Knowledge catalogue
arXiv:2605.16748v1 Announce Type: cross Abstract: Recent advancements in generative video models demonstrate high visual fidelity, yet their integration into enterprise environments is restricted by t
arXiv:2507.21035v3 Announce Type: replace Abstract: Gene expression analysis holds the key to many biomedical discoveries, yet extracting insights from raw transcriptomic data remains formidable due t
arXiv:2605.17804v1 Announce Type: new Abstract: Generative models have demonstrated remarkable potential in time series analysis tasks, like synthesis, forecasting, imputation, etc. However, offering
arXiv:2605.18365v1 Announce Type: new Abstract: Generating geometrically consistent videos remains an open challenge: text-to-video diffusion models trained on web-scale data treat geometry only impli
arXiv:2605.17354v1 Announce Type: new Abstract: Monocular 3D hand reconstruction is intrinsically a geometric problem, yet RGB appearance features alone often struggle to resolve severe ambiguities ca
arXiv:2605.16349v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures achieve scalable capacity through sparse routing, yet the geometric structure of expert specialization remains po
arXiv:2605.18276v1 Announce Type: cross Abstract: Learning dynamical systems through operator-theoretic representations provides a powerful framework for analyzing complex dynamics, as spectral quanti
arXiv:2512.23752v5 Announce Type: replace-cross Abstract: Recent work has shown that small transformers trained in controlled 'wind-tunnel'' settings can implement exact Bayesian inference, and that t
arXiv:2507.01099v4 Announce Type: replace-cross Abstract: Understanding and predicting dynamics of the physical world can enhance a robot's ability to plan and interact effectively in complex environm
arXiv:2603.02531v2 Announce Type: replace-cross Abstract: Classifier-Free Guidance (CFG) improves sample quality in diffusion models, but its dual-pass inference and reliance on null-condition trainin
arXiv:2605.18543v1 Announce Type: new Abstract: Autonomous ground vehicles operating in shallow water or flood-prone terrains require dynamic models that account for hydrodynamic forces. However, the
arXiv:2605.18419v1 Announce Type: cross Abstract: Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology
arXiv:2505.20914v2 Announce Type: replace Abstract: General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties, while simul
arXiv:2602.08206v2 Announce Type: replace Abstract: Open-vocabulary semantic segmentation has become an important direction in remote sensing, as it enables recognition beyond predefined land-cover ca
arXiv:2605.16371v1 Announce Type: cross Abstract: Large Multimodal Models (LMMs) often struggle with geometric reasoning due to visual hallucinations and a lack of mathematically precise Chain-of-Thou
arXiv:2602.23058v2 Announce Type: replace Abstract: Energy-based predictive world models provide a powerful approach for multi-step visual planning by reasoning over latent energy landscapes rather th
arXiv:2605.16713v1 Announce Type: cross Abstract: Modern Vision-Language Models (VLMs) achieve strong semantic recognition, yet remain brittle on elementary spatial relations such as left of, on, behi
arXiv:2506.13189v3 Announce Type: replace-cross Abstract: Robot teleoperation via augmented reality (AR) offers a promising path toward more intuitive human-robot interaction (HRI). We present a head-
arXiv:2605.18663v1 Announce Type: new Abstract: As LLM benchmarks saturate, the evaluation community has pursued two strategies to increase difficulty: escalating knowledge demands (GPQA, HLE) or remo
arXiv:2602.18584v2 Announce Type: replace-cross Abstract: Targeted data selection has emerged as a crucial paradigm for efficient instruction tuning, aiming to identify a small yet influential subset
arXiv:2605.17086v1 Announce Type: cross Abstract: Automation affects the labour content of work differently across different contexts. Yet, most existing exposure measures assign fixed scores to tasks
arXiv:2605.16520v1 Announce Type: new Abstract: Sampling-based optimization (SBO), like cross-entropy method and evolutionary algorithms, has achieved many successes in solving non-convex problems wit
arXiv:2602.20200v2 Announce Type: replace-cross Abstract: Hierarchical Vision-Language-Action (VLA) models have rapidly become a dominant paradigm for robotic manipulation. It typically comprising a V
arXiv:2506.12648v2 Announce Type: replace-cross Abstract: Iteration complexities for optimizing smooth functions with first-order algorithms are typically stated in terms of a global Lipschitz constan
arXiv:2605.16769v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a promising paradigm for adapting pretrained models under limited data conditions. However, most e
arXiv:2605.16345v1 Announce Type: cross Abstract: Large language models often require fine-tuning to better align their behavior with user intent at deployment. Existing approaches are commonly divide
arXiv:2512.09269v2 Announce Type: replace Abstract: Inferring the eventual goal of a mobile agent from noisy observations of its trajectory is a fundamental estimation problem. We initiate the study o
arXiv:2605.17812v1 Announce Type: new Abstract: Vertical AI firms in accounting, law, healthcare, procurement, and similar domains historically bundled workflow, domain logic, and accountability into
arXiv:2511.08154v2 Announce Type: replace-cross Abstract: We revisit the fermion mass problem of the SU(5) grand unified theory using machine learning techniques. The original SU(5) model proposed by
arXiv:2605.16435v1 Announce Type: cross Abstract: Heatwaves are an important problem in cities, and climate change makes this problem more difficult. In this paper, we present a GPU-based deep learnin
