Knowledge catalogue
arXiv:2605.06368v2 Announce Type: replace-cross Abstract: Despite extensive research into mitigating distribution shifts, many existing algorithms yield inconsistent performance, often failing to outp
arXiv:2606.22886v2 Announce Type: replace Abstract: Objective: This study aims to improve the reliability and robustness of medical named entity recognition (NER) in Chinese atopic dermatitis (AD) cli
arXiv:2609.25962v1 Announce Type: new Abstract: Neural network verification has become a key tool for providing formal guarantees on the behaviour of neural networks. However, many verification proble
arXiv:2609.25152v1 Announce Type: new Abstract: Deep Imbalanced Regression (DIR) addresses a common failure mode of regression models: target distributions are highly non-uniform, causing models to pe
arXiv:2609.25373v1 Announce Type: new Abstract: On-device assistants require function-calling models that map natural language to local system actions, but existing resources emphasize web APIs or nar
arXiv:2609.22601v1 Announce Type: cross Abstract: The increasing reliance of autonomous AI agents on external and distributed knowledge sources introduces a fundamental challenge for decentralized inf
arXiv:2609.26377v1 Announce Type: new Abstract: Fairness-aware distributed learning prioritizes clients with large losses to reduce performance disparities, but label poisoning can create large losses
arXiv:2609.25978v1 Announce Type: cross Abstract: Saliency maps are widely used to explain deep learning predictions in medical imaging, yet visually plausible explanations do not necessarily reflect
arXiv:2609.26077v1 Announce Type: new Abstract: A Strassen-type algorithm has many realizations with the same exact product and multiplication count yet different fp8 error because basis changes resha
arXiv:2609.25505v1 Announce Type: cross Abstract: Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics num
arXiv:2609.25082v1 Announce Type: new Abstract: Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-fro
arXiv:2502.16396v2 Announce Type: replace Abstract: Federated learning systems are increasingly threatened by data poisoning attacks, where malicious clients compromise global models by contributing t
arXiv:2609.24352v1 Announce Type: new Abstract: Large language models (LLMs), when acting as agents, are expected to take observed data in context, infer the latent state space underlying the world, a
arXiv:2609.25298v1 Announce Type: new Abstract: Cultural evaluation coverage and robustness in language models are difficult to diagnose because pretraining corpora and cultural benchmarks are rarely
arXiv:2609.25192v1 Announce Type: new Abstract: Financial search is a highly demanding task for LLM agents, requiring not only a correct final answer but also temporally valid information retrieval, a
arXiv:2609.24002v1 Announce Type: new Abstract: Large language model agents increasingly answer financial questions by searching regulatory filings. Such questions are often deceptively under-specifie
arXiv:2609.26048v1 Announce Type: cross Abstract: Language-model agents often reach a working solution and then fail to consistently deliver it. We study runtime policies: targeted natural-language in
arXiv:2609.23064v1 Announce Type: new Abstract: Understanding the physical world requires more than object recognition, scene description, and short-term visual prediction, as real-world physical syst
arXiv:2609.25750v1 Announce Type: new Abstract: Manipulation requires both broad scene awareness and detailed local feedback, yet conventional camera rigs provide them through separate front and wrist
arXiv:2609.26796v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive LLMs by enabling non-autoregressive text gene
arXiv:2609.26756v2 Announce Type: new Abstract: X-ray is medicine's most widely used imaging modality, yet remains among its least quantitative. Unlike volumetric modalities like CT or MRI, X-ray coll
arXiv:2602.14285v2 Announce Type: replace-cross Abstract: Automated scholarly paper review (ASPR) has entered the coexistence phase with traditional peer review, where artificial intelligence (AI) sys
arXiv:2609.25716v1 Announce Type: new Abstract: Reference-based image quality assessment (IQA) metrics aim to reflect how humans perceive the perceptual distance between a pair of images. To learn how
arXiv:2609.26299v2 Announce Type: new Abstract: Existing latent world models are typically optimized for future predictability, yet the resulting representations are not necessarily useful for plannin
arXiv:2609.26605v1 Announce Type: cross Abstract: Foundation model embeddings of screening mammograms may encode pre-diagnostic tissue change without task-specific adaptation. We tested whether embedd
arXiv:2609.25085v1 Announce Type: cross Abstract: Data assimilation aims to infer the state of complex dynamical systems based on observational data. However, accurate inference of the multimodal post
arXiv:2609.24084v1 Announce Type: cross Abstract: Open-weight large language models (LLMs) can be copied, modified, and redeployed behind black-box APIs, making post-release ownership verification dif
arXiv:2609.22664v1 Announce Type: cross Abstract: Research on large language model agents for penetration testing is evaluated almost entirely by capability: whether the agent captures a flag or repro
arXiv:2609.22719v1 Announce Type: cross Abstract: Business requirements for enterprise software systems are rarely captured in structured form; the logic resides instead in source code, configuration
arXiv:2609.25655v1 Announce Type: new Abstract: As large language models (LLMs) scale rapidly, dense full-parameter adaptation becomes increasingly expensive, motivating sparse and modular architectur
arXiv:2609.22822v1 Announce Type: cross Abstract: We present Hybrid Quantum Root Cause Analysis (HQ-RCA), an industrially grounded workflow for root cause analysis in banking IT operations, built on a
