Knowledge catalogue
arXiv:2605.19539v1 Announce Type: new Abstract: Geometric foundation models hold promise for unconstrained dense geometry prediction from uncalibrated images. However, in current feed-forward designs,
arXiv:2602.11767v3 Announce Type: replace Abstract: Advances in large language models (LLMs) are driving a shift toward using reinforcement learning (RL) to train agents from iterative, multi-turn int
We’re expanding our partnership with @SpaceX, and will be scaling up on GB200 capacity in Colossus 2 throughout June. Appreciate @elonmusk and the team helping us find good homes for the Claudes. In t
What hardware actually powers open-source AI? Not benchmarks. Not vendor marketing. Real-world community usage. We’re launching @huggingface Hardware: → trending GPUs & CPUs → VRAM distribution → infe
arXiv:2605.20151v1 Announce Type: new Abstract: The proliferation of generative artificial intelligence has given rise to an interactive learning environment, where model parameters are continuously u
arXiv:2605.19839v1 Announce Type: new Abstract: Preference alignment aims to guide generative models by learning from comparisons between preferred and non-preferred samples. In practice, most existin
arXiv:2605.19662v1 Announce Type: new Abstract: Tabular foundation models based on pretrained prior-data fitted networks~(PFNs) have shown strong generalization on diverse tabular tasks, but they are
arXiv:2602.03924v2 Announce Type: replace-cross Abstract: Deep learning has revolutionized weather forecasting, but many challenges remain, including climate modeling. Moreover, the current landscape
arXiv:2605.16430v1 Announce Type: cross Abstract: Scaling LLMs requires tremendous computational resources, and recent advances in AI have gone hand in hand with massive amounts of capital expenditure
arXiv:2605.17808v1 Announce Type: new Abstract: We develop a unified theoretical framework for data-free one-step sampling from unnormalized target distributions based on Wasserstein gradient flows. F
arXiv:2605.17546v1 Announce Type: cross Abstract: Understanding galaxy morphology evolution across cosmic time requires models that can generate realistic galaxy populations conditioned on redshift. I
arXiv:2605.16299v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at code generation but remain heavily reliant on large-scale annotated solutions and verification-based supervision
arXiv:2605.16817v1 Announce Type: cross Abstract: Learned image compression has achieved competitive rate-distortion performance, but very-low-bitrate reconstruction remains difficult because the tran
arXiv:2601.09722v2 Announce Type: replace-cross Abstract: In this work, we present an annotation framework that demonstrates how a multilingual LLM pretrained on a large corpus can be used as a teache
arXiv:2605.17380v1 Announce Type: new Abstract: We present the Agentic AI Detection and Response (ADR) system, the first large-scale, production-proven enterprise framework for securing AI agents oper
arXiv:2605.18733v1 Announce Type: new Abstract: Autoregressive video generation has improved rapidly in visual fidelity and interactivity, but it still suffers from long-term inconsistency and memory
arXiv:2602.03955v2 Announce Type: replace Abstract: While large language model (LLM) multi-agent systems achieve superior reasoning performance through iterative debate, practical deployment is limite
arXiv:2605.17903v1 Announce Type: new Abstract: We automatically generate feedback causal fuzzy cognitive maps (FCMs) from text by teaching large-language-model agents to break the text into overlappi
arXiv:2602.07715v2 Announce Type: replace Abstract: Recovering a signal from its degraded measurements is a long standing challenge in science and engineering. Recently, zero-shot diffusion based meth
arXiv:2605.17071v1 Announce Type: new Abstract: Radiology report generation (RRG) aims to automatically produce clinically accurate textual reports from medical images. Existing methods predominantly
arXiv:2605.17887v1 Announce Type: cross Abstract: We propose OASIS, an outlier- and sink-aware technique built on inter-layer null signaling. As AttnResidual architectures introduce an additional dept
B9222 is a llama.cpp release that adds support for the TRI (Triangle) operation in the Hexagon HTP backend with HVX kernel additions . The release includes optimizations for Hexagon hardware accelerat
arXiv:2605.17608v1 Announce Type: cross Abstract: Construction projects frequently experience schedule delays and forecasting uncertainty due to variability in labor productivity, material availabilit
arXiv:2605.17435v1 Announce Type: new Abstract: Biomedical question answering often requires decisions from retrieved literature whose relevance, quality, and support for candidate answers are uneven.