arXiv:2506.14009v2 Announce Type: replace Abstract: Autonomous drones capable of interpreting and executing high-level language instructions in unstructured environments remain a long-standing goal. Y
arXiv:2512.22473v5 Announce Type: replace-cross Abstract: Transformers empirically perform precise probabilistic reasoning in carefully constructed ``Bayesian wind tunnels'' and in large-scale languag
arXiv:2602.07884v2 Announce Type: replace-cross Abstract: Survival analysis is complicated by censored data, high-dimensional features, and non-linear interactions. Classical models offer interpretabi
arXiv:2508.02383v2 Announce Type: replace Abstract: Spectral graph embedding plays a critical role in graph representation learning by generating low-dimensional vector representations from graph spec
arXiv:2605.18387v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) and Graph Transformers (GTs) are now a fundamental paradigm for graph learning, combining the representation-learning cap
arXiv:2605.17343v1 Announce Type: new Abstract: Computed tomography (CT) metal artifact reduction (MAR) aims to reduce the severe streaking artifacts induced by metallic implants and other high-densit
arXiv:2605.17617v1 Announce Type: new Abstract: Complex operational workflows coordinating personnel, tools, and information are central to enterprise operations, yet end-to-end automation remains cha
arXiv:2511.19078v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, including multi-step
arXiv:2605.16598v1 Announce Type: cross Abstract: Agentic retrieval improves multi-hop question answering by giving language models autonomy to iteratively gather evidence. Recent work augments these
arXiv:2605.17727v1 Announce Type: new Abstract: Frozen vision-language embeddings contain signals at multiple semantic resolutions, from object identity to attributes, relations, and full-caption mean
arXiv:2605.16668v1 Announce Type: cross Abstract: We introduce GraViti, a transformer-based graph-level variational autoencoder that maps entire graphs to compact latent vectors. This design produces
arXiv:2605.16714v1 Announce Type: new Abstract: Security knowledge graphs can provide computable external memory for security agents, but constructing them from long-form cyber threat intelligence (CT
arXiv:2605.17879v1 Announce Type: cross Abstract: Training frontier-scale foundation models involves coordinating tens of thousands of GPUs over multi-month runs, where even minor performance degradat
arXiv:2411.03936v2 Announce Type: replace Abstract: Generative modelling of multi-user datasets has become prominent in science and engineering. Generating a data point for a given user requires emplo
arXiv:2507.16481v3 Announce Type: replace Abstract: Jumping poses a significant challenge for quadruped robots, despite being crucial for many operational scenarios. While optimisation methods exist f
arXiv:2605.18567v1 Announce Type: new Abstract: Structural equation modeling is widely used in IS research. However, inconsistent construct definitions impede the cumulative development of knowledge.
arXiv:2508.08501v3 Announce Type: replace Abstract: We introduce GVGAI-LLM, a video game benchmark for evaluating the reasoning and problem-solving capabilities of large language models (LLMs). Built
arXiv:2605.16873v1 Announce Type: new Abstract: Diffusion priors have recently demonstrated strong capability in enhancing the quality of sparse-view 3D reconstruction by augmenting training views at
arXiv:2605.17007v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable progress in natural language generation, but remain susceptible to hallucination. In response to g
arXiv:2605.16389v1 Announce Type: cross Abstract: Haptic rendering of viscoelastic materials that exhibit creep and stress relaxation is crucial for many applications, such as medical training with re
arXiv:2605.16966v1 Announce Type: new Abstract: Solving inverse partial differential equation (PDE) problems is a fundamental topic in scientific research due to its broad significance across a wide r
arXiv:2605.17734v1 Announce Type: new Abstract: Equipping LLM agents with reusable skills derived from past experience has become a popular and successful approach for tackling complex and long-horizo
arXiv:2603.20421v2 Announce Type: replace-cross Abstract: We present Hawkeye, a system for analyzing and reproducing GPU-level arithmetic operations. Using our framework, anyone can re-execute on a CP
arXiv:2605.17300v1 Announce Type: new Abstract: We introduce HCLM, a hierarchical framework for general-purpose cooperative loco-manipulation with dual quadrupedal systems. Coordinating multi-robot co
arXiv:2506.23287v3 Announce Type: replace Abstract: In single-cell research, tracing and analyzing high-throughput single-cell differentiation trajectories is crucial for understanding biological proc
arXiv:2605.17093v1 Announce Type: cross Abstract: Distilling vision-language models into faster hybrid architectures, such as 3:1 Mamba-2/attention mixes, is now standard practice for making inference
arXiv:2602.16346v3 Announce Type: replace Abstract: LLM-based agents execute real-world workflows via tools and memory. These affordances enable ill-intended adversaries to also use these agents to ca
arXiv:2605.16268v1 Announce Type: cross Abstract: Banks receive millions of reports of fraud, scams, and disputed transactions every year, making it challenging to accurately direct customers to the a
arXiv:2603.20380v2 Announce Type: replace-cross Abstract: Industry practitioners and academic researchers regularly use multi-agent systems to accelerate their work, but the applications through which
arXiv:2605.17393v1 Announce Type: new Abstract: Coordination graphs are a central abstraction in cooperative multi-agent reinforcement learning (MARL), yet existing sparse-graph learners lack a theore