arXiv:2609.23130v1 Announce Type: new Abstract: Large language model (LLM) inference is evolving from an engine-local optimization problem into a distributed control problem involving reusable state,
arXiv:2609.25625v1 Announce Type: new Abstract: Most minimally invasive surgery is still performed with hand-held laparoscopic instruments, and the surgeon's instrument kinematics are lost when the op
arXiv:2609.25408v1 Announce Type: cross Abstract: Online A/B experiments are the decision standard for user engagement, but traffic and readout time limit how many conversational-AI changes can be tes
arXiv:2609.25186v1 Announce Type: cross Abstract: The rising global prevalence of mental health conditions, together with longstanding barriers in traditional healthcare, such as limited resources, hi
arXiv:2609.22790v1 Announce Type: new Abstract: Undergraduate programmes in intelligent medical engineering are expanding, yet laboratory curricula lag behind the multimodal, long-tailed, and distribu
arXiv:2609.26146v1 Announce Type: new Abstract: Risk monitoring in multi-agent systems is commonly built on a per-state primitive that scores each state independently and selects the top K. Under crow
arXiv:2609.26484v1 Announce Type: new Abstract: Training-free visual-token pruning often uses token importance, redundancy, or related selection criteria as proxies for safe removal. We show that thes
arXiv:2609.25034v1 Announce Type: new Abstract: Central bank press conferences are not merely information releases --- they are structured narratives. We study whether the shape of sentiment within a
arXiv:2609.25669v1 Announce Type: new Abstract: Adoption and diffusion of neologisms in online communities have received renewed attention in recent years. As internet slang terms such as APT, referri
arXiv:2609.25050v1 Announce Type: new Abstract: We introduce FrontierMath Erdos (FME), a benchmark of 68 Erdos problems that are open as of August 2026. To solve a task in FME, AI systems must resolve
arXiv:2609.25701v1 Announce Type: new Abstract: We study distributed Byzantine-resilient actor-critic multi-agent reinforcement learning (AC-MARL), where agents collectively learn policies through loc
arXiv:2609.26067v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) replace scalar edge weights with learnable univariate functions, increasing flexibility but also parameter memory bec
arXiv:2609.26095v1 Announce Type: cross Abstract: With the growing global demand for energy, nuclear fusion has emerged as a promising direction for future clean energy. Tokamaks represent one of the
arXiv:2609.25741v1 Announce Type: new Abstract: Generative models have advanced image-conditioned 3D content creation, yet generating controllable and executable 3D scenes from a single image remains
arXiv:2609.26729v1 Announce Type: new Abstract: In this paper, we proposed GAD-MambaUNet, a lightweight medical image segmentation network that combines efficient local modeling, direction--group stat
arXiv:2609.22619v1 Announce Type: new Abstract: Tracking recovery of walking function requires detecting meaningful gait change across rehabilitation sessions, yet objective 3D measurement remains con
arXiv:2609.25652v1 Announce Type: new Abstract: Recent game world models support realistic visual simulation and interactive gameplay based on player inputs. However, they typically learn environment
arXiv:2609.26508v1 Announce Type: new Abstract: Streaming principal component analysis (PCA) seeks to recover a leading spectral subspace in a single pass over a data stream. We give a new analysis of
arXiv:2609.25839v1 Announce Type: new Abstract: Flow-matching schedules affect both sampling dynamics and the variance of the regression target. For centered commuting Gaussians, we show that a direct
arXiv:2609.26118v1 Announce Type: new Abstract: Latent action models (LAMs) learn action-related representations from action-free videos via self-supervised future prediction, offering a scalable para
arXiv:2609.25605v1 Announce Type: cross Abstract: In this paper, we study the estimation of a marginal regression function from independent units with repeated binary, count, or continuous responses u
arXiv:2609.26499v1 Announce Type: new Abstract: Today, progress in open-weight language models enables systems capable of writing, executing and debugging code while still running on a single workstat
arXiv:2609.22691v1 Announce Type: new Abstract: An enduring and richly elaborated dichotomy in cognitive neuroscience is that of human behavior control mechanisms, divided into habitual versus goal-di
arXiv:2609.24955v1 Announce Type: cross Abstract: Visual instructions for physical tasks are typically authored in one context and followed in another, requiring users to translate demonstrated tools,
arXiv:2609.26620v1 Announce Type: new Abstract: Photographic composition aims to provide visual guidance for improving the framing, viewpoint, and spatial arrangement of an image. Early methods primar
arXiv:2609.25247v1 Announce Type: new Abstract: Interaction understanding in 3D scenes requires a joint description of movable parts, their motion, and the regions through which they can be operated.
arXiv:2509.13813v3 Announce Type: replace Abstract: Large language models are known to hallucinate, generating linguistically plausible but incorrect answers to questions. Uncertainty quantification h
arXiv:2609.26342v1 Announce Type: new Abstract: Residual Vector Quantization turns continuous representations into discrete, multi-level token sequences. Yet most methods operate in Euclidean space, d
arXiv:2609.25963v1 Announce Type: new Abstract: Transformer architectures exhibit cross-layer redundancies, yet post-training compression pipelines typically optimize layers in isolation or rely on he