arXiv:2605.17508v1 Announce Type: cross Abstract: Split Federated Learning (SFL) enables privacy-preserving collaborative training by partitioning models between clients and a server. However, under n
arXiv:2605.17270v1 Announce Type: new Abstract: Modern visual object trackers show impressive results on general targets, yet their performance drops substantially when dealing with scene text. Althou
arXiv:2605.16392v1 Announce Type: cross Abstract: Multiple instance learning (MIL) is the dominant framework for whole-slide image analysis in computational pathology, typically combining a frozen pat
arXiv:2602.16699v3 Announce Type: replace-cross Abstract: LLM agents are deployed in environments where they must interact to acquire information. In these scenarios, the agent must reason about inher
arXiv:2604.01674v2 Announce Type: replace Abstract: Model merging aims to integrate multiple expert models into a single model that inherits their complementary strengths without incurring the inferen
arXiv:2605.17110v1 Announce Type: new Abstract: Query clustering organizes queries into groups that reflect shared latent capability demands, enabling capability-aware LLM evaluation. Existing cluster
arXiv:2307.09575v2 Announce Type: replace-cross Abstract: This paper investigates causal influences between agents linked by a social graph and interacting over time. In particular, the work examines
arXiv:2605.16388v1 Announce Type: new Abstract: Semantic communication (SC) aims to reduce transmission overhead by conveying task-relevant information rather than raw data. However, existing SC appro
arXiv:2605.17410v1 Announce Type: new Abstract: Token economics has emerged as a useful lens for understanding resource allocation, value creation, and pricing in large language model systems. While r
arXiv:2605.18202v1 Announce Type: cross Abstract: Neuro-Symbolic Concept-based Models (NeSy-CBMs) are a family of architectures that integrate neural networks with symbolic reasoning for enhanced reli
arXiv:2605.16829v1 Announce Type: new Abstract: Discrete diffusion models are a powerful, emerging paradigm for code generation. They construct programs through iterative refinement of partially corru
arXiv:2605.17144v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models leverage powerful perceptual priors from web-scale Vision-Language Model (VLM) pre-training, yet they remain surpr
arXiv:2605.17559v1 Announce Type: cross Abstract: Large-scale hypothesis testing is central to modern science, where controlling the False Discovery Rate (FDR) has become the standard approach to mana
arXiv:2605.17826v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) excel at multimodal reasoning, yet it remains unclear whether their answers are grounded in visual evidence or driven by
arXiv:2605.18621v1 Announce Type: cross Abstract: Spatial intelligence requires multimodal large language models (MLLMs) to move beyond single-view perception and reason consistently about objects, vi
arXiv:2603.08145v2 Announce Type: replace-cross Abstract: Preference-based alignment methods (e.g., RLHF, DPO) typically optimize a single scalar objective, implicitly averaging over heterogeneous hum
arXiv:2605.18753v1 Announce Type: cross Abstract: Current hierarchical attention methods, such as NSA and InfLLMv2, select the top-k relevant key-value (KV) blocks based on coarse attention scores and
arXiv:2605.16989v1 Announce Type: new Abstract: Individualized treatment selection with continuous actions requires accurate causal response estimation in decision-relevant regions, rather than unifor
arXiv:2510.21712v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs) through the dyna
arXiv:2605.17307v1 Announce Type: cross Abstract: This study develops and evaluates a deep reinforcement learning framework for dynamic portfolio allocation across global equity markets. The Soft Acto
arXiv:2605.16519v1 Announce Type: new Abstract: Accurate polyp segmentation in colonoscopy is essential for early colorectal cancer detection, yet real-world clinical environments pose persistent chal
arXiv:2605.17118v1 Announce Type: new Abstract: Differentiable optimization layers are traditionally integrated in predict-then-optimize frameworks where a neural model estimates parameters that subse
arXiv:2605.17107v1 Announce Type: cross Abstract: We introduce a novel framework for uncertainty quantification of solution operators associated with stochastic partial differential equations (SPDEs).
arXiv:2602.08354v5 Announce Type: replace Abstract: Recent advancements in large reasoning models (LRMs) have greatly improved their capabilities on complex reasoning tasks through Long Chains of Thou
arXiv:2605.16990v1 Announce Type: new Abstract: While 2D diffusion models have achieved remarkable success in identity-preserving personalization, extending this capability to 3D assets remains a sign
arXiv:2605.16737v1 Announce Type: cross Abstract: End-to-End (E2E) autonomous driving models have shown growing capability in recent years, with performance improving on increasingly challenging bench
arXiv:2605.18023v1 Announce Type: new Abstract: Open-Vocabulary Object Detection (OVD) models break the limitations of closed-set detection, enabling the iden- tification of unseen categories through
arXiv:2605.18512v1 Announce Type: new Abstract: In-context learning (ICL) is highly sensitive to which demonstrations appear in the prompt, but selecting them is expensive because the space of possibl
arXiv:2605.17262v1 Announce Type: new Abstract: Despite extensive efforts on egocentric video datasets and benchmarks, understanding users' internal states, which is crucial for enabling seamless AI a
arXiv:2511.07288v2 Announce Type: replace-cross Abstract: Learning complex policies with Reinforcement Learning (RL) is often hindered by instability and slow convergence, a problem exacerbated by the
arXiv:2605.17591v1 Announce Type: new Abstract: Aggregate object detection metrics inherently mask catastrophic and repeatable failures in operationally critical, long-tail minority classes. This pape
arXiv:2605.17909v1 Announce Type: new Abstract: As autonomous agentic systems scale across regulated critical infrastructures, the lack of mechanistic, hardware-rooted enforcement for high-frequency p
arXiv:2603.11689v2 Announce Type: replace Abstract: Frontier Multimodal Large Language Models (MLLMs) exhibit remarkable capabilities in Visual-Language Comprehension (VLC) tasks. However, they are of
arXiv:2605.16975v1 Announce Type: cross Abstract: Electrocardiogram (ECG) foundation models pretrained on typical diagnostic 10-second ECG segments, have demonstrated strong transferability across a r
arXiv:2605.17115v1 Announce Type: new Abstract: Biased manipulation of facts across regional and national media outlets complicates misinformation detection in diverse landscapes like India. This pape
arXiv:2605.17447v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have shown strong promise on Optical Character Recognition (OCR), yet the sheer number of visual tokens required to enco