Knowledge catalogue
arXiv:2605.16423v1 Announce Type: new Abstract: Network quantization has emerged as one of the most practical model compression techniques, which significantly reduces a model's memory and compute con
arXiv:2605.18593v1 Announce Type: cross Abstract: Open-vocabulary embodied AI agents increasingly rely on vision-language models such as CLIP for object perception and task grounding. However, the sha
arXiv:2603.03308v2 Announce Type: replace-cross Abstract: How does the conversational past of large language models (LLMs) influence their future performance? Recent work suggests that LLMs are affect
open sourcing Marlin-2B 🐟 a tiny VLM to extract structured information from videos Marlin is finetuned for two questions devs want to ask in their videos: what is happening, and when? Best open model
arXiv:2605.18349v1 Announce Type: cross Abstract: Occupancy estimation and crowd counting are critical tasks in designing smart and efficient public transport vehicles. Given that public transport loa
arXiv:2602.18227v2 Announce Type: replace Abstract: Accurate AC power flow (AC-PF) prediction under domain shift is critical when models trained on medium-voltage (MV) grids are deployed on high-volta
arXiv:2605.18352v1 Announce Type: new Abstract: Presupposition projection in conditionals is central to theories of meaning and pragmatics, yet it remains largely unevaluated in large language models.
arXiv:2605.17681v1 Announce Type: new Abstract: Humanoid and legged robots interact with the environment through intermittent contacts, making accurate motion estimation fundamentally dependent on rea
arXiv:2605.16604v1 Announce Type: new Abstract: Efficient agentic systems should incur expensive frontier-model costs only on decisions where a cheaper local model is likely to fail. Existing LLM casc
arXiv:2605.16767v1 Announce Type: new Abstract: Multi-label legal annotation requires assigning multiple labels from large, evolving taxonomies to long, fact-intensive documents, often under limited s
arXiv:2603.13708v2 Announce Type: replace Abstract: In this paper, we explore text-guided image editing in the remote sensing domain using generative modeling. We propose rsedit, a collection of model
arXiv:2605.17610v1 Announce Type: cross Abstract: The rapid growth of online video platforms and AI-generated content has made reliable video guardrails a key challenge for safety and real-world deplo
arXiv:2605.18613v1 Announce Type: cross Abstract: Latent representations are at the heart of the majority of modern generative models. In the audio domain they are typically produced by a neural-audio
arXiv:2603.02218v2 Announce Type: replace-cross Abstract: Large language models (LLMs) make it plausible to build systems that improve through self-evolving loops, but many existing proposals are bett
arXiv:2602.08167v2 Announce Type: replace-cross Abstract: Embodied Chain-of-Thought (CoT) reasoning has significantly enhanced Vision-Language-Action (VLA) models, yet current methods rely on rigid te
arXiv:2512.17843v4 Announce Type: replace-cross Abstract: By evaluating Large Language Models (LLMs) through uniform, text-only interfaces, current academic benchmarks obscure how the unique designs a
arXiv:2605.17842v1 Announce Type: new Abstract: Autoregressive language models execute Transformer layers sequentially, creating a latency bottleneck that is not removed by conventional tensor or pipe
This X thread from Google AI showcases practical and creative applications of Gemini Omni, Google's multimodal AI model, as demonstrated and shared by the user community. The thread appears to be a cu
arXiv:2605.18603v1 Announce Type: new Abstract: Vision-Language Models (VLMs) deployed as situated agents in high-resolution visual environments require active perception -- the ability to dynamically
arXiv:2605.18656v1 Announce Type: cross Abstract: Federated Learning is a leading framework for training ML and AI models collaboratively across numerous user devices or databases. We study the trade-
arXiv:2605.16470v1 Announce Type: cross Abstract: Adapting large language models (LLMs) to downstream tasks via full fine-tuning is increasingly impractical due to its computational and memory demands
arXiv:2605.16581v1 Announce Type: new Abstract: Masked language modeling (MLM) is the standard objective for training protein language models, typically implemented by randomly masking individual resi
Google's I/O 2026 keynote today was once again full of AI-related announcements including a new family of Gemini 3.5 AI models, new features for Search and Gmail, and updates about its Project Aura sm
arXiv:2605.16508v1 Announce Type: cross Abstract: As agent systems scale, skills accumulate into large reusable libraries, yet their scaling laws remain poorly understood. Across 15 frontier LLMs, 1,1
arXiv:2605.16790v1 Announce Type: cross Abstract: Tool use enables large language models to solve complex tasks through sequences of API calls, yet existing reinforcement learning approaches fail to s
Today, we launched a brand-new intelligent Search box. Here's what that means: An upgrade to the Search experience with our most advanced Gemini 3.5 models, bringing with them our latest agentic capab
ComfyUI announced the availability of Anthropic's Claude AI model integrated into their platform, allowing users to utilize Claude's capabilities within the ComfyUI interface. The announcement include
arXiv:2605.17936v1 Announce Type: new Abstract: Recent works have illustrated that modern NLP models trained for diverse tasks ranging from sentiment analysis to language generation succumb to univers
arXiv:2605.16719v1 Announce Type: cross Abstract: Scientific and technological frontiers advance through punctuated dynamics, yet the principles governing these dynamics remain poorly understood. Here
arXiv:2605.17626v1 Announce Type: new Abstract: Test-time scaling is an important mechanism for improving large language models, especially on tasks with deterministic verifiers. Code translation is a
