Knowledge catalogue
The independent review of the OpenAI Hugging Face incident, supposedly a watershed moment in cybersecurity, wasn't done by a cybersecurity firm and the authors have no cybersecurity experience. That's
arXiv:2511.16695v2 Announce Type: replace Abstract: Art is a deeply personal and expressive medium, where each artist brings their own style, technique, and cultural background into their work. Tradit
arXiv:2608.27885v1 Announce Type: new Abstract: Multimodality translation (e.g., text-to-image) is a core generative AI task. However, existing approaches (1) follow generative paths that do not direc
arXiv:2606.17016v2 Announce Type: replace Abstract: As LLM agents are deployed in long-horizon sessions, context accumulation drives up inference costs. Existing approaches utilize text pruning or dyn
arXiv:2608.27929v1 Announce Type: new Abstract: Videos are expensive to analyze frame by frame, yet many video understanding tasks depend on knowing where relevant moments occur. A system may need to
arXiv:2608.27514v1 Announce Type: new Abstract: Diffusion-based language models (dLLMs) enable parallel token generation through iterative denoising, but existing decoding strategies collapse to singl
arXiv:2608.28018v1 Announce Type: new Abstract: Knowledge-intensive reasoning requires Large Language Models (LLMs) to ground answers in provided evidence. When evidence is insufficient, it is desirab
arXiv:2608.28195v1 Announce Type: new Abstract: Optical character recognition (OCR) for handwritten Indic manuscripts is essential for large-scale digitization and computational access to manuscript h
arXiv:2608.27662v1 Announce Type: new Abstract: This work presents a generative continual learning framework based on growing self-organizing maps (GSOMs) that are augmented with learned distributiona
arXiv:2608.28069v1 Announce Type: new Abstract: Recent progress has been made in 3D Gaussian representation for reconstruction, generation, and physical simulation. However, current approaches mainly
arXiv:2608.28549v1 Announce Type: new Abstract: Recent generative approaches to geometry estimation adapt pretrained image diffusion models and treat the task as image-conditioned generation. Leveragi
arXiv:2608.27562v1 Announce Type: new Abstract: Translating continuous, noisy egocentric video streams into discrete, temporally ordered action steps is fraught with visual challenges. Heavy ego-motio
arXiv:2608.28008v1 Announce Type: new Abstract: In this paper, we propose a new token compression paradigm for video Multimodal Large Language Models (MLLMs), termed Visual Token Coding (VTC). Inspire
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Banger paper from Stanford on efficient test-time scaling. If you run agents that think for a long time, this one is worth your time. (bookmark it) Long reasoning keeps the entire trace in memory thro
Interesting paper on prompt optimization. They claim that a single-lineage prompt optimizer just matched GEPA on a smaller rollout budget. Prompt optimization has been drifting toward heavier machiner
// Memory is what breaks long-horizon agents // It's undeniable how important memory/recall is for long-horizon tasks. If you are building for long-horizon tasks, this is a great read. (bookmark it) T
arXiv:2406.06837v2 Announce Type: cross Abstract: A Bayesian data assimilation scheme is formulated for advection-dominated advective and diffusive evolutionary problems, based upon the Dynamic Likeli
arXiv:2608.27313v1 Announce Type: cross Abstract: We establish a global finite-sample guarantee for synchronous quantile temporal-difference learning (QTD) in tabular distributional reinforcement lear
arXiv:2601.21217v2 Announce Type: replace-cross Abstract: We introduce a flexible empirical Bayes approach for fitting Bayesian generalized linear models. Specifically, we adopt a novel mean-field var
arXiv:2510.11567v2 Announce Type: replace Abstract: Synthetic datasets are widely used for training urban scene recognition models, but even highly realistic renderings show a noticeable gap to real i
arXiv:2608.27010v1 Announce Type: new Abstract: In the present work an efficient border control management procedure is proposed. Compared to operational queue management systems, whose operations are
arXiv:2608.26164v1 Announce Type: new Abstract: Large language models can interpret natural-language chemistry questions, but their internal reasoning is difficult to inspect, constrain, and validate.
