All entries

arXiv:2605.30807v1 Announce Type: new Abstract: Conditional generative models have recently achieved remarkable success in various applications. However, a suitable metric for evaluating the reliabili

Connecting agents directly to Snowflake, Slack, or BigQuery via MCP/CLIs is a multi-token disaster. They default to brute-force exploration, firing 20-30 tool calls just to rediscover context on every

arXiv:2601.06453v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly grounded in sensor data to perceive and reason about human physiology and the physical world. However,

arXiv:2605.31073v1 Announce Type: new Abstract: Reasoning-based LLM guardrails improve safety moderation by generating explicit rationales before issuing final decisions. However, their rationales do

arXiv:2605.31494v1 Announce Type: new Abstract: Post-training of language models is commonly framed as a sample-score-update loop implemented by gradient descent. A recent line of work, exemplified by

arXiv:2605.30610v1 Announce Type: new Abstract: Adapting generative foundation models, in particular diffusion and flow models, to optimize given reward functions (e.g., binding affinity) while satisf

arXiv:2605.31388v1 Announce Type: new Abstract: Multi-Objective Reinforcement Learning (MORL) extends standard RL by optimizing policies with respect to multiple, often conflicting, objectives. While

arXiv:2601.01754v3 Announce Type: replace-cross Abstract: Transformers excel empirically on tasks that process well-formed inputs according to some grammar, such as natural language and code. However,

arXiv:2605.31291v1 Announce Type: cross Abstract: Recommender systems may operate under multiple, competing objectives. For example, audience reach, cultural values, public service mandate, and operat

arXiv:2605.30689v1 Announce Type: cross Abstract: Zero-shot Temporal Action Localization (ZS-TAL) aims to detect and locate previously unseen actions in untrimmed videos. However, existing approaches

arXiv:2605.30631v1 Announce Type: cross Abstract: While automated diagnosis systems have achieved remarkable success in computed tomography (CT)-based lung cancer screening, their development remains

arXiv:2605.30648v1 Announce Type: new Abstract: Recent work has analyzed the convergence of first-order methods under non-uniform smoothness assumptions that better model the loss landscape in machine

arXiv:2605.31172v1 Announce Type: new Abstract: This work studies the convergence of two-timescale stochastic approximations (SA), a class of iterative algorithms that update two sets of parameters in

arXiv:2605.31239v1 Announce Type: cross Abstract: Bagging-based ensembles, most notably Adaptive Random Forests, are among the strongest performers for learning from data streams. A common denominator

Cosmos3-Super-Image2Video is an omnimodal world model capable of generating video from combinations of text and image inputs . The model is designed to run on workstation-grade compute like the NVIDIA

arXiv:2507.12453v5 Announce Type: replace Abstract: In automated machine learning, scientific discovery, and other applications of Bayesian optimization, deciding when to stop evaluating expensive bla

arXiv:2605.30846v1 Announce Type: new Abstract: Object counting remains fragmented across domain-specific datasets and task formulations, despite rapid progress in generalist vision models. Existing c

arXiv:2605.30590v1 Announce Type: cross Abstract: Two clinical AI systems can score nearly identically on coverage-based rubrics yet behave radically differently when their patient inputs change: one

arXiv:2605.30653v1 Announce Type: new Abstract: Multi-agent LLM systems often treat agreement as evidence: when many agents in a panel give the same answer, that answer is assumed to be more reliable.

arXiv:2605.11946v2 Announce Type: replace Abstract: Large Language Model agents are increasingly augmented with agent skills. Current evaluation methods for skills remain limited. Most deployed benchm

arXiv:2605.30611v1 Announce Type: cross Abstract: Scientific figures are among the most effective means of communicating complex research ideas, yet producing publication-quality illustrations remains

arXiv:2512.11571v2 Announce Type: replace Abstract: Autonomously performing tasks often requires robots to plan high-level discrete actions and continuous low-level motions to realize them. Previous T

arXiv:2605.30836v1 Announce Type: new Abstract: Recent SVD based compression methods for large language models like SVD LLM and Basis Sharing can be unified under one optimization problem. While mathe

