Knowledge catalogue
arXiv:2610.17146v1 Announce Type: cross Abstract: Bayesian predictive regions account for parameter uncertainty but can be poorly calibrated under model misspecification, even with expensive full MCMC
arXiv:2610.16761v1 Announce Type: cross Abstract: LLM-based information systems need safety guardrails. Most guardrails classify the prompt alone. If the prompt looks harmful, the system rejects it be
arXiv:2610.14835v1 Announce Type: new Abstract: Branch-and-bound (BnB) solves mixed-integer linear programs by constructing and exploring a search tree. Node selection and branching are two recurrent
arXiv:2610.18325v1 Announce Type: new Abstract: Virtual footwear try-on (VFTON), a critical yet underexplored area in virtual try-on (VTON), aims to synthesize faithful try-on results given diverse fo
arXiv:2603.02475v2 Announce Type: replace Abstract: Skin tone classification is a fundamental task in computer vision, with applications in quantifying medical features, assessing demographic represen
arXiv:2610.17323v1 Announce Type: new Abstract: Face recognition systems are deployed widely enough that a photograph shared online can be matched without its subject's consent. Generator-based protec
arXiv:2610.14651v1 Announce Type: new Abstract: The recent Segment Anything Model 3 (SAM3) extends powerful promptable segmentation from spatial cues to concepts, raising the prospect of a single foun
arXiv:2606.16767v2 Announce Type: replace Abstract: Existing image editing methods can be generally categorized into textual instruction-based and visual prompt-based ones. Textual instructions are se
arXiv:2610.16977v1 Announce Type: new Abstract: Visual in-context learning (ICL) enables vision-language models (VLMs) to adapt at test time via demonstration examples, but its effectiveness depends c
arXiv:2610.18649v1 Announce Type: new Abstract: Message passing neural networks have shown remarkable performance in algorithmic and constraint reasoning, due to their ability to encode the structure
arXiv:2610.15128v1 Announce Type: new Abstract: Modern AI systems increasingly combine large language models (LLMs) with human experts, using the LLM to handle routine queries while deferring uncertai
arXiv:2603.18091v2 Announce Type: replace Abstract: Test-time sampling can improve Vision-Language-Action (VLA) policies, but only if the model can efficiently select a good action from multiple candi
arXiv:2603.25063v2 Announce Type: replace-cross Abstract: Recent agentic systems demonstrate that large language models can generate complex codebases from natural language prompts. Despite this progr
arXiv:2610.17657v1 Announce Type: new Abstract: An embodied brain, or System 2, must turn an abstract goal into actions while discovering relevant scene conditions and checking whether executed skills
arXiv:2610.17893v1 Announce Type: cross Abstract: Effective collaboration among large language model (LLM) agents in multi-agent systems requires sharing relevant evidence, requesting missing informat
arXiv:2610.17726v1 Announce Type: new Abstract: Large language model (LLM) agents excel at complex, multi-step tasks but suffer from catastrophic forgetting during continual learning--acquiring new sk
arXiv:2603.18572v2 Announce Type: replace-cross Abstract: Deep unrolled models (DUM) have become the state-of-the-art for accelerated MRI reconstruction, yet their robustness under domain shift remain
arXiv:2610.16595v1 Announce Type: cross Abstract: Surface-based cortical analysis is useful in many applications as it is a natural way to model the highly folded structure of the cerebral cortex, all
arXiv:2204.09334v4 Announce Type: replace-cross Abstract: Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle
Welcome to Mistral, New Material. Let’s bring design to mission-critical AI. New Material joins @MistralAI to design the frontier. Over the last 6 years we've teamed up with ambitious organisations tr
arXiv:2610.15778v1 Announce Type: new Abstract: When a policy is trained with GRPO against a frozen, reference-free LLM judge, the judge accepts more of its answers, but in a post-hoc analysis we dete
Learn how Azure Content Understanding prebuilt analyzers combine domain knowledge with grounding and confidence, and what Advanced Contextualization preview benchmarks show about quality, modeled cost
After months sitting on the sidelines of the frontier AI race, Google has finally launched its long-awaited Gemini 4 Argon model, claiming it can compete with the best the industry has to offer. Googl
I’m planning to get a Mac mini mainly to run local LLMs and AI agents 24/7. I plan to run models up to 27B parameters with Q4 quantization, and maybe not for coding, maybe just for everday task and mo
I am an independent researcher. Due to budget constraints, I shifted my 21B MoE pre-training—which I had originally planned to run on 64 H100s—to a single NVIDIA GH200 ($2.29 per hour), and I have com
arXiv:2610.13480v1 Announce Type: cross Abstract: Nonlinear model predictive control (NMPC) can be applied to large nonlinear systems with complex objectives while enforcing constraints, but the cost
I designed a node to align any pair of images trying to correct the pixel drift on Qwen Image 2.1 https://github.com/zuanfilm/Qalign workflow This node don't need any model or text encoder Qalign is a
arXiv:2603.07427v3 Announce Type: replace Abstract: As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costl
arXiv:2610.13607v1 Announce Type: new Abstract: Negative polarity items (NPIs), such as 'anymore' and 'ever', are grammatical only in select environments, predominantly in the context of negation. Bec
arXiv:2610.12644v1 Announce Type: cross Abstract: Backdoor Attacks on Large Language Models (LLMs) typically follow a trigger-response pattern, where satisfying a trigger condition activates a single
