Knowledge catalogue
arXiv:2602.10056v2 Announce Type: replace Abstract: We introduce WildCat, a high-accuracy, low-cost approach to compressing the attention mechanism in neural networks. While attention is a staple of m
arXiv:2606.02482v1 Announce Type: new Abstract: While video streaming understanding has made significant strides, real-world applications, such as live sports broadcasting, autonomous driving, and mul
arXiv:2602.03896v2 Announce Type: replace-cross Abstract: Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samp
arXiv:2605.30510v1 Announce Type: cross Abstract: Brain cancer's severity necessitates precise brain tumor segmentation, which is crucial for effective brain tumor diagnosis. Manual identification, bu
arXiv:2505.20840v2 Announce Type: replace Abstract: We revisit DropEdge, a data augmentation technique for GNNs which randomly removes edges to expose diverse graph structures during training. While b
arXiv:2605.30599v1 Announce Type: cross Abstract: Medical knowledge is continuously evolving. This creates a need to update or selectively forget information encoded in already-trained medical LLMs. M
arXiv:2602.01399v2 Announce Type: replace-cross Abstract: The Shapley value is a ubiquitous framework for attribution in machine learning, encompassing feature importance, data valuation, and causal i
arXiv:2502.15224v2 Announce Type: replace-cross Abstract: Interactive discovery requires agents to maintain and update structured beliefs over many rounds of feedback. Before evaluating agents in nois
arXiv:2605.31539v1 Announce Type: new Abstract: Postoperative pancreatic fistula (POPF) is a serious complication after pancreatic resection, increasing morbidity, hospital stay, and healthcare costs.
arXiv:2605.30860v1 Announce Type: cross Abstract: A central aim of deep learning theory is to characterize how neural networks make predictions in the regime of simultaneously large model and training
arXiv:2605.30826v1 Announce Type: cross Abstract: Biomedical NER is deceptively simple for modern LLMs: plausible biomedical mentions are easy to surface, but corpus-convention correctness depends on
arXiv:2603.09221v2 Announce Type: replace Abstract: Associative memory has long underpinned the design of sequential models. Beyond recall, humans reason by projecting future states and selecting goal
arXiv:2605.30448v1 Announce Type: cross Abstract: Black-box LLM distillation is usually evaluated as an output-matching problem: a student is considered successful when its responses are semantically
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:2605.30919v1 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:2605.30686v1 Announce Type: cross Abstract: ReAct agents that interleave chain-of-thought reasoning with tool calls are increasingly deployed for real tasks such as scheduling, file retrieval, a
arXiv:2506.11653v3 Announce Type: replace-cross Abstract: Dataset bias often leads deep learning models to exploit spurious correlations instead of task-relevant signals. We introduce the Standard Ant
arXiv:2602.10324v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly deployed in social and strategic scenarios, it becomes critical to understand where and why their b
arXiv:2605.30861v1 Announce Type: new Abstract: Post-training for reasoning models typically combines supervised fine-tuning with reinforcement learning from verifiable rewards, most commonly with GRP
arXiv:2605.31228v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards is an effective route for post-training to strengthen the reasoning capability of large language models
arXiv:2605.31562v1 Announce Type: new Abstract: RNA sequencing produces rich and diverse datasets of gene expression, offering compelling insights into cellular state and function that have many appli
arXiv:2605.30924v1 Announce Type: new Abstract: MLLM-powered embodied agents deployed in real-world environments encounter physical hazards. However, existing approaches lack explicit mechanisms for i
arXiv:2510.04500v2 Announce Type: replace Abstract: This work demonstrates how increasing the number of neurons in a network without increasing its total number of non-zero parameters improves perform
arXiv:2510.16138v2 Announce Type: replace Abstract: Existing expert merging strategies for Sparse Mixture of Experts (SMoE) typically rely on input-dependent or input-independent averaging of expert p
arXiv:2601.19448v2 Announce Type: replace Abstract: Deep Neural Networks remain inherently vulnerable to backdoor attacks. Traditional test-time defenses largely operate under the paradigm of internal
arXiv:2602.24210v2 Announce Type: replace-cross Abstract: Large reasoning models (LRMs) produce reasoning traces (RTs) that often contain sensitive information. These leaky thoughts are difficult to c
arXiv:2605.31042v1 Announce Type: cross Abstract: LLM agents are evolving from conversational chatbots to operational tools in real-world workspaces. In local agentic harnesses, an LLM can read and wr
arXiv:2408.16457v5 Announce Type: replace Abstract: Hypergraphs are powerful mathematical structures that can model complex, high-order relationships in various domains, including social networks, bio
arXiv:2511.15692v2 Announce Type: replace Abstract: This paper introduces SS-MixNet, a lightweight and effective deep learning model for hyperspectral image (HSI) classification. The architecture inte
arXiv:2601.21645v2 Announce Type: replace Abstract: We investigate the relation between end-to-end equivariance and layerwise equivariance in deep neural networks. We prove the following: For a networ
arXiv:2605.30596v1 Announce Type: new Abstract: Independently trained neural models typically converge to incompatible latent representations, creating a fundamental barrier to highly modular AI syste
