Knowledge catalogue
arXiv:2605.03135v1 Announce Type: new Abstract: Standard classification treats all errors equally, but in content moderation, medical screening, and safety-critical applications, mistakes on clear-cut
arXiv:2605.02175v1 Announce Type: new Abstract: The Legg--Hutter universal intelligence measure provides a rigorous scalar assessment of general intelligence as expected reward across all computable e
just want to go back to how much intense pushback i got on this story at all levels of the company at the time, and how people speak very differently when under the threat of perjury lot of names etch
arXiv:2605.01790v1 Announce Type: cross Abstract: A common design pattern in high-quality music generation is to handle structure and fidelity in different representation spaces: a generator first mod
arXiv:2605.04040v1 Announce Type: new Abstract: Text-to-image generation has advanced rapidly with diffusion models, progressing from CLIP and T5 conditioning to unified systems where a single LLM bac
arXiv:2605.03363v1 Announce Type: new Abstract: In this work, we propose a hybrid hierarchical control framework for reactive dexterous grasping that explicitly decouples high-level spatial intent fro
Tim Harford compares large language models to tricksters, arguing that LLMs excel at generating plausible-sounding text without necessarily ensuring accuracy or truthfulness. The article likely explor
arXiv:2605.03299v1 Announce Type: new Abstract: Cross-lingual topic modeling aims to discover shared semantic structures across languages, yet existing models depend on sparse bilingual resources and
arXiv:2412.13235v4 Announce Type: replace Abstract: The logic-constrained shortest path problem (LCSPP) combines a one-to-one shortest path problem with satisfiability constraints imposed on the routi
arXiv:2605.03228v1 Announce Type: cross Abstract: As large language model (LLM)-powered agents are increasingly deployed to perform complex, real-world tasks, they face a growing class of attacks that
Many trials feel like Rashomon with different witnesses. The amazing thing about Musk-OpenAI is how much agreement there has been (at least so far) on the facts. The question is really whether what Op
arXiv:2605.02908v1 Announce Type: new Abstract: Understanding how textual embeddings contribute to memorization in text-to-image diffusion models is crucial for both interpretability and safety. This
arXiv:2605.01507v1 Announce Type: new Abstract: Prior studies report that partial driving automation can increase the cognitive demands on human drivers. This effect largely arises from human drivers'
arXiv:2602.05048v2 Announce Type: replace Abstract: Joint planning through language-based interactions is a key area of human-AI teaming. Planning problems in the open world often involve various aspe
Mira Murati, OpenAI's former CTO, has testified under oath that CEO Sam Altman lied to her about the safety standards for a new AI model. In a video deposition shown during the ongoing Musk v. Altman
Mira Murati’s testimony is gripping – and what it makes absolutely clear is how utterly wrong most of Twitter was about why Sam was fired. – It had nothing per se to do with AI safety - It had nothing
arXiv:2605.03359v1 Announce Type: new Abstract: Recent trends in sparse-view 3D reconstruction have taken two different paths: feed-forward reconstruction that predicts pixel-aligned point maps withou
arXiv:2605.01710v1 Announce Type: new Abstract: AI products often route requests through version aliases, service tiers, tool choices, regional endpoints, fallback rules, or safety handling before res
arXiv:2605.02087v1 Announce Type: new Abstract: Some frontier AI developers aim to align language models to a Model Spec or Constitution that describes the intended model behavior. However, standard a
arXiv:2605.02971v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit severe multilingual safety misalignment: they possess strong safeguards in high-resource languages but remain hig
Jay Peters / The Verge: Musk v. Altman: Mira Murati testifies that Sam Altman lied to her about the safety standards for a new OpenAI model and that he made her work more difficult — OpenAI's former C
arXiv:2605.03058v1 Announce Type: new Abstract: A key goal of explainable AI (XAI) is to express the decision logic of large language models (LLMs) in symbolic form and link it to internal mechanisms.
arXiv:2605.02092v1 Announce Type: new Abstract: The automation of scientific research workflows has emerged as a transformative frontier in artificial intelligence, yet existing autonomous research ag
arXiv:2605.03769v1 Announce Type: new Abstract: Matrix-based optimizers have demonstrated immense potential in training Large Language Models (LLMs), however, designing an ideal optimizer remains a fo
arXiv:2503.17715v3 Announce Type: replace Abstract: We introduce the Normalized Matching Transformer (NMT), a deep learning approach for efficient and accurate sparse semantic keypoint matching betwee
arXiv:2605.03065v1 Announce Type: new Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.
