Knowledge catalogue
arXiv:2607.03770v1 Announce Type: cross Abstract: Prior studies have demonstrated that diffusion classifiers achieve robust zero-shot classification performance. However, their effectiveness is strong
arXiv:2607.04277v1 Announce Type: cross Abstract: The pursuit of self-evolving AI raises a critical question: when is autonomous self-improvement sustainable rather than degenerative? Drawing an analo
arXiv:2607.05093v1 Announce Type: new Abstract: The exponential growth of video traffic demands novel semantic communication paradigms that transmit meaning rather than raw bits. We present a generati
arXiv:2508.16386v2 Announce Type: replace Abstract: We study the problem of fair cohort selection under uncertainty, motivated by university admissions where applicant outcomes are only partially obse
arXiv:2512.15765v3 Announce Type: replace Abstract: Data valuation is a natural framework for understanding which preference datasets matter most when aligning a Large Language Model (LLM) using multi
arXiv:2606.14216v2 Announce Type: replace Abstract: Accurate and computationally efficient vehicle models are essential for motion planning of autonomous vehicles, where positional accuracy directly a
arXiv:2607.04044v1 Announce Type: new Abstract: Recently, Joint Embedding Predictive Architectures (JEPAs) have attracted significant attention in the computer vision and machine learning communities
arXiv:2607.03091v1 Announce Type: new Abstract: Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promising approach for augmenting traditional
arXiv:2607.04591v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation by integrating visual perception, language understan
arXiv:2510.15476v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used as interfaces to information, code, and real-world services, making prompt-level security f
arXiv:2607.04119v1 Announce Type: cross Abstract: Reconstructing Computer-Aided Design (CAD) modeling sequences from images is crucial for preserving design intent and supporting parametric editing. H
arXiv:2607.04739v1 Announce Type: new Abstract: Sampling action chunks via generative models has become a widely adopted methodology for robotic learning from demonstration. However, existing methods
arXiv:2607.04189v1 Announce Type: new Abstract: Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift
arXiv:2601.22652v2 Announce Type: replace-cross Abstract: Spectral gradient methods, such as the Muon optimizer, modify gradient updates by preserving directional information while discarding scale, a
arXiv:2603.20659v2 Announce Type: replace Abstract: Large scale pre-training on text and image data along with diverse robot demonstrations has helped Vision Language Action models (VLAs) to generaliz
arXiv:2607.04963v1 Announce Type: new Abstract: Reinforcement Learning (RL) is the dominant paradigm for training Large Language Model (LLM) agents on long-horizon tasks. However, sparse and delayed r
States are asking for a $1.4 trillion fine of Meta over addicting kids. Basically they are saying Facebook's business model is a result of crime, and all of Mark Zuckerberg's property should be forfei
arXiv:2607.04708v1 Announce Type: cross Abstract: Agentic AI is shifting online shopping from search toward delegated purchasing, where autonomous buying agents monitor markets and decide when to buy
arXiv:2607.03501v1 Announce Type: cross Abstract: Copernicus, the European Union's Earth observation program, produces petabytes of Earth observation and climate data, offering immense potential for r
arXiv:2203.01687v3 Announce Type: replace Abstract: Medical landmark detection usually requires accurate expert annotations, which are laborious and difficult to scale across anatomical regions. In th
arXiv:2510.08048v4 Announce Type: replace-cross Abstract: Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, wh
arXiv:2607.03181v1 Announce Type: cross Abstract: Successful diffusion of AI in the workforce hinges on the economic value that AI brings to human endeavors. Bringing AI into the workforce is more tha
arXiv:2607.04061v1 Announce Type: cross Abstract: Distinguishing Large Language Model (LLM) generated text from human writing is a critical and difficult challenge. While LLMs are trained to write lik
arXiv:2607.03788v1 Announce Type: new Abstract: Discrete diffusion promises orders-of-magnitude faster generation than autoregressive (AR) models for sequential discrete data, yet its full potential o
arXiv:2607.03143v1 Announce Type: cross Abstract: Vision-language alignment powers open-vocabulary recognition, retrieval, and LVLM grounding, yet natural captions are often underspecified, making sim
arXiv:2606.22726v2 Announce Type: replace Abstract: Choreographic motion generation poses unique challenges for AI, demanding precise semantic control over complex, temporally structured, and expressi
arXiv:2607.02615v1 Announce Type: cross Abstract: Generating structured artifacts with Large Language Models - e.g. database queries, threat framework mappings, entity schemas - is relatively straight
The AI boom is just not sustainable; Apollo’s Torsten Slok joins the chorus. Torsten Slok argues that the AI boom can only be seen in the hyperscalers and semiconductor companies and that this is caus
arXiv:2607.02955v1 Announce Type: cross Abstract: We argue that AI systems used in conducting foreign policy tasks - broadly enacting 'statecraft' - should be a priority test case for technical AI gov
Written by: Shebin Mathew Introduction The 'Golden SAML' technique, first described by CyberArk researchers in 2017, and further detailed by Mandiant researchers in 2021, remains one of the most effec
