Knowledge catalogue
arXiv:2508.17458v2 Announce Type: replace Abstract: Verbal multiword expressions (VMWEs) remain difficult for machine translation because their meanings are often not recoverable from their component
arXiv:2604.16706v1 Announce Type: cross Abstract: Automated evaluation of tool-using large language model (LLM) agents is widely assumed to be reliable, but this assumption has rarely been validated a
arXiv:2604.18320v1 Announce Type: new Abstract: Self-evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo-label-based methods suffer from progressive quality degr
arXiv:2601.10306v2 Announce Type: replace-cross Abstract: While Reinforcement Learning (RL) has advanced LLM reasoning, applying it to long-context scenarios is hindered by sparsity of outcome rewards
arXiv:2604.17087v1 Announce Type: new Abstract: Recent Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language understanding tasks, yet their inference efficie
arXiv:2508.07809v5 Announce Type: replace Abstract: Reinforcement learning with verifiable reward (RLVR) has become a promising paradigm for post-training large language models (LLMs) to improve their
arXiv:2604.17753v1 Announce Type: cross Abstract: Merging multiple Low-Rank Adaptation (LoRA) experts into a single backbone is a promising approach for efficient multi-task deployment. While existing
arXiv:2604.18052v1 Announce Type: cross Abstract: Intrusion detection systems (IDSs) for 5G networks must handle complex, high-volume traffic. Although opaque 'black-box' models can achieve high accur
arXiv:2602.16090v2 Announce Type: replace-cross Abstract: The response of the climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and sl
arXiv:2604.16528v1 Announce Type: new Abstract: Embryo selection is one of multiple crucial steps in in-vitro fertilization, commonly based on morphological assessment by clinical embryologists. Altho
arXiv:2504.04814v3 Announce Type: replace-cross Abstract: Trustworthy artificial intelligence (AI) is essential in healthcare, particularly for high-stakes tasks like medical image segmentation. Expla
arXiv:2512.11108v3 Announce Type: replace Abstract: Good quality explanations strengthen the understanding of language models and data. Feature attribution methods, such as Integrated Gradient, are a
arXiv:2604.17879v1 Announce Type: new Abstract: Camouflaged Object Detection is challenging due to the high degree of similarity between camouflaged objects and their surrounding backgrounds. Current
arXiv:2604.18296v1 Announce Type: new Abstract: Static concreteness ratings are widely used in NLP, yet a word's concreteness can shift with context, especially in figurative language such as metaphor
arXiv:2502.13637v2 Announce Type: replace Abstract: Human affordance learning investigates contextually relevant novel pose prediction such that the estimated pose represents a valid human action with
arXiv:2604.16757v1 Announce Type: new Abstract: The expression of emotions that serve social purposes, such as asserting independence or fostering interdependence, is central to human interactions and
arXiv:2505.22226v2 Announce Type: replace Abstract: Recent theoretical advances reveal that the Hadamard product induces nonlinear representations and implicit high-dimensional mappings for the field
arXiv:2604.18168v1 Announce Type: new Abstract: Few-step generation has been a long-standing goal, with recent one-step generation methods exemplified by MeanFlow achieving remarkable results. Existin
arXiv:2604.16865v1 Announce Type: cross Abstract: In this paper, we consider the problem of extraction of most informative features from time series that are regarded as observed values of stochastic
arXiv:2604.16450v1 Announce Type: cross Abstract: Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess de
arXiv:2604.16610v1 Announce Type: cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability. Conventional fairness
arXiv:2604.16780v1 Announce Type: new Abstract: This paper presents FairNVT, a lightweight debiasing framework for pretrained transformer-based encoders that improves both representation and predictio
arXiv:2506.12176v5 Announce Type: replace Abstract: In explainable AI, surrogate models are commonly evaluated by their fidelity to a neural network's predictions. Fidelity, however, measures alignmen
arXiv:2601.11886v2 Announce Type: replace Abstract: In high-stakes domains like medicine, it may be generally desirable for models to faithfully adhere to the context provided. But what happens if the
arXiv:2512.20182v3 Announce Type: replace Abstract: Recognizing whether outputs from large language models (LLMs) contain faithfulness hallucination is crucial for real-world applications, e.g., retri
arXiv:2604.16522v1 Announce Type: new Abstract: This paper proposes a fast and online method for jointly performing 3D multi-object tracking and pose estimation using multiple monocular cameras. Our a
arXiv:2604.18491v1 Announce Type: new Abstract: Computational Fluid Dynamics (CFD) is central to race-car aerodynamic development, yet its cost -- tens of thousands of core-hours per high-fidelity eva
arXiv:2604.17956v1 Announce Type: new Abstract: Machine learning has become integral to medical research and is increasingly applied in clinical settings to support diagnosis and decision-making; howe
arXiv:2604.16778v1 Announce Type: new Abstract: LLM-powered agents often reason from scratch when presented with a new problem instance and lack automatic mechanisms to transfer learned skills to othe
arXiv:2604.16612v1 Announce Type: new Abstract: Traffic prediction plays a central role in intelligent transportation systems (ITS) by supporting real-time decision-making, congestion management, and
arXiv:2604.16574v1 Announce Type: new Abstract: Federated Learning (FL) faces challenges from client data heterogeneity and resource-constrained mobile devices, which can degrade model accuracy. Perso
