Knowledge catalogue
arXiv:2606.04592v1 Announce Type: cross Abstract: LLM-based digital twins promise to scale and accelerate market research, but most published twins are either coarse persona bots conditioned on a few
arXiv:2510.26219v3 Announce Type: replace-cross Abstract: Test-time alignment of large language models (LLMs) attracts attention because fine-tuning of LLMs requires high computational costs. In this
arXiv:2604.07778v2 Announce Type: replace Abstract: Existing accountability frameworks for AI systems, legal, ethical, and regulatory, rest on a shared assumption: for any consequential outcome, at le
arXiv:2606.04388v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as an effective paradigm for collaborative intelligence while preserving data privacy. However, data heterogeneity
arXiv:2606.04517v1 Announce Type: cross Abstract: Graph-based deep learning methods have been widely employed in encrypted traffic analysis to exploit latent correlations across different granularitie
arXiv:2606.04658v1 Announce Type: cross Abstract: Optimizing urban layouts for climate adaptation requires balancing building density with cold-air ventilation. Because physics-based climate simulatio
arXiv:2606.04410v1 Announce Type: new Abstract: While neural video codecs (NVCs) have demonstrated superior compression ratio, their prohibitive computational complexity remains a critical barrier to
arXiv:2606.04850v1 Announce Type: cross Abstract: Designing a neural network processor is an end-to-end co-design problem: network architecture and training budget determine the inference workload; ha
arXiv:2606.04286v1 Announce Type: new Abstract: Online reviews provide valuable insights into the perceived quality of facets of a product or service. While aspect-based sentiment analysis has focused
arXiv:2602.08142v2 Announce Type: replace Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation. A common approach is to use predictive distributions from
arXiv:2510.27191v5 Announce Type: replace-cross Abstract: Planning under partial observability is an essential capability of autonomous robots. The Partially Observable Markov Decision Process (POMDP)
arXiv:2602.06883v3 Announce Type: replace-cross Abstract: The smoothness of the transformer architecture has been extensively studied in the context of generalization, training stability, and adversar
arXiv:2606.04369v1 Announce Type: new Abstract: Few-shot cross-category 3D anomaly detection aims to determine whether an unknown point cloud belongs to a target normal category using only a few norma
We're presenting ParseBench at CVPR 2026! ParseBench is the most comprehensive document understanding benchmark for VLMs. ✅ It contains 2k pages of real-world enterprise documents ✅ It has comprehensi
arXiv:2603.09242v2 Announce Type: replace Abstract: The growing realism of generative models has blurred the boundary between real and synthetic content, posing significant challenges to reliable AI-g
arXiv:2606.04361v1 Announce Type: cross Abstract: Age of Information (AoI) has become a central metric for the design of wireless update systems, especially in applications where fresh measurements su
arXiv:2606.05159v1 Announce Type: new Abstract: Rigorous evaluation of learning-based robotic systems is an essential prerequisite for deployment. However, real-world test data is expensive to gather;
arXiv:2606.03432v1 Announce Type: cross Abstract: The number of malware (either variant or novel) is rapidly increasing, making malware detection and mitigation a complex problem. One approach to impr
arXiv:2606.02849v1 Announce Type: new Abstract: Interval wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it accounts for the inherent uncertaint
arXiv:2606.02755v1 Announce Type: cross Abstract: Large language model (LLM) applications are increasingly expected to satisfy deterministic institutional requirements while relying on probabilistic g
arXiv:2606.03895v1 Announce Type: cross Abstract: Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model ca
arXiv:2606.03965v1 Announce Type: cross Abstract: Large language models improve final-answer accuracy through extended chain-of-thought reasoning, but often spend tokens inefficiently and offer little
arXiv:2509.26169v2 Announce Type: replace Abstract: Alignment of large language models remains a central challenge in natural language processing. Preference optimization has emerged as a popular and
arXiv:2606.03774v1 Announce Type: new Abstract: Eye tracking is essential for smart glasses, as it provides insight into user attention for ambient intelligence applications. However, most existing ey
arXiv:2606.02832v1 Announce Type: new Abstract: Despite a great deal of prior research into Procedural Content Generation (PCG), relatively little prior work has explored generating enemies for video
arXiv:2606.03631v1 Announce Type: cross Abstract: Multivariate time series classification (MTSC) is pivotal in high-stakes domains, such as clinical diagnosis and industrial fault detection, where saf
arXiv:2602.06842v2 Announce Type: replace-cross Abstract: Deep learning-based hybrid iterative methods (DL-HIMs) integrate classical numerical solvers with neural operators, utilizing their complement
arXiv:2605.11954v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used in social science as scalable measurement tools for converting unstructured text into variables t
arXiv:2604.01410v2 Announce Type: replace Abstract: Text production (and translations) proceeds in the form of stretches of typing, interrupted by keystroke pauses. It is often assumed that fast typin
arXiv:2606.02598v1 Announce Type: new Abstract: Accurate and generalizable estimation of cognitive workload from electroencephalography (EEG) is critical for human-centered and safety-critical systems
arXiv:2606.02604v1 Announce Type: cross Abstract: ESG and climate risk data remain fragmented across heterogeneous Scope 1, Scope 2, and Scope 3 reporting environments, while conventional validation p
arXiv:2606.03666v1 Announce Type: new Abstract: Recent deep unfolding networks (DUNs) have advanced Compressive Sensing (CS) by effectively integrating iterative optimization with deep learning archit
arXiv:2606.02762v1 Announce Type: new Abstract: Knowledge of real-time road slipperiness, or even better, a refined estimate of peak grip potential, is a critical input for vehicle warning and interve
arXiv:2606.03536v1 Announce Type: new Abstract: Expressive whole-body motion is important for humanoid robots operating in human environments, where robots are expected to move stably while presenting
arXiv:2606.03624v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have demonstrated impressive capabilities in many tasks, yet they struggle with reliably following multiple instructions,
arXiv:2606.03296v1 Announce Type: new Abstract: Complex, dynamic, and interactive driving environments pose significant challenges for autonomous driving, primarily due to the pervasive uncertainty of
arXiv:2410.14573v2 Announce Type: replace-cross Abstract: Optimizing costly black-box functions within a constrained evaluation budget presents significant challenges in many real-world applications.
