Knowledge catalogue
arXiv:2604.07569v1 Announce Type: cross Abstract: Despite the increasing prevalence of large language models (LLMs), we still have a limited understanding of how their representational spaces are stru
arXiv:2604.06255v1 Announce Type: cross Abstract: Stellar astrophysics relies critically on accurate descriptions of the physical conditions inside stars. Traditional solvers such as exttt{MESA} (Mo
arXiv:2603.10476v2 Announce Type: replace Abstract: LLM alignment has progressed in single-agent settings through paradigms such as RL with human feedback (RLHF), while recent work explores scalable a
arXiv:2604.07027v1 Announce Type: new Abstract: Nonstationarity is ubiquitous in practical classification settings, leading deployed models to perform poorly even when they generalize well to holdout
arXiv:2604.07146v2 Announce Type: replace Abstract: Knowledge-based visual question answering (KB-VQA) requires vision-language models to understand images and use external knowledge, especially for r
arXiv:2604.08425v1 Announce Type: cross Abstract: When humans label subjective content, they disagree, and that disagreement is not noise. It reflects genuine differences in perspective shaped by anno
arXiv:2604.07799v1 Announce Type: new Abstract: Embodied agents are expected to operate persistently in dynamic physical environments, continuously acquiring new capabilities over time. Existing appro
arXiv:2512.19576v5 Announce Type: replace-cross Abstract: Attitude control is essential for many satellite missions. Classical controllers, however, are time-consuming to design and sensitive to model
arXiv:2511.17844v4 Announce Type: replace-cross Abstract: Fine-tuning large-scale text-to-video diffusion models to add new generative controls, such as those over physical camera parameters (e.g., sh
arXiv:2510.13820v3 Announce Type: replace-cross Abstract: This research proposes an extensive technique for monitoring and controlling the industrial parameters using Internet of Things (IoT) technolo
arXiv:2604.07467v1 Announce Type: new Abstract: Discrete speech units (DSUs) are derived by quantising representations from models trained using self-supervised learning (SSL). They are a popular repr
arXiv:2509.17183v3 Announce Type: replace-cross Abstract: Alignment plays a crucial role in Large Language Models (LLMs) in aligning with human preferences on a specific task/domain. Traditional align
arXiv:2604.07966v1 Announce Type: new Abstract: Diffusion models have achieved remarkable progress in video generation, but their controllability remains a major limitation. Key scene factors such as
arXiv:2505.17209v2 Announce Type: replace Abstract: Recent advances in autonomous driving research towards motion planners that are robust, safe, and adaptive. However, existing rule-based and data-dr
arXiv:2604.06298v1 Announce Type: new Abstract: Recent alignment work on Large Language Models (LLMs) suggests preference optimization can improve reasoning by shifting probability mass toward better
arXiv:2604.08039v1 Announce Type: new Abstract: Interpreting the concepts encoded by individual neurons in deep neural networks is a crucial step towards understanding their complex decision-making pr
arXiv:2604.07886v1 Announce Type: new Abstract: We investigate how and to what extent hierarchical relations (e.g., Japan subset Eastern Asia subset Asia) are encoded in the internal representat
arXiv:2604.06171v1 Announce Type: cross Abstract: Communications networks now form the backbone of our digital world, with fast and reliable connectivity. However, even with appropriate redundancy and
arXiv:2604.08126v1 Announce Type: new Abstract: Objective Structured Clinical Examinations (OSCEs) are the standard method for assessing medical students' clinical and communication skills through str
arXiv:2604.06571v1 Announce Type: cross Abstract: Missing-person and child-safety investigations rely on heterogeneous case documents, including structured forms, bulletin-style posters, and narrative
arXiv:2510.13907v3 Announce Type: replace Abstract: Large language models (LLMs) are highly sensitive to prompts, but most automatic prompt optimization (APO) methods assume access to ground-truth ref
arXiv:2604.06188v1 Announce Type: cross Abstract: People increasingly hold sustained, open-ended conversations with large language models (LLMs). Public reports and early studies suggest that, in such
arXiv:2508.08935v4 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) have attracted considerable attention for their ability to integrate partial differential equation priors i
arXiv:2505.02781v4 Announce Type: replace Abstract: Identifying controlled direct effects (CDEs) is crucial across numerous scientific domains. While existing methods can identify these effects from c
arXiv:2604.07092v2 Announce Type: replace Abstract: In this work, we present LIANet (Location Is All You Need Network), a coordinate-based neural representation that models multi-temporal spaceborne E
arXiv:2509.09926v5 Announce Type: replace Abstract: Long-tailed semi-supervised learning (LTSSL) presents a formidable challenge where models must overcome the scarcity of tail samples while mitigatin
arXiv:2604.06629v1 Announce Type: cross Abstract: We present Logical Robots, an interactive multi-agent simulation platform where autonomous robot behavior is specified declaratively in the logic prog
arXiv:2603.09677v3 Announce Type: replace Abstract: Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the O
arXiv:2502.17421v4 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) can now process extremely long contexts, efficient inference over these extended inputs has become increasingl
arXiv:2506.18841v3 Announce Type: replace-cross Abstract: Ultra-long generation by large language models (LLMs) is a widely demanded scenario, yet it remains a significant challenge due to their maxim
