success in power quality disturbance (PQD) classification. To enhance model transparency, explainab
arXiv:2604.13240v1 Announce Type: new Abstract: Mapping the spatial distribution of species is essential for conservation policy and invasive species management. Species distribution models (SDMs) are
arXiv:2604.13645v1 Announce Type: cross Abstract: Co-training, which combines limited in-domain real-world data with abundant surrogate data such as simulation or cross-embodiment robot data, is widel
arXiv:2604.13057v1 Announce Type: new Abstract: For millions of users in developing economies who depend on mobile banking as their primary gateway to financial services, app quality directly shapes f
A 'race' implies that it is something that is worth 'winning', which is not the case for superintelligence. Whoever finished first is just the first to trigger irrecoverable catastrophe. 'There is no
arXiv:2604.06168v2 Announce Type: replace Abstract: World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model t
arXiv:2508.00555v2 Announce Type: replace-cross Abstract: Jailbreaking is an essential adversarial technique for red-teaming these models to uncover and patch security flaws. However, existing jailbre
Three major advertising companies—Dentsu, Publicis, and WPP—settled with the Federal Trade Commission over allegations they colluded on anti-misinformation policies that reduced ad revenue for conserv
arXiv:2604.13495v1 Announce Type: new Abstract: Alzheimer's disease (AD) progresses heterogeneously across individuals, motivating subject-specific synthesis of follow-up magnetic resonance imaging (M
arXiv:2604.13079v1 Announce Type: cross Abstract: Current AI alignment paradigms rely on behavioral correction: external supervisors (e.g., RLHF) observe outputs, judge against preferences, and adjust
arXiv:2604.13459v1 Announce Type: new Abstract: Turbofan engine degradation under sustained operational stress necessitates robust prognostic systems capable of accurately estimating the Remaining Use
arXiv:2604.13705v1 Announce Type: new Abstract: Fairness in language models is typically studied as a property of a single, centrally optimized model. As large language models become increasingly agen
arXiv:2604.13891v1 Announce Type: new Abstract: Automated driving at unsignalized intersections is challenging due to complex multi-vehicle interactions and the need to balance safety and efficiency.
arXiv:2604.13824v1 Announce Type: new Abstract: World models have been emerging as critical components for assessing the consequences of actions generated by interactive agents in online planning and
arXiv:2604.13305v1 Announce Type: new Abstract: Reward models (RMs) are inherently non-neutral value functions designed and trained to encode specific objectives, such as human preferences or text-ima
arXiv:2604.13253v1 Announce Type: new Abstract: Adaptive Conformal Inference (ACI) provides distribution-free prediction intervals with asymptotic coverage guarantees for time series under distributio
arXiv:2503.06078v2 Announce Type: replace Abstract: Federated learning (FL) has emerged as a promising framework for distributed learning, enabling collaborative model training without sharing private
arXiv:2604.13325v1 Announce Type: new Abstract: Safety filters provide a practical approach for enforcing safety constraints in autonomous systems. While learning-based tools scale to high-dimensional
arXiv:2604.13098v1 Announce Type: cross Abstract: State-of-the-art (SOTA) urban traffic control increasingly employs Multi-Agent Reinforcement Learning (MARL) to coordinate Traffic Light Controllers (
Can people please stop taking shots at @sama, and boycott his company instead? Violence is not the answer. This is really bad. The scary part in the US is that it doesn’t matter whether you are the CE
arXiv:2604.13245v1 Announce Type: new Abstract: Safe navigation for multi-robot systems requires enforcing safety without sacrificing task efficiency under decentralized decision-making. Existing dece
arXiv:2604.13409v1 Announce Type: new Abstract: In clinical practice, the robustness of deep learning models for multimodal brain tumor segmentation is severely compromised by incomplete MRI data. Thi
arXiv:2604.13804v1 Announce Type: new Abstract: The rapid evolution of multimodal large models has revolutionized the simulation of diverse characters in speech dialogue systems, enabling a novel inte
arXiv:2603.27064v2 Announce Type: replace-cross Abstract: Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a ca
arXiv:2604.13561v1 Announce Type: new Abstract: Vision-language models trained with contrastive learning on paired medical images and reports show strong zero-shot diagnostic capabilities, yet the eff
arXiv:2604.13816v1 Announce Type: new Abstract: Determining the number of clusters is a central challenge in unsupervised learning, where ground-truth labels are unavailable. The Silhouette coefficien
arXiv:2604.13883v1 Announce Type: new Abstract: Modern machine learning models typically represent inputs as fixed points in a high-dimensional embedding space. While this approach has been proven pow
arXiv:2510.26491v2 Announce Type: replace Abstract: Data selection is a critical aspect of Reinforcement Learning with Verifiable Rewards (RLVR) for enhancing the reasoning capabilities of large langu
arXiv:2604.13551v1 Announce Type: new Abstract: Entity alignment (EA) aims to identify entities referring to the same real-world object across different knowledge graphs (KGs). Recent approaches based
arXiv:2604.13995v1 Announce Type: new Abstract: This paper introduces a novel approach for image and video orientation estimation by leveraging depth distribution in natural images. The proposed metho
arXiv:2604.13902v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed significant advances in the reasoning capabilities of Large Language Models (LLMs).