arXiv:2510.01460v4 Announce Type: replace-cross Abstract: Offline-to-online reinforcement learning (RL) has emerged as a practical paradigm that leverages offline datasets for pretraining and online i
arXiv:2607.03928v1 Announce Type: cross Abstract: Accent normalization (AN) seeks to convert non-native (L2) accented speech into standard (L1) speech while preserving speaker identity. The current te
arXiv:2509.01251v3 Announce Type: replace Abstract: This paper presents a joint effort towards the development of a data-driven Social Robot Navigation metric to facilitate benchmarking and policy opt
arXiv:2408.09112v2 Announce Type: replace Abstract: Reinforcement learning policies parametrized by deep neural networks have achieved strong performance for continuous control, yet even small input p
arXiv:2607.04745v1 Announce Type: cross Abstract: Modern thermal visual place recognition (TIR-VPR) frontends based on foundation models achieve remarkable closed-set retrieval but suffer from an over
arXiv:2511.13103v2 Announce Type: replace Abstract: Multi-agent reinforcement learning (MARL) has shown promise for large-scale network control, yet existing methods face two major limitations. First,
arXiv:2607.04751v1 Announce Type: cross Abstract: Big goals are hard to achieve all at once; breaking them into small steps is wiser. We present Trust Region Policy Distillation (TOP-D), which transfo
arXiv:2607.04728v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training for large language models (LLMs) follows a efficient paradigm of 'rollout then update', which inevitably res
arXiv:2607.04487v1 Announce Type: cross Abstract: Neural autoregressive solvers for the Multi-Attribute Vehicle Routing Problem (MAVRP) reach competitive cost but offer no per-step justification, a pr
arXiv:2607.04425v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform int
arXiv:2607.04430v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in question answering (QA) systems, yet they may generate hallucinated or misaligned responses wi
arXiv:2607.02569v1 Announce Type: new Abstract: This paper presents a safety-centered empirical evaluation of uncertainty-aware last-layer adaptation for referable diabetic retinopathy screening using
arXiv:2510.25599v2 Announce Type: replace Abstract: Regression tasks, notably in safety-critical domains, require reliable uncertainty quantification, yet the literature remains largely classification
arXiv:2510.16923v3 Announce Type: replace-cross Abstract: Deep learning models deployed in safety critical applications like autonomous driving use simulations to test their robustness against adversa
arXiv:2607.04984v1 Announce Type: new Abstract: Continual Generalized Category Discovery (C-GCD) aims to incrementally identify novel categories from sequential unlabeled data while preserving recogni
arXiv:2607.03589v1 Announce Type: new Abstract: State Space Models (SSMs) have emerged as an alternative to Vision Transformers, yet most vision SSMs inherit directional token scanning from causal seq
arXiv:2509.25533v2 Announce Type: replace-cross Abstract: As Vision Language Models (VLMs) are deployed across safety-critical applications, understanding and controlling their behavioral patterns has
arXiv:2508.08521v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) are increasingly being used in a broad range of applications, bringing their security and behavioral control to
arXiv:2607.04517v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are commonly treated as end-to-end action policies conditioned on natural-language task descriptions. In practice, h
arXiv:2607.05180v1 Announce Type: cross Abstract: Adverse driving conditions, such as bad weather, remain a principal barrier to autonomous driving because they degrade two things at once: what the ve
arXiv:2607.05394v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a powerful recipe for improving language-model reasoning, but it is expensive to repeat on ev
arXiv:2607.05132v1 Announce Type: cross Abstract: As large language models are deployed as autonomous agents that communicate intentions before acting, a critical safety question is whether agents tha
Who is entitled to benefit from major advances in technology—and on what basis? This new paper with @Dr_Atoosa argues that the benefits of technology—including AI—belong to the world in the sense that
arXiv:2607.04879v1 Announce Type: new Abstract: Although multi-source fusion positioning systems have achieved significant progress, accurate and reliable heading estimation remains a critical challen
⚠️ Wow. The Treasury Department reportedly knows that the massive GenAI build out poses systemic risks to the US financial system – and doesn’t want to acknowledge it publicly. People may talk about t
You don't have to choose between 'AI is fake hype' and 'Superintelligence is inevitable, lie down and accept it.' There's a third option: humans deciding, through their governments, that machines smar
A CEO who “vowed to fire anyone who doesn’t use AI in 2025” now says AI could not replace her executive assistant. This says a lot about how many big believers in AI have realized that AI is not as go
Race control displayed a 'Safety Car in this lap' message during the 2026 British Grand Prix at Silverstone, raising expectations for a final lap restart that never materialized, as the message was er
Gary Marcus argues that Geoff Hinton, rather than Dario Amodei, was the originator of making exaggerated claims about neural networks' impact on employment. This appears to be part of a broader discus
GenAI isn’t good enough to replace millions of employees, so there is basically no way the capex is going to earn out. If AI isn't going to wipe out millions of jobs every year, it has to generate pro