Knowledge catalogue
arXiv:2509.14029v2 Announce Type: replace Abstract: Nanopore-based single-molecule sensing is a promising route to fast, low-cost disease diagnosis and protein sequencing: as an analyte such as a pept
arXiv:2609.05081v1 Announce Type: new Abstract: This paper introduces Deep Microcompression (DMC), a hardware-aware pipeline for deep learning inference on bare-metal microcontrollers. DMC integrates
arXiv:2609.05364v1 Announce Type: cross Abstract: Machine-learning performance modeling is a uniquely hostile terrain for long-lived software: the assumptions baked into today's abstractions are inval
arXiv:2606.12807v2 Announce Type: replace Abstract: Summaries of real-world events can become outdated as contexts evolve and new information arrives. A common response is to generate a new summary fr
arXiv:2609.04390v1 Announce Type: cross Abstract: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects approximately 30% of the general population. Ultrasound-based imaging, includ
arXiv:2609.05361v1 Announce Type: new Abstract: Human-robot interaction (HRI) enables intuitive and intelligent collaboration between humans and robots in real-world environments. This paper introduce
arXiv:2609.04778v1 Announce Type: new Abstract: Diffusion language models (DLMs) offer a non-autoregressive alternative for mobile edge agentic artificial intelligence (AI) by refining tokens through
arXiv:2609.05404v1 Announce Type: cross Abstract: Diffusion TV is an interactive AI art installation that offers a tangible and embodied experience of diffusion models through a modified CRT TV. By ph
arXiv:2609.05214v1 Announce Type: new Abstract: The Appropriately Combined Edge-length (ACE) sequence in A-BLiN depends on the zooming dimension d_z. This note removes that dependence. The next edge l
arXiv:2609.04959v1 Announce Type: new Abstract: Recent calls for harder machine translation benchmarks have not clarified what difficulty should mean. We argue that one meaningful and currently unmeas
arXiv:2609.04407v1 Announce Type: new Abstract: Deep neural operators learn mappings between input functions and complete PDE solution fields, enabling forward evaluations of new problem instances ord
arXiv:2609.05363v1 Announce Type: new Abstract: Trade-up recommendation identifies higher-quality alternatives that preserve a customer's purchase intent while offering upgraded benefits. Large langua
arXiv:2609.04531v1 Announce Type: new Abstract: Language generation is almost universally treated as a sequential process: autoregressive models emit one token at a time, while diffusion language mode
arXiv:2511.03699v2 Announce Type: replace Abstract: We investigate whether Large Language Models (LLMs) exhibit conspiratorial tendencies, whether they display socio-demographic biases in this domain,
arXiv:2609.05245v1 Announce Type: new Abstract: Human knowledge is inherently structured and interdependent: mastery of a concept requires prior mastery of its prerequisites, a principle formalized by
arXiv:2609.04782v1 Announce Type: new Abstract: Autoregressive (AR) large language models formulate reasoning as token-level probabilistic sampling, which induces three fundamental defects in complex
arXiv:2609.04579v1 Announce Type: new Abstract: Grounded language-model pipelines can be divided into three stages: selecting an object, retrieving passages for it, and using that evidence to answer.
arXiv:2609.05339v1 Announce Type: new Abstract: Model upgrades are routine; memory migrations are not. An agent can keep the same memory store and still forget: a new model may interpret old notes dif
arXiv:2609.05275v1 Announce Type: new Abstract: Layer dropout (a.k.a. stochastic depth) has been shown to enable faster training, higher accuracy, and robustness to zero-shot layer pruning in both lan
arXiv:2609.04759v1 Announce Type: new Abstract: Robotic dressing assistance is a promising solution for supporting older adults with physical impairments in daily living. However, dressing under human
arXiv:2609.04590v1 Announce Type: cross Abstract: Catheterisation image processing requires segmentation models that are fast, accurate and explainable. While most of the existing studies usually focu
arXiv:2609.04570v1 Announce Type: cross Abstract: LLM-based code generation fails when correctness depends on execution-dependent coupling: the meaning of one routine is defined by the runtime behavio
arXiv:2609.04779v1 Announce Type: cross Abstract: Graph representation learning (GRL) serves as a canonical paradigm for modeling complex networks. However, real-world AI systems inherently manifest a
arXiv:2512.18429v2 Announce Type: replace Abstract: Event-based cameras (ECs) have emerged as bio-inspired sensors that report pixel brightness changes asynchronously, offering unmatched speed and eff
arXiv:2609.05049v1 Announce Type: new Abstract: Autonomous systems require robust low-latency perception under rapidly changing scene dynamics and challenging illumination. In event cameras object det
arXiv:2609.04850v1 Announce Type: new Abstract: While autonomous mobile agents hold great potential for assisting older adults with smartphone usage, existing GUI benchmarks mainly rely on explicit, g
arXiv:2609.05328v1 Announce Type: new Abstract: Generating categorical graphs requires choosing node and edge types that form a coherent structure without depending on node order. Many graph generator
arXiv:2609.04249v1 Announce Type: cross Abstract: Existing audio-video generation methods produce well-synchronized clips but are limited to short durations, while long-video generation methods extend
arXiv:2602.09455v2 Announce Type: replace-cross Abstract: Affine Maximizer Auctions (AMAs), a generalized mechanism family from VCG, are widely used in automated mechanism design due to their inherent
arXiv:2609.04690v1 Announce Type: cross Abstract: Multimodal emotion recognition has attracted growing interest due to its importance in human-computer interaction, remote education, and healthcare. T
arXiv:2609.04667v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly proposed for enterprise workflows, yet existing evaluations rarely test whether business-decision con
arXiv:2609.05043v1 Announce Type: new Abstract: Machine translation (MT) offers a scalable way to extend English instruction-tuning data to multiple languages, but it can distort task-critical constra
arXiv:2609.04272v1 Announce Type: cross Abstract: This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a ze
arXiv:2507.17049v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA)-enabled robots integrate visual perception, natural language understanding, and action planning to interpret thei
arXiv:2609.04391v1 Announce Type: new Abstract: In this work, a novel evaluation scheme built on a generalized variant of the Rand Index measure, namely, the Hullermeier-Rifqi Index, is proposed in or
arXiv:2609.04290v1 Announce Type: cross Abstract: Despite increasing reliance on LLMs that reason with external evidence supplied by tools, retrieval-augmented generation, other agents, and users, how
arXiv:2607.09773v2 Announce Type: replace Abstract: Computer-use agents must solve long-horizon tasks through repeated interaction with partially observable, multimodal desktop environments. Although
arXiv:2609.04280v1 Announce Type: cross Abstract: Modern LLM-based agents operate through a harness of tools, reusable skills, and specialist agents that shapes what they observe and what they can do.