arXiv:2605.30443v1 Announce Type: new Abstract: Multilingual large language models can generate figurative language, but whether the internal signals driving this behavior are language-specific or reu

arXiv:2501.01926v3 Announce Type: replace-cross Abstract: Large vision-language models (LVLMs) have shown remarkable capabilities in visual-language understanding. Despite their success, LVLMs still s

arXiv:2605.31093v1 Announce Type: new Abstract: Breast cancer is a major global health concern, and mammography screening plays a central role in early detection. The large volume of screening examina

arXiv:2605.30640v1 Announce Type: cross Abstract: Low-rank adaptation has become a standard method for parameter-efficient fine-tuning of large language models, but even small amounts of unsafe or adv

Current AI is bandaids all the way down; LLMs can’t play nicely with basic tools like databases and knowledge graphs, and you never know what you are going to get. It’s long time to face facts: LLMs h

arXiv:2602.14441v2 Announce Type: replace Abstract: Multimodal misinformation increasingly mixes realistic im-age edits with fluent but misleading text, producing persuasive posts that are difficult t

arXiv:2605.31164v1 Announce Type: cross Abstract: Training data plays a central role in large language models (LLMs) optimization, motivating extensive research on data scheduling strategies. Most exi

arXiv:2605.30859v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has become pivotal for improving model capabilities yet suffers from rollout efficiency bottlenecks due to the long-tail r

arXiv:2605.31360v1 Announce Type: cross Abstract: The Artificial Intelligence (AI) life cycle requires a thorough understanding of the underlying data dynamics for robust, safe and cost-effective AI d

arXiv:2603.13727v2 Announce Type: replace Abstract: Symbolic regression is a powerful tool for knowledge discovery, enabling the extraction of interpretable mathematical expressions directly from data

arXiv:2605.30919v1 Announce Type: cross Abstract: The rapid development of large language models (LLMs) has raised concerns on the use of inappropriate data for training, which has led to a growing in

arXiv:2509.18898v2 Announce Type: replace Abstract: In this paper, we propose the first Structure-from-Motion (SfM)-free deblurring 3D Gaussian Splatting method via event camera, dubbed DeblurSplat. W

This Databricks article challenges common misconceptions about data layout optimization strategies, comparing Liquid Clustering with traditional partitioning approaches. It examines eight specific myt

arXiv:2605.31336v1 Announce Type: new Abstract: Recent advances in video generative models have promoted rapid progress in controllable world models. However, maintaining fine-grained spatio-temporal

arXiv:2509.20941v2 Announce Type: replace Abstract: As surgical AI transitions from pixel-level detection to complex reasoning, Scene Graphs (SGs) offer the structured, relational representations nece

arXiv:2605.31391v1 Announce Type: cross Abstract: Modern machine learning techniques have become increasingly important in particle physics because of their powerful pattern-recognition capabilities,

arXiv:2602.10809v2 Announce Type: replace Abstract: Existing multimodal retrieval systems excel at semantic matching but implicitly assume that query-image relevance can be measured in isolation. This

arXiv:2509.02970v3 Announce Type: replace Abstract: Partial participation is essential for communication-efficient federated learning at scale, yet existing Byzantine-robust methods typically assume f

arXiv:2605.31007v1 Announce Type: cross Abstract: Anomaly detection in physiological sensor data from Wireless Body Area Networks (WBANs) can be caused by sensor faults, network disruptions, or missin

arXiv:2605.31286v1 Announce Type: cross Abstract: Real-world household robots require Vision-Language-Action (VLA) foundation models that can acquire reusable manipulation skills across diverse object

Demo2 showcases Qwen's multimodal interactive hybrid agent capabilities, likely demonstrating the integration of multiple data types (text, image, audio, video) with interactive features and hybrid pr

Demo3 showcases Qwen's browser agent capabilities, likely demonstrating an AI system's ability to autonomously interact with web browsers to perform tasks such as navigation, form filling, or informat

arXiv:2605.30901v1 Announce Type: new Abstract: Counterfactual explanations (CEs) are essential for actionable recourse, yet their reliability is often compromised in low-density regions, where classi