arXiv:2606.03823v1 Announce Type: new Abstract: Urban traffic simulation is a critical tool for infrastructure planning, including the placement of electric vehicle charging stations. However, realist
arXiv:2606.03535v1 Announce Type: cross Abstract: Retrieval effectiveness varies substantially across queries, making it important to estimate ranking quality before relevance judgments are available.
arXiv:2606.03398v1 Announce Type: cross Abstract: Formal languages have proven to be effective conduits to understand the inner mechanisms of transformers. Past work has shown that transformers traine
arXiv:2606.03292v1 Announce Type: cross Abstract: Many systems used in real-world environments require adding new categories and incorporating new information without forgetting what was previously le
arXiv:2606.02581v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) faces a fundamental three-way tension: deeper retrieval improves factual grounding but inflates token costs and e
arXiv:2602.06219v2 Announce Type: replace-cross Abstract: World models offer a promising avenue for more faithfully capturing complex dynamics, including contacts and non-rigidity, as well as complex
arXiv:2511.02417v2 Announce Type: replace Abstract: The adoption of agroecological practices in modern agriculture requires robotic systems capable of operating in highly diverse and complex field env
arXiv:2606.03498v1 Announce Type: new Abstract: Training modern machine learning models increasingly requires computation to be distributed across many accelerators. Data parallelism remains the defau
arXiv:2606.02789v1 Announce Type: new Abstract: Human-object interaction (HOI) recognition is critical for automatically analyzing student behavior in complex educational environments. Although state-
arXiv:2506.03087v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have become essential tools for analyzing graph-structured data in domains such as drug discovery and financial a
arXiv:2606.02982v1 Announce Type: cross Abstract: The rapid growth of large language model (LLM) inference services has increased the demand for efficient multi-tenant GPU scheduling. While modern inf
arXiv:2606.03704v1 Announce Type: new Abstract: Financial decision-making tasks such as stock recommendation and portfolio allocation typically estimate future return and risk and then select trades o
arXiv:2606.03825v1 Announce Type: cross Abstract: Transformers have become the dominant architecture for large language models, largely due to the scalability and flexibility of attention, feed-forwar
arXiv:2606.03770v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become integral to modern applications, yet their deployment remains challenging. Beyond executing the models themse
arXiv:2512.23234v3 Announce Type: replace-cross Abstract: Infrared gas leak detection is important for industrial safety and environmental monitoring, but automatic detection remains challenging becau
arXiv:2606.03073v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language models (LLMs) is highly sensitive to hyperparameter configurations, making hyperparameter optimization
arXiv:2510.15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations. As renewable penetration grows,
arXiv:2606.03812v1 Announce Type: new Abstract: Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems, demand reliable hazard identifica
arXiv:2602.07842v2 Announce Type: replace Abstract: Confidence calibration is essential for making large language models (LLMs) reliable, yet existing training-free methods have been primarily studied
arXiv:2606.03390v1 Announce Type: new Abstract: This work studies ``extreme motion generation'', which aims to maximize the Cartesian path length along a pre-defined trajectory within the manipulator'
arXiv:2606.03199v1 Announce Type: new Abstract: Organic crystal structure prediction (CSP) is a requirement for computational modelling of organic solids, but traditionally costs several CPU-years per
arXiv:2606.03050v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) enables non-contact extraction of blood volume pulse (BVP) signals using consumer-grade cameras. Recent unsupervised
arXiv:2506.01969v3 Announce Type: replace-cross Abstract: Efficient inference of Multi-Head Latent Attention (MLA) is challenged by deploying the DeepSeek-R1 671B model on a single Multi-GPU server. T