Knowledge catalogue
arXiv:2604.19036v1 Announce Type: new Abstract: Defeasible statements are statements that are likely, or probable, or usually true, but may occasionally be false. Plausible reasoning makes conclusions
arXiv:2603.29078v2 Announce Type: replace Abstract: We present PolarQuant, a post-training weight quantization method for large language models (LLMs) that exploits the distributional structure of neu
arXiv:2604.19024v1 Announce Type: new Abstract: Safe Reinforcement Learning from Human Feedback (Safe RLHF) has recently achieved empirical success in developing helpful and harmless large language mo
arXiv:2604.19129v1 Announce Type: new Abstract: Existing facial reenactment methods struggle with a trade-off between expressiveness and fine-grained controllability. Holistic facial reenactment model
arXiv:2510.18333v2 Announce Type: replace-cross Abstract: Despite progress in watermarking algorithms for large language models (LLMs), real-world deployment remains limited. We argue that this gap st
arXiv:2604.18649v1 Announce Type: cross Abstract: As generative AI faces intensifying legal challenges, the machine learning community has increasingly relied on post-hoc mitigation -- especially mach
arXiv:2604.19684v1 Announce Type: new Abstract: Explainable artificial intelligence (XAI) has predominantly focused on generating model-centric explanations that approximate the behavior of black-box
arXiv:2601.15349v2 Announce Type: replace Abstract: In narrow, unstructured underwater environments such as environmental monitoring and minimally invasive medical procedures, micro soft robots exhibi
arXiv:2604.18801v1 Announce Type: new Abstract: Lossy compression is widely used to reduce storage and I/O costs for large-scale particle datasets in scientific applications such as cosmology, molecul
arXiv:2510.13763v2 Announce Type: replace-cross Abstract: Amortized simulator-based inference offers a powerful framework for tackling Bayesian inference in computational fields such as engineering or
arXiv:2604.18892v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves multimodal reasoning by rewarding verifiable final answers. Yet answer-correct trajectori
arXiv:2604.18712v1 Announce Type: new Abstract: Probing has shown that language model representations encode rich linguistic information, but it remains unclear whether they also capture cognitive sig
arXiv:2604.19069v1 Announce Type: cross Abstract: Neural NLI models overfit dataset artifacts instead of truly reasoning. A hypothesis-only model gets 57.7% in SNLI, showing strong spurious correlatio
arXiv:2604.19083v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in cross-modal understanding and generation, yet their deployment is threate
arXiv:2508.01371v3 Announce Type: replace-cross Abstract: Smart contracts are important for digital finance, yet they are hard to patch once deployed. Prior work has mainly explored LLMs for smart con
arXiv:2604.18919v1 Announce Type: new Abstract: Analyzing topics extracted from text data in relation to external outcomes is important across fields such as computational social science and organizat
arXiv:2512.06380v3 Announce Type: replace-cross Abstract: Audio Large language models (LLMs) are increasingly deployed in the real world, where they inevitably capture speech from unintended nearby by
arXiv:2506.06211v2 Announce Type: replace-cross Abstract: Puzzlehunts are a genre of complex, multi-step puzzles lacking well-defined problem definitions. In contrast to conventional reasoning benchma
arXiv:2604.00161v2 Announce Type: replace Abstract: Optical Character Recognition (OCR) is increasingly regarded as a foundational capability for modern vision-language models (VLMs), enabling them no
arXiv:2601.00679v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been emerging as prominent AI models for solving many natural language tasks due to their high performance (
arXiv:2508.20467v2 Announce Type: replace-cross Abstract: In the highly volatile and uncertain global financial markets, traditional quantitative trading models relying on statistical modeling or empi
arXiv:2604.19344v1 Announce Type: new Abstract: Robotic parkour provides a compelling benchmark for advancing locomotion over highly challenging terrain, including large discontinuities such as elevat
arXiv:2510.10866v3 Announce Type: replace-cross Abstract: Measuring dataset similarity is fundamental in machine learning, particularly for transfer learning and domain adaptation. In the context of s
arXiv:2604.18621v1 Announce Type: cross Abstract: Quantum machine learning offers a promising new paradigm for computational biology by leveraging quantum mechanical principles to enhance cancer class
arXiv:2604.18838v1 Announce Type: new Abstract: This research investigates the performance and efficacy of machine learning models in stock prediction, comparing Artificial Neural Networks (ANNs), Qua
arXiv:2503.03023v2 Announce Type: replace Abstract: We study non-linear bandit optimization where the learner maximizes a black-box function with zeroth order function oracle, which has been successfu
arXiv:2509.05890v3 Announce Type: replace-cross Abstract: Quantum reinforcement learning has emerged as a framework combining quantum computation with sequential decision-making, and applications to t
arXiv:2604.18995v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive generation by enabling parallel token prediction. Ho
arXiv:2604.19349v1 Announce Type: new Abstract: Monocular scene flow estimation aims to recover dense 3D motion from image sequences, yet most existing methods are limited to two-frame inputs, restric
arXiv:2604.19499v1 Announce Type: new Abstract: This article introduces two new measures for authorship attribution - Rank-Turbulence Delta and Jensen-Shannon Delta - which generalise Burrows's classi