arXiv:2509.21912v2 Announce Type: replace Abstract: Guidance provides a simple and effective framework for posterior sampling by steering the generation process towards the desired distribution. When
arXiv:2604.13731v1 Announce Type: new Abstract: Multi-page Document Visual Question Answering requires reasoning over semantics, layouts, and visual elements in long, visually dense documents. Existin
arXiv:2602.20981v3 Announce Type: replace Abstract: Scaling multimodal alignment between video and audio is challenging, particularly due to limited data and the mismatch between text descriptions and
arXiv:2604.13677v1 Announce Type: new Abstract: Mobile robots joining public spaces like sidewalks must care for pedestrian comfort. Many studies consider pedestrians' objective safety, for example, b
arXiv:2604.13491v1 Announce Type: new Abstract: With the rapid progress of Multimodal Large Language Models (MLLMs), unified MLLMs that jointly perform image understanding and generation have advanced
arXiv:2604.13598v1 Announce Type: new Abstract: Recent reinforcement learning (RL) approaches have advanced radiology report generation (RRG), yet two core limitations persist: (1) report-level reward
arXiv:2604.13410v1 Announce Type: cross Abstract: We study the problem of estimating the effect function for a continuous treatment, which maps each treatment value to a population-averaged outcome. A
arXiv:2604.13533v1 Announce Type: cross Abstract: Achieving general-purpose robotics requires empowering robots to adapt and evolve based on their environment and feedback. Traditional methods face li
arXiv:2604.13788v1 Announce Type: cross Abstract: Imitation learning (IL) policies in robotics deliver strong performance in controlled settings but remain brittle in real-world deployments: rare even
arXiv:2508.05153v2 Announce Type: replace Abstract: Category-level generalization for robotic garment manipulation, such as bimanual smoothing, remains a significant hurdle due to high dimensionality,
arXiv:2604.14035v1 Announce Type: new Abstract: Fairness in algorithmic decision-making is often defined in the predictive space, where predictive performance - used as a proxy for decision-maker (DM)
arXiv:2604.13592v1 Announce Type: new Abstract: Reasoning capabilities in large language models (LLMs) have generally advanced significantly. However, it is still challenging for existing reasoning-ba
arXiv:2604.13518v1 Announce Type: new Abstract: Self-supervised learning has emerged as a major technique for the task of learning from unlabeled data, where the current methods mostly revolve around
arXiv:2601.21667v2 Announce Type: replace-cross Abstract: Current mobile manipulation research predominantly follows an instruction-driven paradigm, where agents rely on predefined textual commands to
arXiv:2604.14142v1 Announce Type: cross Abstract: While reinforcement learning with verifiable rewards (RLVR) significantly enhances LLM reasoning by optimizing the conditional distribution P(y|x), it
arXiv:2604.13067v1 Announce Type: cross Abstract: SpeechLLMs process spoken language directly from audio, but accent and vocal identity cues can lead to biased behaviour. Current bias evaluations ofte
arXiv:2604.14053v1 Announce Type: new Abstract: Efficiency and safety of Large Language Models (LLMs), among other factors, rely on the quality of tokenization. A good tokenizer not only improves infe
@GaryMarcus Essentially your prediction that LLMs to scale wouldn’t solve key fundamental problems, reasoning, factual reliability, compositionality, grounded understanding etc are now elephantine chi
Gary Marcus advocates for applying Satyagraha—a principle of nonviolent resistance emphasizing moral conviction—as a response to contemporary challenges, likely in the context of AI development and go
arXiv:2604.13609v1 Announce Type: new Abstract: Reinforcement learners can attain high reward through novel unintended strategies. We study a Bayesian mitigation for general environments: we expand th
arXiv:2510.00573v2 Announce Type: replace Abstract: Robotic food scooping is a critical manipulation skill for food preparation and service robots. However, existing robot learning algorithms, especia
arXiv:2510.00695v3 Announce Type: replace-cross Abstract: Inherently, robotic manipulation tasks are history-dependent: leveraging past context could be beneficial. However, most existing Vision-Langu
arXiv:2604.13871v1 Announce Type: new Abstract: Deep neural networks (DNNs) deliver state-of-the-art accuracy on regression and classification tasks, yet two structural deficits persistently obstruct