arXiv:2510.24942v2 Announce Type: replace-cross Abstract: Despite their impressive performance, vision-language models (VLMs) still struggle on culturally situated inputs. To understand how VLMs proce
arXiv:2511.17171v4 Announce Type: replace Abstract: Predicting wildfire risk is a reasoning-intensive spatial problem that requires the integration of visual, climatic, and geographic factors to infer
arXiv:2604.17919v1 Announce Type: new Abstract: Recent advances in flow-based offline reinforcement learning (RL) have achieved strong performance by parameterizing policies via flow matching. However
arXiv:2604.16649v1 Announce Type: new Abstract: Directed energy deposition (DED) produces complex thermo-mechanical responses that can lead to distortion and reduced dimensional accuracy of a manufact
arXiv:2604.17344v1 Announce Type: cross Abstract: When task-specific labels are not available, it becomes difficult to select an embedding model for a specific target corpus. Existing labelless measur
arXiv:2604.17513v1 Announce Type: new Abstract: Simulation frameworks such as Isaac Sim have enabled scalable robot learning for locomotion and rigid-body manipulation; however, contact-rich simulatio
arXiv:2604.17720v1 Announce Type: cross Abstract: Point-based Neural Networks (PNNs) have become a key approach for point cloud processing. However, a core operation in these models, Farthest Point Sa
arXiv:2511.00868v2 Announce Type: replace Abstract: Large Language Model (LLM) serving is increasingly constrained by the growing size of the key-value (KV) cache, which scales with both context lengt
arXiv:2604.18109v1 Announce Type: new Abstract: This paper presents factorized linear projection (FLiP) models for understanding pretrained sentence embedding spaces. We train FLiP models to recover t
arXiv:2509.18611v2 Announce Type: replace Abstract: Pretraining on large-scale collections of PDE-governed spatiotemporal trajectories has recently shown promise for building generalizable models of d
arXiv:2510.09204v2 Announce Type: replace-cross Abstract: Centralized trajectory optimization in the joint space of multiple robots allows access to a larger feasible space that can result in smoother
arXiv:2604.17625v1 Announce Type: new Abstract: This paper introduces a novel methodology for generating fast and memory-efficient video continuations. Our method, dubbed FlowC2S, fine-tunes a pre-tra
arXiv:2604.17149v1 Announce Type: cross Abstract: Accurate autoregressive prediction of 3D turbulent flows remains challenging for neural PDE solvers, as small errors in fine-scale structures can accu
arXiv:2604.16448v1 Announce Type: cross Abstract: As edge AI deployments scale to billions of devices running always-on, real-time compound AI pipelines, they represent a massive and largely unmanaged
arXiv:2603.19857v2 Announce Type: replace-cross Abstract: Recent Video-to-Audio (V2A) methods have achieved remarkable progress, enabling the synthesis of realistic, high-quality audio. However, they
arXiv:2505.11140v3 Announce Type: replace Abstract: We introduce fs1, a simple yet effective method that improves the factuality of reasoning traces by collecting them from large reasoning models and
arXiv:2604.17194v1 Announce Type: cross Abstract: Converting betting odds into accurate outcome probabilities is a fundamental challenge in order to use betting odds as a benchmark for sports forecast
arXiv:2604.18379v1 Announce Type: new Abstract: Most data-driven ionospheric forecasting models operate on gridded products, which do not preserve the time-varying sampling structure of satellite-base
arXiv:2601.06803v2 Announce Type: replace Abstract: While Chain-of-Thought empowers Large Vision-Language Models with multi-step reasoning, explicit textual rationales suffer from an information bandw
arXiv:2601.03938v2 Announce Type: replace-cross Abstract: Continual learning (CL) for large language models (LLMs) aims to enable sequential knowledge acquisition without catastrophic forgetting. Memo
arXiv:2604.17785v1 Announce Type: new Abstract: Unlearning in large language models (LLMs) has emerged as a promising safeguard against adversarial behaviors. When the forgetting loss is applied unifo
arXiv:2603.26475v2 Announce Type: replace Abstract: Electrocardiograms (ECGs) are among the most widely available clinical signals and play a central role in cardiovascular diagnosis. While recent fou
arXiv:2604.16376v1 Announce Type: new Abstract: This study investigates the applicability of authorship attribution based on stylistic features to support actor analysis in threat intelligence. As a f
arXiv:2604.17268v1 Announce Type: new Abstract: AI-generated imagery has reached near-photorealistic fidelity, yet this technology poses significant threats to information security and societal trust.
arXiv:2603.09721v2 Announce Type: replace Abstract: High-fidelity video generation remains challenging for diffusion models due to the difficulty of modeling complex spatio-temporal dynamics efficient
arXiv:2603.07690v2 Announce Type: replace Abstract: Streaming Visual Geometry Transformers such as StreamVGGT enable strong online 3D perception, but their KV-cache grows unbounded over long streams,
arXiv:2505.18128v4 Announce Type: replace Abstract: We introduce Frankentexts, a long-form narrative generation paradigm that treats an LLM as a composer of existing texts rather than as an author. Gi
arXiv:2604.18159v1 Announce Type: new Abstract: Empathy is essential for fostering natural interactions in spoken dialogue systems, as it enables machines to recognize the emotional tone of human spee
arXiv:2604.18328v1 Announce Type: new Abstract: We present FregeLogic, a hybrid neuro-symbolic system for SemEval-2026 Task 11 (Subtask 1), which addresses syllogistic validity prediction while reduci