Knowledge catalogue
arXiv:2605.28276v1 Announce Type: new Abstract: Reinforcement learning algorithms are commonly analyzed (and designed) under the Markov assumption. This is unrealistic, as most environments encountere
arXiv:2605.28484v1 Announce Type: new Abstract: Composing finite-state transducers (FSTs) for context-dependent morphophonological rules -- consonant gradation, vowel harmony, possessive suffix assimi
arXiv:2605.27467v1 Announce Type: cross Abstract: Traditional Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) units operate on discrete time steps, often failing to capture the flui
arXiv:2605.28304v1 Announce Type: new Abstract: Composing autoregressive models remains a core challenge in understanding how large language models can combine behaviors or skills learned across tasks
arXiv:2605.28615v1 Announce Type: new Abstract: Despite the rapid progress of text-to-image (T2I) models, generating images that accurately reflect complex compositional prompts (covering attribute bi
Computer runs on the same secure infrastructure as the rest of Perplexity. The enterprise-grade security layer includes SAML SSO, audit logs, and granular admin controls. Read more: https://www.perple
arXiv:2605.27952v1 Announce Type: new Abstract: Visual odometry (VO) is a fundamental component in robotics and augmented reality. RGB-D direct VO benefits from metric depth measurements, but it can d
arXiv:2602.18982v4 Announce Type: replace Abstract: Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independ
arXiv:2605.28010v1 Announce Type: new Abstract: Self-evolving large language models (LLMs) learn by generating their own training tasks and solutions, reducing reliance on human-curated supervision. H
arXiv:2411.13479v4 Announce Type: replace-cross Abstract: We consider conformal prediction for multivariate data and focus on hierarchical data, where some components are linear combinations of others
arXiv:2605.28093v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has emerged as a promising paradigm for enhancing large language models (LLMs) on multi-hop question answering (QA)
arXiv:2605.28516v1 Announce Type: cross Abstract: Simulation-based inference with neural posterior estimation (NPE) often yields overconfident and unreliable posteriors under limited simulation budget
arXiv:2605.27811v1 Announce Type: new Abstract: Auto-bidding systems aim to maximize advertiser value over long horizons under budget constraints and ratio targets such as cost-per-acquisition, yet fu
arXiv:2605.27450v1 Announce Type: cross Abstract: Modern industrial recommender systems use a deep ranking model to score N candidates against the same user and context features. Standard implementati
arXiv:2605.27975v1 Announce Type: new Abstract: Generative models, including diffusion models, are increasingly used as foundation models and adapted through sequential fine-tuning, making continual l
arXiv:2605.28577v1 Announce Type: new Abstract: AI model hubs provide access to a rapidly growing collection of powerful pre-trained models, enabling off-the-shelf mixture-of-experts systems with diff
arXiv:2602.12468v2 Announce Type: replace Abstract: Diffusion language models offer a promising alternative to autoregressive models due to their global, non-causal generation process, but their conti
arXiv:2605.28420v1 Announce Type: new Abstract: While machine learning (ML) architectures have evolved rapidly to account for complex data, loss functions like cross-entropy remain mostly structure-ag
arXiv:2605.28062v1 Announce Type: new Abstract: We describe ConvMemory, a small 3.6M-parameter learned reranker for conversational long-term memory retrieval, trained with cross-encoder teacher superv
I cannot provide a summary for this entry, as the URL appears to be either invalid or refers to a post that is not publicly accessible or does not exist. To create an accurate knowledge base entry, th
arXiv:2603.00349v2 Announce Type: replace Abstract: Many complex tasks require extended effort, diverse capabilities, or coordinated actions beyond what a single agent can provide. However, simply add
arXiv:2605.28742v1 Announce Type: new Abstract: Language models can use verifiable rewards to improve at a wide variety of reasoning tasks. However, both parametric (e.g. RLVR) and non-parametric (e.g
Artificial intelligence cloud operator CoreWeave Inc. today announced the launch of a new offering that gives enterprise outfits the ability to deploy AI agents that can learn and improve themselves a
arXiv:2510.22016v2 Announce Type: replace Abstract: Selecting an appropriate evaluation metric for classifiers is crucial for model comparison, parameter optimization, and deployment decisions, yet th
arXiv:2604.00402v2 Announce Type: replace-cross Abstract: Developing robust models to accurately predict the trajectories of surrounding agents is fundamental to autonomous driving safety. However, mo
arXiv:2605.28251v1 Announce Type: cross Abstract: We consider the problem of learning a counterfactually fair regressor. We adopt a causal uncertainty view in which counterfactual fairness is defined
arXiv:2601.00501v2 Announce Type: replace Abstract: We introduce CPPO, a Contrastive Perception Policy Optimization method for finetuning vision--language models (VLMs). Reliable perception is a core
arXiv:2604.01604v2 Announce Type: replace Abstract: While modern LLMs are aligned to refuse harmful requests, it is essential to understand the underlying mechanistic basis of this refusal behavior fo
crazy that this was announced on the day tokenmaxxing died. Anthropic raised 65 billion in a funding round that valued the artificial intelligence company at 965 billion including the new investment,
Create transformation videos where a cube, sphere, capsule, or custom object transforms into a person or item with a button press using Seedance 2.0 inside ComfyUI. The workflow automatically generate