arXiv:2508.20765v2 Announce Type: replace-cross Abstract: The automatic understanding of video content is advancing rapidly. Empowered by deeper neural networks and large datasets, machines are increa
arXiv:2604.07822v1 Announce Type: new Abstract: We study implicit reasoning, i.e. the ability to combine knowledge or rules within a single forward pass. While transformer-based large language models
arXiv:2510.24561v2 Announce Type: replace-cross Abstract: LoRA has become a widely adopted method for PEFT, and its initialization methods have attracted increasing attention. However, existing method
arXiv:2503.18018v2 Announce Type: replace Abstract: We demonstrate that large language models' (LLMs) mathematical reasoning is culturally sensitive: testing 14 models from Anthropic, OpenAI, Google,
arXiv:2604.08333v1 Announce Type: new Abstract: The rise of multimodal large language models (MLLMs) has sparked an unprecedented wave of applications in the field of medical imaging analysis. However
arXiv:2601.11471v3 Announce Type: replace Abstract: The key-value (KV) cache is a primary memory bottleneck in Transformers. We propose Low-Rank Key-Value (LRKV) attention, which reduces KV cache memo
arXiv:2604.07823v1 Announce Type: new Abstract: Performance, the externalization of intent, emotion, and personality through visual, vocal, and temporal behavior, is what makes a character alive. Lear
arXiv:2604.07143v1 Announce Type: new Abstract: We introduce Lumbermark, a robust divisive clustering algorithm capable of detecting clusters of varying sizes, densities, and shapes. Lumbermark iterat
arXiv:2512.17489v2 Announce Type: replace Abstract: Text-to-image (T2I) models have demonstrated remarkable progress in creative image generation, yet they still lack precise control over scene illumi
arXiv:2603.04300v2 Announce Type: replace Abstract: Foundation models in general promise to accelerate scientific computation by learning reusable representations across problem instances, yet constra
arXiv:2604.06737v1 Announce Type: cross Abstract: Large language models have demonstrated remarkable capabilities across a wide range of natural language processing tasks, yet their application in the
arXiv:2512.19253v4 Announce Type: replace-cross Abstract: We present the first empirical study of machine unlearning (MU) in hybrid quantum-classical neural networks. While MU has been extensively exp
arXiv:2604.06950v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) are increasingly being deployed as automated content moderators. Within this landscape, we uncover a critic
arXiv:2604.07276v1 Announce Type: cross Abstract: GROMACS is a de-facto standard for classical Molecular Dynamics (MD). The rise of AI-driven interatomic potentials that pursue near-quantum accuracy a
arXiv:2509.22750v3 Announce Type: replace Abstract: Real-world multi-hop QA is naturally linked with ambiguity, where a single query can trigger multiple reasoning paths that require independent resol
arXiv:2604.06269v1 Announce Type: cross Abstract: Automated cellular reasoning faces a core dichotomy: supervised methods fall into the Reference Trap and fail to generalize to out-of-distribution cel
arXiv:2604.07574v1 Announce Type: new Abstract: Image matching is a fundamental problem in Computer Vision with direct applications in robotics, remote sensing, and geospatial data analysis. We presen
arXiv:2409.09298v2 Announce Type: replace-cross Abstract: The Matrix Profile (MP), a versatile tool for time series data mining, has been shown effective in time series anomaly detection (TSAD). This
arXiv:2604.02445v2 Announce Type: replace Abstract: Matrix Profile (MP) methods are an interpretable and scalable family of distance-based methods for time-series anomaly detection, but strong benchma
arXiv:2603.22364v2 Announce Type: replace-cross Abstract: Diffusion models have achieved state-of-the-art performance in generative modeling, but their success often relies heavily on classifier-free
arXiv:2509.22981v2 Announce Type: replace Abstract: We study a class of multi-stage stochastic programs, which incorporate modeling features from Markov decision processes (MDPs). This class includes
arXiv:2604.07345v1 Announce Type: cross Abstract: The rapid growth of generative artificial intelligence (AI) has introduced unprecedented computational demands, driving significant increases in the e
arXiv:2604.06505v1 Announce Type: cross Abstract: Large language models (LLMs) are widely explored for reasoning-intensive research tasks, yet resources for testing whether they can infer scientific c
arXiv:2604.06846v1 Announce Type: cross Abstract: Interactive medical dialogue benchmarks have shown that LLM diagnostic accuracy degrades significantly when interacting with non-cooperative patients,
arXiv:2604.06180v1 Announce Type: cross Abstract: Medical diagnosis using Large Multimodal Models (LMMs) has gained increasing attention due to capability of these models in providing precise diagnose
arXiv:2604.08203v1 Announce Type: new Abstract: Medical Vision-Language Models (VLMs) hold immense promise for complex clinical tasks, but their reasoning capabilities are often constrained by text-on
arXiv:2604.08364v1 Announce Type: new Abstract: In this paper, we introduce MegaStyle, a novel and scalable data curation pipeline that constructs an intra-style consistent, inter-style diverse and hi
arXiv:2604.07877v1 Announce Type: new Abstract: Long-term memory is fundamental for personalized and autonomous agents, yet populating it remains a bottleneck. Existing systems treat memory extraction
arXiv:2604.06881v1 Announce Type: new Abstract: Neural operators have emerged as powerful surrogates for dynamical systems due to their grid-invariant properties and computational efficiency. However,
arXiv:2507.10303v2 Announce Type: replace-cross Abstract: Stochastic simulators exhibit intrinsic stochasticity due to unobservable, uncontrollable, or unmodeled input variables, resulting in random o