arXiv:2604.19047v1 Announce Type: cross Abstract: Existing QA benchmarks typically assume distinct documents with minimal overlap, yet real-world retrieval-augmented generation (RAG) systems operate o
arXiv:2604.19321v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) remains structurally uncertain despite parameter-efficient methods such as Low-Rank Adaptation (LoRA), as the
arXiv:2512.19442v3 Announce Type: replace-cross Abstract: Diffusion-based generative models have greatly impacted the speech processing field in recent years, exhibiting high speech naturalness and sp
arXiv:2512.00676v2 Announce Type: replace Abstract: Isolated digit classification has served as a motivating problem for decades of machine learning research. In real settings, numbers often occur as
arXiv:2604.19172v1 Announce Type: new Abstract: The rapid advancement and widespread adoption of Large Language Models (LLMs) have elevated the need for reliable AI-generated content (AIGC) detection,
arXiv:2601.04562v2 Announce Type: replace Abstract: Generative recommendation with large language models (LLMs) reframes prediction as sequence generation, yet existing LLM-based recommenders remain l
arXiv:2604.18946v1 Announce Type: new Abstract: Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. This
arXiv:2512.13684v2 Announce Type: replace Abstract: We present Recurrent Video Masked-Autoencoders (RVM): a novel approach to video representation learning that leverages recurrent computation to mode
arXiv:2602.05416v2 Announce Type: replace-cross Abstract: While proper orthogonal decomposition (POD)-based surrogates are widely explored for hydrodynamic applications, the use of Koopman autoencoder
arXiv:2604.19079v1 Announce Type: cross Abstract: Unification of automatic speech recognition (ASR) systems reduces development and maintenance costs, but training a single model to perform well in bo
arXiv:2510.16822v3 Announce Type: replace-cross Abstract: Coral reefs are rapidly declining under anthropogenic pressures (e.g., climate change), creating an urgent need for scalable and automated mon
arXiv:2604.19144v1 Announce Type: new Abstract: Recent years have witnessed growing interest in applying Large Reasoning Models (LRMs) to Machine Translation (MT). Existing approaches predominantly ad
arXiv:2604.19049v1 Announce Type: cross Abstract: LLM-assisted defect discovery has a precision crisis: plausible-but-wrong reports overwhelm maintainers and degrade credibility for real findings. We
arXiv:2409.06080v2 Announce Type: replace-cross Abstract: We demonstrate the ability of large language models (LLMs) to perform material and molecular property regression tasks, a significant deviatio
arXiv:2604.18893v1 Announce Type: cross Abstract: A series of high-profile tragedies involving companion chatbots has triggered an unusually rapid regulatory response. Several jurisdictions, including
arXiv:2604.19720v1 Announce Type: new Abstract: Human video generation remains challenging due to the difficulty of jointly modeling human appearance, motion, and camera viewpoint under limited multi-
arXiv:2604.19104v1 Announce Type: cross Abstract: Developing bipedal football robots in dynamiccombat environments presents challenges related to motionstability and deep coupling of multiple tasks, a
arXiv:2604.19060v1 Announce Type: new Abstract: Accurate disease classification from radiology reports is essential for many applications. While supervised fine-tuning (SFT) of lightweight LLMs improv
arXiv:2604.19099v1 Announce Type: cross Abstract: Education is not merely the transmission of information or the optimisation of individual performance; it is a fundamentally social, constructive, and
arXiv:2604.18738v1 Announce Type: new Abstract: Masked diffusion language models such as LLaDA2.1 rely on Token-to-Token (T2T) editing to correct their own generation errors: whenever a different toke
arXiv:2505.06335v2 Announce Type: replace-cross Abstract: Federated Learning (FL) has the potential for simultaneous global learning amongst a large number of parallel agents, enabling emerging AI suc
arXiv:2509.13281v5 Announce Type: replace Abstract: Current safety evaluations of language models rely on benchmark-based assessments that may miss localized vulnerabilities. We present RepIt, a simpl
arXiv:2503.16251v2 Announce Type: replace-cross Abstract: Federated Learning (FL) has gained prominence in machine learning applications across critical domains by enabling collaborative model trainin
arXiv:2603.26815v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems for financial document question answering typically follow a chunk-based paradigm: documents are
arXiv:2410.03294v4 Announce Type: replace Abstract: This study addresses the deployment challenges of integer-only quantized Transformers on resource-constrained embedded FPGAs (Xilinx Spartan-7 XC7S1
arXiv:2604.18811v1 Announce Type: cross Abstract: Despite the perceived success of large-scale dataset distillation (DD) methods, recent evidence finds that simple random image baselines perform on-pa
arXiv:2505.20816v2 Announce Type: replace Abstract: Recent advances in multimodal question answering have primarily focused on combining heterogeneous modalities or fine-tuning multimodal large langua
arXiv:2604.19299v1 Announce Type: cross Abstract: Despite the impressive capabilities of large language models, their substantial computational costs, latency, and privacy risks hinder their widesprea
arXiv:2604.19523v1 Announce Type: new Abstract: Social deduction games such as Mafia present a unique AI challenge: players must reason under uncertainty, interpret incomplete and intentionally mislea
arXiv:2604.18757v1 Announce Type: cross Abstract: The retina provides a unique, noninvasive window into Alzheimer's disease (AD) and dementia, capturing early structural changes through morphometric f