Introducing the Google Cloud Knowledge Catalog
Traditional data catalogs were built as manual inventories for technical users, focusing on table structures rather than the deep context that AI agents need. When agents lack business semantics and d
Knowledge catalogue
Traditional data catalogs were built as manual inventories for technical users, focusing on table structures rather than the deep context that AI agents need. When agents lack business semantics and d
arXiv:2604.18729v1 Announce Type: new Abstract: Humor holds up a mirror to social perception: what we find funny often reflects who we are and how we judge others. When language models engage with hum
Kimi K2.6 is free on Nous Portal for the next 24 hours Made possible by @vercel's AI Gateway & @Kimi_Moonshot Run 'hermes update', then 'hermes model' and select Kimi K2.6 to try out one of the most i
arXiv:2604.19464v1 Announce Type: cross Abstract: More than half of the global population struggles to meet their civil justice needs due to limited legal resources. While Large Language Models (LLMs)
arXiv:2604.18897v1 Announce Type: new Abstract: We present a systematic empirical study of prompt engineering for formal mathematical reasoning in the context of the SAIR Equational Theories Stage 1 c
LiteParse, our OSS document parser, is really good at parsing complex PDF layouts, text, and tables into a clean spatial grid. The best part is it doesn't use VLMs or any ML models at all. It's entire
arXiv:2604.19445v1 Announce Type: new Abstract: This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world a
arXiv:2604.19159v1 Announce Type: new Abstract: Deep-feature-based perceptual similarity models have demonstrated strong alignment with human visual perception in Image Quality Assessment (IQA). Howev
arXiv:2604.19383v1 Announce Type: cross Abstract: Metal-organic frameworks (MOFs) are a major target of machine-learning-based property prediction, yet most models assume that a single framework repre
arXiv:2510.18333v2 Announce Type: replace-cross Abstract: Despite progress in watermarking algorithms for large language models (LLMs), real-world deployment remains limited. We argue that this gap st
arXiv:2604.19069v1 Announce Type: cross Abstract: Neural NLI models overfit dataset artifacts instead of truly reasoning. A hypothesis-only model gets 57.7% in SNLI, showing strong spurious correlatio
arXiv:2604.18838v1 Announce Type: new Abstract: This research investigates the performance and efficacy of machine learning models in stock prediction, comparing Artificial Neural Networks (ANNs), Qua
arXiv:2601.04562v2 Announce Type: replace Abstract: Generative recommendation with large language models (LLMs) reframes prediction as sequence generation, yet existing LLM-based recommenders remain l
arXiv:2604.19079v1 Announce Type: cross Abstract: Unification of automatic speech recognition (ASR) systems reduces development and maintenance costs, but training a single model to perform well in bo
arXiv:2604.18982v1 Announce Type: new Abstract: Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents. Training such age
arXiv:2604.18780v1 Announce Type: new Abstract: Semi-Markov Conditional Random Fields (semi-CRFs) assign labels to segments of a sequence rather than to individual positions, enabling exact inference
arXiv:2512.15907v2 Announce Type: replace Abstract: Evaluating the quality of tables generated by large language models (LLMs) remains an open challenge: existing metrics either flatten tables into te
arXiv:2601.09953v2 Announce Type: replace Abstract: Standardized math assessments require expensive human pilot studies to establish the difficulty of test items. We investigate the predictive value o
arXiv:2604.19245v1 Announce Type: cross Abstract: Repair, an important resource for resolving trouble in human-human conversation, remains underexplored in human-LLM interaction. In this study, we inv
The first wave of AI changed how we find information; the next wave is changing how we get work done. Today, we’re enhancing our most powerful AI tools and bringing them together under one roof. Gemin
arXiv:2604.18716v1 Announce Type: cross Abstract: Today, machine learning is widely applied in sensitive, security-related, and financially lucrative applications. Model extraction attacks undermine c
arXiv:2603.22608v2 Announce Type: replace Abstract: Users often rely on Large Language Models (LLMs) for processing multiple documents or performing analysis over a number of instances. For example, a
arXiv:2604.19697v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown promising reasoning abilities, yet evaluating their performance in specialized domains remains chall
arXiv:2604.06665v2 Announce Type: replace Abstract: Video depth estimation is essential for providing 3D scene structure in applications ranging from autonomous driving to mixed reality. Current end-t
arXiv:2509.16343v2 Announce Type: replace-cross Abstract: Building robust vision systems for high-stakes domains such as remote sensing requires stronger visual reasoning than what single-pass inferen
Companies are shifting from gen AI that simply answers questions to autonomous agents that perceive, reason, and act on their behalf. Attempting to scale these agents on legacy stacks exposes structur
arXiv:2602.11199v2 Announce Type: replace Abstract: Large language models (LLMs) often respond even when prompts omit critical details or include misleading information, leading to hallucinations or r
Access GPT Image 2.0 natively in Hermes Agent Update now to get access - just run `hermes update` and select your image generation tool model with `hermes tools` Introducing ChatGPT Images 2.0 A state
arXiv:2604.02846v2 Announce Type: replace Abstract: Fourier-encoded implicit neural representations (INRs) have shown strong capability in modeling continuous signals from discrete samples. However, c