arXiv:2608.26883v1 Announce Type: new Abstract: Robotic grippers face substantial challenges in grasping and manipulating thin objects. Most existing grippers rely on highly precise approach and grasp
arXiv:2608.26963v1 Announce Type: new Abstract: This work concerns adversarial training against the small-norm adversarial examples that arise from the inherent input instability of a trained deep neu
arXiv:2608.26193v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) achieve strong performance on VQA and scene understanding, yet affective reasoning remains vulnerable to shortc
arXiv:2608.26140v1 Announce Type: new Abstract: Diffusion Large Language Models (DLLMs) enable non-autoregressive decoding and bidirectional context modeling, but efficient inference remains challengi
arXiv:2608.26309v1 Announce Type: new Abstract: Modern machine learning models rely on large amounts of labeled data. However, manual annotation of large-scale datasets is expensive and time-consuming
arXiv:2608.26516v1 Announce Type: new Abstract: Two of the most fundamental questions in statistical learning theory are the following: which prediction problems are learnable, and how should they be
arXiv:2608.27000v1 Announce Type: new Abstract: We study bilateral interpolation of finite-order rigid-motion jets represented by unit dual quaternions. An order-n multidual (MD) algebra is the trunca
arXiv:2608.26152v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit unprecedented natural language generation and many text-based problem-solving capabilities. Indeed, in many langu
arXiv:2608.26213v1 Announce Type: cross Abstract: Large language model (LLM)-based audio-visual speech recognition (AVSR) systems are robust under noise. Contrastive decoding (CD), originally introduc
arXiv:2608.27095v1 Announce Type: new Abstract: Age-related macular degeneration (AMD) and diabetic macular edema (DME) are leading causes of vision loss, and optical coherence tomography (OCT) is the
arXiv:2508.01587v4 Announce Type: replace Abstract: Lifelong Person Re-Identification (LReID) typically resists catastrophic forgetting by replaying historical samples, rehearsing domain distributions
arXiv:2601.08444v2 Announce Type: replace Abstract: Table reasoning, a task to answer questions by reasoning over data presented in tables, is an important topic due to the prevalence of knowledge sto
arXiv:2508.11017v3 Announce Type: replace-cross Abstract: Large language models (LLMs) struggle with cross-lingual knowledge transfer: they sometimes hallucinate when asked in one language about facts
arXiv:2608.26822v1 Announce Type: cross Abstract: The National Oceanic and Atmospheric Administration (NOAA) employs independent prediction systems for distinct forecast products. While some separatio
arXiv:2608.26870v1 Announce Type: new Abstract: Weak signals are early, low-visibility indicators that precede significant changes before those changes become established. Existing detection methods,
arXiv:2608.26121v1 Announce Type: new Abstract: Large language models state false facts as fluently as true ones, yet a model often 'knows' internally when it is on shaky ground: the probability it as
arXiv:2608.26185v1 Announce Type: new Abstract: Equal participation in co-located discussion is important for effective collaboration, yet people often hold back when they anticipate negative interper
arXiv:2608.27038v1 Announce Type: new Abstract: Although batch prompting makes large language model inference more efficient by processing multiple instances simultaneously, it suffers from unpredicta
arXiv:2608.26544v1 Announce Type: new Abstract: We present Chart2SVG, a multimodal large language model that converts static raster charts into structurally organized, semantically enriched SVGs that
arXiv:2608.27254v1 Announce Type: new Abstract: One approach to mechanistic interpretability explains behavior through circuits: the components and connections that carry it. Frozen discovery often re
arXiv:2606.18723v2 Announce Type: replace Abstract: Intravascular ultrasound (IVUS) lumen and external elastic membrane (EEM) segmentation is important for quantitative coronary plaque burden assessme
arXiv:2608.26965v1 Announce Type: cross Abstract: This paper introduces ClusterAttention, a general training-free speedup of bidirectional attention layers. Existing sparse attention methods either re
arXiv:2608.26386v1 Announce Type: cross Abstract: Retrieval-augmented methods improve factual accuracy by grounding language models in external knowledge, but retrieving over unstructured text often i
arXiv:2608.26656v1 Announce Type: cross Abstract: Multi-object removal in 3D scenes is challenging due to severe occlusions, semantic entanglement, and the difficulty of maintaining geometric and mult
arXiv:2608.27203v1 Announce Type: new Abstract: A known necessary condition for Fisher consistency of the structured support vector machine requires the task loss to be a metric for which every output
arXiv:2608.26192v1 Announce Type: cross Abstract: How documents are segmented into retrievable chunks and how those chunks are embedded strongly affect Retrieval-Augmented Generation (RAG) quality, ye
arXiv:2608.26465v1 Announce Type: new Abstract: Compositional generalization is usually evaluated through model accuracy. We instead ask which structural or lexical identifications make held-out COGS
arXiv:2608.27122v1 Announce Type: new Abstract: Directed graph learning naturally leads to trainable nonsymmetric propagation operators with distinct right and left spectral structures. Building on th
arXiv:2608.26273v1 Announce Type: cross Abstract: Static shape estimation of co-manipulative continuum robots (CCRs) is challenging because the continuum arms and manipulated flexible object form a cl
arXiv:2601.04778v2 Announce Type: replace-cross Abstract: Video-language models (VLMs) achieve strong multimodal understanding but remain prone to hallucinations, especially when reasoning about actio
arXiv:2604.14586v4 Announce Type: replace-cross Abstract: The rapid expansion of gaming industry requires advanced recommender systems tailored to its dynamic landscape. Existing Graph Neural Network
arXiv:2608.27455v1 Announce Type: new Abstract: Recent advances in inference-time scaling have significantly improved the reasoning performance of large language models (LLMs). However, these methods
arXiv:2608.26943v1 Announce Type: cross Abstract: This paper proposes a novel data-driven algorithm to approximate the dominant eigenfunctions (aka.~modes) of the Koopman operator of nonlinear dynamic
arXiv:2608.27162v1 Announce Type: cross Abstract: Battery lifetime is central to sustainable electrification, yet the particle cracking that drives lithium-ion cathode aging is hard to measure: quanti
arXiv:2608.26135v1 Announce Type: new Abstract: We present a modular data-science pipeline for estimating public sentiment towards individuals from fragmented, unstructured open-source intelligence (O
arXiv:2608.26989v1 Announce Type: new Abstract: This paper investigates decentralized multitask learning over networks when the underlying task relationships are unknown. While existing graph-regulari
arXiv:2608.27278v1 Announce Type: cross Abstract: Whole-slide images (WSIs) are central to computational pathology but are prohibitively large, making patch-based processing the practical unit for fou