arXiv:2610.13304v1 Announce Type: new Abstract: DAG structures can be learned from observations by incorporating acyclicity via smooth constraints, such as DAGMA's log-determinant function. Typically,
arXiv:2610.12658v1 Announce Type: new Abstract: We develop a data-driven sensor-placement framework and apply it to aerosol monitoring in the United Kingdom (UK). A trained convolutional Gaussian neur
arXiv:2509.23003v3 Announce Type: replace-cross Abstract: Mechanics-inspired neural dynamical models improve interpretability and physical consistency, but they usually assume a known configuration sp
arXiv:2610.13328v1 Announce Type: new Abstract: Serving a Mixture-of-Experts (MoE) model pays for expert precision twice: the resident footprint pays for every expert the router can reach, while each
arXiv:2610.13021v1 Announce Type: new Abstract: Continual learning requires models to accumulate past experience and transfer it to new tasks. Despite substantial research on continual learning, exist
arXiv:2610.13066v1 Announce Type: cross Abstract: The optimization objective for e-commerce advertising creatives has shifted from aesthetic quality to online business metrics like click-through rate
arXiv:2610.12493v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) tools have often been presented as methods to increase trust in machine learning models by making aspects of t
arXiv:2610.12987v1 Announce Type: new Abstract: If a second text corpus were collected from the same population, how much would a predictor's outputs change? We study this question for masked-text rec
arXiv:2610.06411v2 Announce Type: replace Abstract: Agentic systems increasingly coordinate molecular-design tools, but it is unclear which layer of the stack limits outcomes on real projects. We deve
arXiv:2610.13768v1 Announce Type: cross Abstract: What can an adversary recover from a model trained with DP-SGD? Its (arepsilon,elta) guarantee does not answer this question directly. We derive a dis
arXiv:2610.12528v1 Announce Type: new Abstract: Continuous glucose monitoring (CGM) foundation models encode each day as one vector that mixes who the person is, what happened that day and how the sen
arXiv:2511.01047v4 Announce Type: replace-cross Abstract: Automated program repair (APR) has recently shifted toward large language models and agent-based systems, while prior work shows that reposito
History never repeats itself but it often rhymes Hint: Transformer rhymes with Diffusion Transformer We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinba
arXiv:2610.13040v1 Announce Type: cross Abstract: Consumer rotating devices, including washing machines, air conditioners, electric fans, robotic vacuum cleaners, and home fitness equipment, increasin
arXiv:2610.13534v1 Announce Type: new Abstract: Long-horizon embodied agents must maintain task-relevant information, coordinate heterogeneous robots, and adapt their decisions as execution unfolds. E
arXiv:2610.13305v1 Announce Type: new Abstract: We introduce MORAD, an online reinforcement learning framework for backbone-conditioned RNA diffusion models that trains a single shared policy across d
arXiv:2610.12879v1 Announce Type: new Abstract: Diffusion language models generate tokens in parallel, but still require multiple sampling steps to produce high-quality text. In this paper, we investi
arXiv:2610.13370v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have become the standard architecture for frontier LLMs. The multiple parallel experts in each transformer block theoret
arXiv:2610.13635v1 Announce Type: new Abstract: Hypergraph Neural Networks (HNNs), designed to model higher-order relations beyond pairwise interactions, typically rely either on fixed structural weig
arXiv:2610.13550v1 Announce Type: new Abstract: Recent speech LLMs can leverage rich paralinguistic cues, enabling more complex and personalized interactions. However, little work investigates how thi
arXiv:2610.12885v1 Announce Type: new Abstract: Federated Direct Preference Optimization (FedDPO) enables privacy-preserving alignment of language models across distributed agents without centralizing
arXiv:2610.13644v1 Announce Type: cross Abstract: Selecting a subset of features (columns) from a tabular dataset is a ubiquitous challenge in real-world applications. Industry experts seek to minimiz
separating artifacts from the agent makes sense for sharing and cross-AI collaboration No vendor lock-in for your artifacts. Sites, decks, 3D games, reports, dashboards and galleries. Artifacts on Fly
arXiv:2610.13207v1 Announce Type: new Abstract: Fake news detection under low resource conditions is challenged by the scarcity of labeled data and the difficulty of capturing subtle deceptive pattern
arXiv:2610.12975v1 Announce Type: new Abstract: We present SmartSalesBench, the first multi-turn conversational benchmark explicitly designed to evaluate large language model (LLM) sales agents agains
arXiv:2610.13812v1 Announce Type: new Abstract: Flow-matching and diffusion models are increasingly used as priors for linear inverse problems, however, most methods are bespoke to one class of trajec
arXiv:2610.13835v1 Announce Type: new Abstract: An ideal (language) model must continually update its knowledge. Existing work largely focuses on making these updates efficient after training. We inve
arXiv:2610.13036v1 Announce Type: new Abstract: Evaluating deep learning optimizer is challenging because confounding factors such as hyperparameter tuning and optimizer-model co-design often obscure
arXiv:2610.13058v1 Announce Type: new Abstract: Large language model agents increasingly operate in long-horizon environments where early mistakes can trigger cascading failures and trap agents in dea
As a business intelligence user, this probably sounds familiar: Most dashboards only provide you with a standardized canvas and a fixed library of charts, and the “analysis” consists of a CSV export t