arXiv:2604.17889v1 Announce Type: new Abstract: Despite recent progress in multimodal large language models (MLLMs), reliable visual question answering in aerial scenes remains challenging. In such sc
arXiv:2603.23224v2 Announce Type: replace Abstract: Generative models have shown substantial impact across multiple domains, their potential for scene synthesis remains underexplored in robotics. This
arXiv:2604.17366v1 Announce Type: new Abstract: Argumentation skills are an essential toolkit for large language models (LLMs). These skills are crucial in various use cases, including self-reflection
The Reddit post 'Benchmarking programs?' in r/ollama likely discusses tools and methods for measuring the performance of local language models running on Ollama. Available benchmarking tools for Ollam
arXiv:2508.16745v2 Announce Type: replace Abstract: Reasoning is a core capability of large language models, yet how multi-step reasoning is learned and executed remains unclear. We study this questio
arXiv:2604.16785v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have enabled open-ended object recognition, yet they struggle with fine-grained tasks. In co
arXiv:2604.17316v1 Announce Type: new Abstract: Safe clinical deployment of Large Language Models (LLMs) requires not only high accuracy but also robust uncertainty calibration to ensure models defer
arXiv:2603.23987v2 Announce Type: replace Abstract: Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under distribution shifts at the next. In this work, we st
arXiv:2601.17230v2 Announce Type: replace Abstract: Automated Fact-Checking has largely focused on verifying general knowledge against static corpora, overlooking high-stakes domains like law where tr
arXiv:2604.16665v1 Announce Type: new Abstract: Urgent blood donation seeking posts and messages on social media often go unnoticed due to the overwhelming volume of daily communications. Traditional
arXiv:2604.17492v1 Announce Type: new Abstract: Joint image-feature generative modeling has recently emerged as an effective strategy for improving diffusion training by coupling low-level VAE latents
arXiv:2509.00789v2 Announce Type: replace Abstract: The pursuit of autonomous agents capable of temporally coherent planning is hindered by a fundamental flaw in current vision-language models (VLMs):
arXiv:2511.08480v3 Announce Type: replace Abstract: Multimodal Large Language Models advance multimodal representation learning by acquiring transferable semantic embeddings, thereby substantially enh
arXiv:2602.05449v3 Announce Type: replace Abstract: While diffusion models have achieved great success in the field of video generation, this progress is accompanied by a rapidly escalating computatio
arXiv:2604.08302v2 Announce Type: replace Abstract: We present DMax, a new paradigm for efficient diffusion language models (dLLMs). It mitigates error accumulation in parallel decoding, enabling aggr
arXiv:2604.17191v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) is crucial for AI systems that operate collaboratively in distributed and adversarial settings, particularly i
arXiv:2604.18508v1 Announce Type: cross Abstract: Many recent document embedding models are trained on document-as-image representations, embedding rendered pages as images rather than the underlying
arXiv:2510.00761v5 Announce Type: replace Abstract: Large language model (LLM) unlearning aims to surgically remove the influence of undesired data or knowledge from an existing model while preserving
arXiv:2602.14122v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have recently achieved remarkable progress in vision-language understanding. Yet, human perception is inher
arXiv:2604.18336v1 Announce Type: cross Abstract: Indoor robot navigation is often compromised by glass surfaces, which severely corrupt depth sensor measurements. While foundation models like Depth A
arXiv:2604.18320v1 Announce Type: new Abstract: Self-evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo-label-based methods suffer from progressive quality degr
arXiv:2508.07809v5 Announce Type: replace Abstract: Reinforcement learning with verifiable reward (RLVR) has become a promising paradigm for post-training large language models (LLMs) to improve their
arXiv:2511.17171v4 Announce Type: replace Abstract: Predicting wildfire risk is a reasoning-intensive spatial problem that requires the integration of visual, climatic, and geographic factors to infer
Flexible Aspect Ratios ChatGPT Images 2.0 supports aspect ratios as wide as 3:1 and as tall as 1:3. It can generate outputs that are ready to fit the formats you need, from wide banners and presentati
arXiv:2604.17785v1 Announce Type: new Abstract: Unlearning in large language models (LLMs) has emerged as a promising safeguard against adversarial behaviors. When the forgetting loss is applied unifo
arXiv:2604.16648v1 Announce Type: new Abstract: In this work, we present FRIGID, a framework with a novel diffusion language model that generates molecular structures conditioned on mass spectra via i
arXiv:2601.16397v2 Announce Type: replace Abstract: Deploying multimodal large language models (MLLMs) for clinical summarization demands not only fluent generation but also transparency about where e
arXiv:2603.14389v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed a leap in Large Language Model (LLM) reasoning, yet its optimization dynamics re
arXiv:2601.09173v4 Announce Type: replace-cross Abstract: Representational similarity analysis and related methods have become standard tools for comparing the internal geometries of neural networks a
arXiv:2506.07160v3 Announce Type: replace Abstract: Recent progress in large language models (LLMs) has boosted mathematical reasoning, yet geometry remains challenging where auxiliary construction is
arXiv:2604.17822v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continuously acquire new categories while preserving previously learned knowledge. Recently, Contrastive Langua