arXiv:2605.27701v1 Announce Type: new Abstract: We present Frost Training, a method for improving Monte Carlo-based policy optimization for a large family of LLM-as-a-judge tasks called Cross-Entropy
arXiv:2605.24302v2 Announce Type: replace Abstract: Egocentric action recognition is a challenging task due to erratic camera motion, frequent hand occlusion, and the difficulty of maintaining consist
A new report out today from Corporation Service Co. finds that third-party registrations of domains containing “FIFA” have surged in the buildup to the 2026 World Cup, with many of them staged for fra
arXiv:2605.28763v1 Announce Type: new Abstract: Interactive 3D assets used in games and simulation are typically decomposed into specific semantic parts to support animation, physics, and scripted beh
arXiv:2605.28543v1 Announce Type: new Abstract: LLMs often default to equal treatment across cultural groups, even though context warrants differentiation: this is a lack of difference awareness. Usin
arXiv:2605.27654v1 Announce Type: cross Abstract: Generative translation systems are cultural technologies because they decide how socially meaningful cues are rendered within culturally specific gram
arXiv:2605.27800v1 Announce Type: new Abstract: CASTLE 2026 asks 185 multiple-choice questions over 600+ hours of synchronized multi-view egocentric video. We explore two approaches on top of a shared
arXiv:2605.27584v1 Announce Type: new Abstract: The proliferation of social media platforms and online communities has inadvertently catalyzed the spread of cyberbullying, hate speech, and other forms
Tesla's Cybercab autonomous vehicle successfully demonstrated self-driving capabilities by operating independently from the GigaTexas manufacturing facility in Austin, Texas. This milestone showcases
arXiv:2605.28369v1 Announce Type: new Abstract: E-commerce platforms have begun recruiting crowdsourced jurors to adjudicate massive volumes of transaction disputes. Unlike formal legal judgment, E-co
arXiv:2605.27954v1 Announce Type: new Abstract: Agentic large language models are increasingly used to solve real-world tasks by reasoning over goals, invoking tools, and interacting with external env
arXiv:2605.27460v1 Announce Type: new Abstract: Single-frame atmospheric turbulence mitigation is inherently ill-posed due to spatially varying blur coupled with non-rigid geometric distortion. Existi
arXiv:2512.00252v4 Announce Type: replace-cross Abstract: Data assimilation (DA) is a cornerstone of scientific and engineering applications, combining model forecasts with sparse and noisy observatio
arXiv:2605.28596v1 Announce Type: cross Abstract: extsc{DarkEmulator2} is a neural network emulator of the nonlinear matter power spectrum in a nine-dimensional w_0 w_a nu o CDM parameter space, devel
arXiv:2605.28139v1 Announce Type: new Abstract: Building competitive automatic speech recognition (ASR) models usually requires large-scale au- dio supervision, which makes reproduction and specializa
Data Formulator introduces AI-powered analytics for enterprise data workflows. Data teams can easily bring enterprise data into an AI-ready workspace where users can explore, analyze, and visualize da
Vultr's data residency solution enables organizations to store and process data within specific geographic regions to meet regulatory compliance requirements such as GDPR, HIPAA, and local data protec
Paul Sandle / Reuters: Dealroom: London overtakes Paris to reclaim its position as Europe's leading tech hub, and now ranks fourth globally; London startups raised $17.7B in 2025 — London has reclaime
arXiv:2605.27483v1 Announce Type: cross Abstract: Despite theoretical promise, debate as a scalable oversight protocol has produced mixed empirical results: gains in some settings, and null effects in
arXiv:2512.00349v2 Announce Type: replace Abstract: Are frontier AI systems becoming more capable? Certainly. Yet such progress is not an unalloyed blessing but rather a Trojan horse: behind their per
arXiv:2605.28167v1 Announce Type: new Abstract: Text-to-image diffusion models, which are theoretically equivalent to score-based generative models, generate images through a multi-step denoising proc
arXiv:2605.27831v1 Announce Type: new Abstract: We study decentralized online convex optimization when agents communicate over a graph and messages may be compressed. Classical decentralized online me
arXiv:2605.28340v1 Announce Type: cross Abstract: The use of residential photovoltaics has increased dramatically in recent years. With battery systems becoming more affordable, the optimal operation
arXiv:2605.27858v1 Announce Type: cross Abstract: Claim verification splits between end-to-end classifiers that are accurate but yields no inspectable traces, and decomposition-based methods produce i
arXiv:2605.28144v1 Announce Type: new Abstract: LLMs have shown remarkable proficiency in general language understanding and reasoning. However, they consistently underperform in spatial reasoning tha
arXiv:2605.27900v1 Announce Type: new Abstract: Federated Learning (FL) with pre-trained Vision-Language Models (VLMs) has emerged as a promising paradigm for various downstream tasks. By leveraging i
arXiv:2602.03491v2 Announce Type: replace-cross Abstract: Reasoning over table images remains challenging for Large Vision-Language Models (LVLMs) due to complex layouts and tightly coupled structure-
arXiv:2602.06880v2 Announce Type: replace Abstract: Adaptive methods like Adam have become the extit{de facto} standard for large-scale vector and Euclidean optimization due to their coordinate-wise a
arXiv:2605.28697v1 Announce Type: cross Abstract: Speckle tracking echocardiography (STE) is the clinical standard for myocardial strain estimation. Despite good performance on global strain (GLS), it
arXiv:2605.27991v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have achieved remarkable empirical success, yet their training dynamics remain understood mainly from optimization rather