arXiv:2609.04239v1 Announce Type: new Abstract: This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series (TS) foundation model (TSFM) tailored to financial
arXiv:2411.01576v3 Announce Type: replace Abstract: The explainable clustering problem was first posed by Moshkovitz et al. (ICML 2020) and studies how well an axis-aligned decision tree with K leaves
arXiv:2609.04512v1 Announce Type: cross Abstract: Oral potentially malignant disorders (OPMDs) are critical precursors to oral cancer, yet clinical detection remains challenging because of substantial
arXiv:2509.22480v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been widely used for problem-solving tasks. Most recent work improves their performance through supervised f
arXiv:2609.04565v1 Announce Type: new Abstract: Large language models demonstrate increasingly strong reasoning capabilities through effective post-training. Yet, prevailing post-training methods opti
arXiv:2609.04501v1 Announce Type: cross Abstract: Eye movement biometrics (EMB) is an emerging behavioral modality for user authentication, particularly in virtual- and augmented-reality systems, wher
arXiv:2609.04276v1 Announce Type: new Abstract: Vision-language models (VLMs), such as CLIP, have achieved strong performance across multimodal tasks by aligning visual and textual representations in
arXiv:2609.04277v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies have shown strong potential for general-purpose robotic manipulation, but they can still fail unpredictably duri
arXiv:2609.04910v1 Announce Type: new Abstract: Gaussian kernel sums are the computational core of maximum mean discrepancies (MMDs), kernel gradient flows, Stein variational gradient descent (SVGD),
arXiv:2609.04549v1 Announce Type: new Abstract: The FitzHugh-Nagumo (FHN) system serves as a simplified model of neuronal voltage dynamics, capturing the activator-inhibitor structure behind both isol
arXiv:2609.04392v1 Announce Type: new Abstract: Fine-grained visual understanding depends on local detail, yet visual encoders face a trade-off between costly full-image high-resolution processing and
arXiv:2609.05223v1 Announce Type: new Abstract: Artificial intelligence models are promising for medical diagnosis, but they require large numbers of unbiased data, which in medicine are distributed a
arXiv:2609.04815v1 Announce Type: new Abstract: Detecting orchestrated cyberattack campaigns that span multiple organizations traditionally requires sharing sensitive telemetry and threat intelligence
arXiv:2609.05242v1 Announce Type: new Abstract: Few-shot action recognition (FSAR) aims to recognize unseen action categories with only a small number of annotated video samples. Recent works typicall
arXiv:2609.04706v1 Announce Type: new Abstract: A merchant's payment processor, ledger, ERP and bank feed are updated by messages that get delayed, duplicated, dropped and reordered, so for minutes at
arXiv:2609.05325v1 Announce Type: new Abstract: Achieving robust SLAM in large-scale underground coal mines with complex structures and severe degeneracies remains highly challenging. Dense smoke and
arXiv:2609.05224v1 Announce Type: new Abstract: Recent progress in multimodal large language models (MLLMs) has fueled significant enthusiasm in their potential to act as autonomous agents for real-wo
arXiv:2609.05207v1 Announce Type: cross Abstract: Dynamical symbolic regression methods identify governing differential equations from noisy data, balancing interpretability and predictive accuracy. H
arXiv:2512.14779v2 Announce Type: replace Abstract: Standard weather forecast evaluations focus on the forecaster's perspective and on a statistical assessment comparing forecasts and observations. In
arXiv:2609.04875v1 Announce Type: cross Abstract: Long-running LLM agents are stateful: beyond the transcript they accrete compressed summaries, plaintext memory, pending tool plans, and, under every
arXiv:2512.14675v2 Announce Type: replace Abstract: Contemporary reservoir computing relies heavily on globally Lipschitz, well-behaved activation functions, limiting applications in defense, disaster
arXiv:2609.04963v1 Announce Type: new Abstract: Reasoning allows artificial intelligence models to revisit and correct their mistakes, enabling recent frontier advances in mathematical theorem solving