arXiv:2605.30917v1 Announce Type: cross Abstract: As large-scale visual-document corpora such as arXiv papers and enterprise PDFs continue to grow, visual-document retrieval has gained increasing atte
Intel Corp. today announced that more than 130 design engagements for its Series 3 processor family for edge artificial intelligence and edge computing designs and also unveiled a new open-source fram
arXiv:2604.02969v2 Announce Type: replace-cross Abstract: The natural gradient method is a central tool for statistical optimisation, but its broader application is hindered by the assumption of a Euc
arXiv:2605.31096v1 Announce Type: new Abstract: While visually grounded Chain-of-Thought (CoT) has emerged as a promising paradigm to enhance fine-grained perception in multimodal large language model
arXiv:2603.08721v2 Announce Type: replace-cross Abstract: New AI accelerators with novel instruction set architectures (ISAs) often require developers to manually craft low-level kernels, a time-consu
arXiv:2602.02220v2 Announce Type: replace Abstract: Language-conditioned goal navigation (LGN) requires agents to locate user-specified targets without step-by-step guidance. However, existing benchma
Last week we revamped Liteparse to be the fastest PDF parser out there ⚡️ An underrated part of liteparse is it doesn't just give you text. It gives you bounding boxes that a coding agent can use to p
arXiv:2605.31276v1 Announce Type: new Abstract: Accurately modeling crop response to Nitrogen (N) fertilization is a fundamental challenge in precision agriculture, as it impacts both economic returns
arXiv:2605.31201v1 Announce Type: new Abstract: Financial retrieval-augmented generation (RAG) systems typically rank evidence by textual relevance, but in financial markets the useful evidence source
arXiv:2605.30501v1 Announce Type: new Abstract: Watermarking embeds statistical signatures in AI-generated text for detection and attribution. We reveal a fundamental vulnerability: when users access
arXiv:2605.31598v1 Announce Type: new Abstract: Video vision-language models (VLMs) are increasingly used in long-horizon and streaming settings, yet most video encoders still rely on spatiotemporal s
arXiv:2510.03415v3 Announce Type: replace-cross Abstract: Recent work asks whether large language models (LLMs) condition their reasoning on explicit rules rather than statistical regularities from pr
arXiv:2605.30571v1 Announce Type: cross Abstract: Physical AI systems, including robots, autonomous vehicles, embodied agents and edge copilots, often run a different inference workload from cloud LLM
arXiv:2605.30727v1 Announce Type: new Abstract: Deep research agents increasingly combine private local documents with external tools like web retrieval, creating a privacy risk: an agent's external q
arXiv:2603.05529v2 Announce Type: replace-cross Abstract: Data critical to real-world decision-making is increasingly found within organizations. Such data is heterogeneous, constantly evolving, and o
arXiv:2511.17380v2 Announce Type: replace Abstract: Deep learning (DL) models, despite their remarkable success, remain vulnerable to small input perturbations that can cause erroneous outputs, motiva
Cosmos3-Super-Image2Video is a 64B model for temporally coherent image-to-video generation . NVIDIA's Cosmos platform is designed to accelerate Physical AI development by enabling machines to understa
arXiv:2507.05488v2 Announce Type: replace Abstract: We present OLG++, a semantic extension of the Obligation Logic Graph (OLG) for modeling regulatory and legal rules in municipal and interjurisdictio
arXiv:2605.30969v1 Announce Type: new Abstract: Text-based human motion editing aims to modify existing motion sequences according to natural language instructions while maintaining the consistency of
arXiv:1709.08894v3 Announce Type: replace-cross Abstract: Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unl
arXiv:2605.30639v1 Announce Type: cross Abstract: Embodied agents have made strong progress in navigating to target objects, but reaching the goal vicinity does not guarantee that the agent has found
arXiv:2602.07457v2 Announce Type: replace-cross Abstract: Repository-level code editing requires models to understand complex dependencies and execute precise multi-file modifications across a large c
arXiv:2605.30358v1 Announce Type: new Abstract: Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, where the performance is highly constrained to noise. Addressing the limit
arXiv:2605.31124v1 Announce Type: new Abstract: Estimating 3D attributes directly from images has advanced rapidly with the Visual Geometry Grounded Transformer (VGGT), which predicts camera parameter
arXiv:2605.31328v1 Announce Type: new Abstract: Emergent misalignment (EM) is the surprising tendency of language models to become broadly misaligned after fine-tuning on narrowly misaligned examples.
arXiv:2605.31108v1 Announce Type: new Abstract: In this work we introduce a novel approach to domain incremental learning, adapting models over time to evolving, non-stationary data. In contrast to ot
arXiv:2510.02060v2 Announce Type: replace Abstract: In tabular anomaly detection (AD), textual semantics often carry critical signals, as the definition of an anomaly is closely tied to domain-specifi
arXiv:2605.31284v1 Announce Type: cross Abstract: The morphological analysis of mitochondria in fluorescence microscopy (FM) is crucial for understanding cellular health, energy production, and metabo
arXiv:2601.05134v2 Announce Type: replace Abstract: Certified unlearning based on differential privacy offers strong guarantees but remains largely impractical: the noisy fine-tuning approaches propos