arXiv:2605.03290v1 Announce Type: new Abstract: Domain randomization (DR) is widely used in policy learning to improve robustness to modeling error, but remains underexplored in contact-rich sampling-
OpenAI violated Canadian privacy laws in developing first ChatGPT model, probe finds https://www.theglobeandmail.com/business/article-openai-chatgpt-violated-canadian-privacy-laws-watchdogs-report/?ut
arXiv:2605.03921v1 Announce Type: new Abstract: We study the (arepsilon, elta)-PAC policy identification problem in finite-horizon episodic Markov Decision Processes. Existing approaches provide finit
arXiv:2605.03490v1 Announce Type: new Abstract: The clinical integration of deep learning models for brain tumor diagnosis in neuro-oncology is severely constrained by limited expert-annotated MRI dat
arXiv:2604.17654v3 Announce Type: replace Abstract: Exploration is a cornerstone of learning from experience: it enables agents to find solutions to complex problems, generalize to novel ones, and sca
arXiv:2605.03357v1 Announce Type: new Abstract: Mean Field Games (MFGs) provide a powerful framework for modeling the collective behavior of large populations of interacting agents. In this paper, we
arXiv:2605.01147v1 Announce Type: new Abstract: As large language models are increasingly deployed as interacting agents in high-stakes decisions, the AI safety community assumes that safety propertie
arXiv:2410.09457v2 Announce Type: replace Abstract: Modern cryptographic methods for implementing privacy-preserving LLMs such as gls{HE} require the LLMs to have a polynomial form. Forming such a rep
arXiv:2605.03733v1 Announce Type: cross Abstract: Minimizing the Mean Squared Error (MSE) is a key objective in machine learning and is commonly used for imputing missing values. While this approach p
arXiv:2605.02372v1 Announce Type: cross Abstract: The growing development of artificial intelligence based solutions, together with privacy legislation, has driven the rise of the so-called privacy pr
arXiv:2602.14663v2 Announce Type: replace Abstract: We present pseudo-differential enhanced physics-informed neural networks (PINNs), an extension of gradient enhancement but in Fourier space. Gradien
arXiv:2605.02120v1 Announce Type: new Abstract: This paper develops a deep reinforcement learning based observer control policy for autonomous bearings-only tracking of a moving target. The observer m
arXiv:2602.00815v2 Announce Type: replace Abstract: Large language models (LLMs) have exhibited remarkable performance on complex reasoning tasks, with reinforcement learning under verifiable rewards
arXiv:2605.03724v1 Announce Type: new Abstract: A recent landscape analysis of LoRA fine-tuning in the neural tangent kernel regime establishes a sufficient condition r(r+1)/2 > KN on the LoRA rank r
arXiv:2605.03269v1 Announce Type: cross Abstract: While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intellig
arXiv:2603.10572v2 Announce Type: replace Abstract: Partially Observable Markov Decision Processes (POMDPs) provide a principled framework for robot decision-making under uncertainty. Solving reach-av
arXiv:2605.02900v1 Announce Type: cross Abstract: Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open-world, saf
arXiv:2503.06917v5 Announce Type: replace Abstract: We study zeroth-order optimization where solutions must minimize a cost d(s) while maintaining high probability under a complex generative prior L(s
arXiv:2605.01055v1 Announce Type: cross Abstract: Object caches underpin cloud and edge services, but production workloads are heterogeneous, nonstationary, and throughput-constrained. Recent simple n
arXiv:2605.03701v1 Announce Type: new Abstract: Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Larg
arXiv:2605.02526v1 Announce Type: cross Abstract: Barrier certificates are scalar functions over the state space of dynamical systems that separate all unsafe states from all reachable states. The exi
arXiv:2605.03846v1 Announce Type: new Abstract: Designing an open-world quadrupedal loco-manipulation system is highly challenging. Traditional reinforcement learning frameworks utilizing exteroceptio
arXiv:2508.18983v3 Announce Type: replace Abstract: The Mixture of Experts (MoE) architecture has emerged as a key technique for scaling Large Language Models by activating only a subset of experts pe
arXiv:2605.03371v1 Announce Type: new Abstract: Cross-scene hyperspectral image (HSI) classification stands as a fundamental research topic in remote sensing, with extensive applications spanning vari
arXiv:2509.20102v2 Announce Type: cross Abstract: Adversarial scenario generation is a cost-effective approach for safety assessment of autonomous driving systems. However, existing methods are often
arXiv:2605.03849v1 Announce Type: new Abstract: Distillation-based acceleration has become foundational for making autoregressive streaming video diffusion models practical, with distribution matching
arXiv:2605.02973v1 Announce Type: new Abstract: Modality translation is inherently under-constrained, as multiple cross-modal mappings may yield the same marginals. Recent work has shown that diffusio
arXiv:2605.02178v1 Announce Type: new Abstract: Recent progress in multi-turn reinforcement learning (RL) has significantly improved reasoning LLMs' performances on complex interactive tasks. Despite
arXiv:2605.01750v1 Announce Type: cross Abstract: Grounding is the collaborative process of establishing mutual belief sufficient for the current communicative purpose. While static grounding maps lan
arXiv:2605.03237v1 Announce Type: cross Abstract: Project-based learning improves student engagement and learning outcomes, yet allocating students to appropriately challenging projects while forming
arXiv:2408.12622v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is reshaping society, from video generation to medical diagnosis, coding agents to autonomous vehicles. Yet resea
arXiv:2605.02681v1 Announce Type: cross Abstract: We present two new classes of causal models of decision-making agents. Our approach is motivated by the needs of modeling the economics of computing s
arXiv:2605.01245v1 Announce Type: cross Abstract: Narrative archetypes (e.g., Hero's Journey, Three-act structure) provide universal story structures that resonate across cultures and media and are im
arXiv:2603.17368v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieved remarkable performance via chain-of-thought (CoT), but recent studies showed that such enhanced reasoning cap