arXiv:2601.06943v2 Announce Type: replace-cross Abstract: In real-world video question answering scenarios, videos often provide only localized visual cues, while verifiable answers are distributed ac
arXiv:2509.22510v3 Announce Type: replace Abstract: Alignment of Large Language Models (LLMs) is the ability to satisfy desired objectives during generation, which is critical for trustworthy deployme
arXiv:2605.17146v1 Announce Type: cross Abstract: Digital twins (DTs) rely on continuous synchronization between physical systems and their virtual counterparts through online parameter estimation und
arXiv:2510.15221v2 Announce Type: replace Abstract: Affective computing has matured rapidly in laboratory settings, yet no prior dataset combines (i) months-to-years of duration, (ii) a naturalistic w
arXiv:2605.18445v1 Announce Type: cross Abstract: Humans can approach complex visual problems by mentally simulating intermediate visual steps, rather than reasoning through language alone. Inspired b
arXiv:2605.16403v1 Announce Type: new Abstract: Despite rapid progress in video-capable MLLMs, we find that their apparent audio understanding in videos is often vision-driven: models rely on visual c
arXiv:2605.18150v1 Announce Type: new Abstract: Diffusion models (DMs) are widely used for text-to-image generation, but their strong generative capabilities also raise concerns about unsafe or undesi
arXiv:2605.16402v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have revolutionized GUI automation, yet their efficacy is largely established on idealized, single-layer interf
arXiv:2605.15617v1 Announce Type: cross Abstract: Large language model (LLM) training today runs on clusters spanning thousands of GPUs. While this scale enables rapid model advances, developing, debu
arXiv:2605.15700v1 Announce Type: new Abstract: Automated machine learning pipelines increasingly produce models whose predictions must be explained to end users, auditors, and downstream decision sys
arXiv:2605.15864v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) often produce self-reflective statements like 'let me check the figure again' during reasoning. Do such statements trigg
arXiv:2507.16806v2 Announce Type: replace-cross Abstract: When language models (LMs) are trained via reinforcement learning (RL) to generate natural language 'reasoning chains', their performance impr
arXiv:2605.15588v1 Announce Type: new Abstract: As large language models (LLMs) are deployed in consequential settings such as medical question answering and legal reasoning, the ability to estimate w
Composer 2.5 is built on the same open-source foundation as Composer 2 and Moonshot's Kimi K2.5 model. This indicates technical alignment and shared architecture between these AI systems developed by
arXiv:2605.16207v1 Announce Type: new Abstract: Effective tutoring requires distinguishing optimal, valid but suboptimal, and incorrect student solutions, a distinction central to intelligent tutoring
arXiv:2605.15775v1 Announce Type: new Abstract: Continual learning (CL) aims to train models sequentially over multiple domains without forgetting previously learned knowledge. However, existing CL me
arXiv:2505.06982v3 Announce Type: replace Abstract: Accurate and privacy-preserving diagnosis of ophthalmic diseases remains a critical challenge in medical imaging, particularly given the limitations
Dell + Nvidia Media 'We give you model choice, without infrastructure chaos' — @MichaelDell, live from #DellTechWorld 🎤 Kimi K2.6, DeepSeek V4 Pro, GLM 5.1, MiniMax M2.7 & DeepSeek V4 Flash are now on
arXiv:2605.15518v1 Announce Type: new Abstract: The effective detection and governance of Large Language Model (LLM) generated content has become increasingly critical due to the growing risk of misus
arXiv:2605.16003v1 Announce Type: new Abstract: Autoregressive video diffusion models enable open-ended generation through local attention and KV caching. However, existing training-free long-video op
arXiv:2605.16008v1 Announce Type: new Abstract: Plaque assays remain the gold standard readout of virus infectivity; however, plaque counting from plate images is labor-intensive and prone to inter-op
arXiv:2511.03260v2 Announce Type: replace Abstract: Medical image segmentation models struggle to achieve efficient global context modeling and long-range dependency reasoning under practical computat
arXiv:2603.16011v2 Announce Type: replace-cross Abstract: Large language model (LLM) coding agents increasingly operate at the repository level, motivating benchmarks that evaluate their ability to op
arXiv:2605.15412v1 Announce Type: cross Abstract: Modern quantitative trading increasingly relies on systematic models to extract predictive signals from large-scale financial data, where alpha factor
arXiv:2605.15295v1 Announce Type: cross Abstract: Machine learning (ML) algorithms are increasingly deployed in high-stakes decision-making domains such as loan approvals, hiring, and recidivism predi
arXiv:2605.15673v1 Announce Type: cross Abstract: We present a highly detailed instance segmentation model for delineating individual tree crowns in natural broadleaf forests using aerial imagery acqu
arXiv:2605.15486v1 Announce Type: cross Abstract: This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The LLM is fed
arXiv:2605.16211v1 Announce Type: new Abstract: Traditional scientific modeling typically begins with fixed, instance-wise effective equations and then carries out equation-specific analysis and compu
arXiv:2605.15224v1 Announce Type: new Abstract: Large language model-based agents make mistakes, yet critique can often guide the same model toward correct behavior. However, when critique is removed,
arXiv:2510.05676v2 Announce Type: replace Abstract: Graph-based methods are becoming increasingly popular in machine learning due to their ability to model complex data and relations. Insurance fraud