Knowledge catalogue
arXiv:2604.22739v1 Announce Type: new Abstract: Social interactions dominate our perceptions of the world and shape our daily behavior by attaching social meaning to acts as simple and spontaneous as
arXiv:2512.16251v5 Announce Type: replace-cross Abstract: We introduce the Consensus-Bottleneck Asset Pricing Model (CB-APM), which embeds aggregate analyst consensus as a structural bottleneck, treat
arXiv:2507.03014v2 Announce Type: replace-cross Abstract: Large language models (LLMs) face significant copyright and intellectual property challenges as the cost of training increases and model reuse
arXiv:2604.22411v1 Announce Type: new Abstract: Even when decoding with temperature T=0, large language models (LLMs) can produce divergent outputs for identical inputs. Recent work by Thinking Machin
arXiv:2604.22672v1 Announce Type: new Abstract: Batch processes are inherently transient and typically nonlinear, motivating nonlinear model predictive control (NMPC). However, adopting NMPC is hinder
arXiv:2601.11719v3 Announce Type: replace Abstract: Self-supervised learning, in the context of foundation model training, is a powerful pre-training method for learning feature representations withou
arXiv:2602.05639v4 Announce Type: replace Abstract: We introduce Variational Joint Embedding (VJE), a reconstruction-free latent-variable framework for non-contrastive self-supervised learning in repr
arXiv:2604.22032v1 Announce Type: new Abstract: Every ML kernel ships with an implicit contract about what it computes. People rarely write the contract down. When two kernels disagree -- when a matmu
arXiv:2604.22098v1 Announce Type: new Abstract: Time introduces fundamental challenges in model development and deployment: models are usually trained on historical data while deployed on future data
arXiv:2604.22302v1 Announce Type: new Abstract: Recent text-to-image (T2I) models have demonstrated impressive capabilities in photorealistic synthesis and instruction following. However, their reliab
arXiv:2508.05633v2 Announce Type: replace-cross Abstract: Live streaming platforms have become a dominant form of online content consumption, offering dynamically evolving content, real-time interacti
arXiv:2505.14990v3 Announce Type: replace Abstract: Often, multilingual language models are trained with the objective to map semantically similar content (in different languages) in the same latent s
arXiv:2604.22266v1 Announce Type: new Abstract: Large Language Models often achieve strong performance by generating long intermediate chain-of-thought reasoning. However, it remains unclear when a mo
arXiv:2602.17681v2 Announce Type: replace-cross Abstract: Post-training quantization (PTQ) is a widely used approach for reducing the memory and compute costs of large language models (LLMs). Recent s
arXiv:2604.22050v1 Announce Type: cross Abstract: Transformers are mostly relying on softmax attention, which introduces quadratic complexity with respect to sequence length and remains a major bottle
arXiv:2604.22235v1 Announce Type: cross Abstract: Industrial robots are widely used in manufacturing, yet most manipulation still depends on fixed waypoint scripts that are brittle to environmental ch
arXiv:2604.22244v1 Announce Type: new Abstract: Ensuring safety for black-box hybrid dynamical systems presents significant challenges due to their instantaneous state jumps and unknown explicit nonli
arXiv:2604.22056v1 Announce Type: new Abstract: Optimal wireless transmitter placement is a central task in radio-network planning, yet exhaustive search becomes prohibitively expensive at scale. This
arXiv:2604.22565v1 Announce Type: cross Abstract: Large Language Models (LLMs) can reason well, yet often miss decisive evidence when it is buried in long, noisy contexts. We introduce HiLight, an Evi
arXiv:2508.10695v2 Announce Type: replace-cross Abstract: Personalization is crucial for enhancing both the effectiveness and user satisfaction of language technologies, particularly in information-se
arXiv:2604.22164v1 Announce Type: new Abstract: Recent advances in deep learning have enabled the generation of videos from textual descriptions as well as the prediction of future sequences from inpu
arXiv:2604.22363v1 Announce Type: cross Abstract: Household environments present one of the most common, impactful yet challenging application domains for robotics. Within household scenarios, manipul
arXiv:2510.12328v5 Announce Type: replace Abstract: Accurate rainfall forecasting, particularly for extreme events, remains a significant challenge in climatology and the Earth system. This paper pres
arXiv:2604.01907v2 Announce Type: replace-cross Abstract: Annotated 3D scene data is scarce and expensive to acquire, while abundant unlabeled videos are readily available on the internet. In this pap
arXiv:2604.22061v1 Announce Type: cross Abstract: Patient-trial matching requires reasoning over long, heterogeneous electronic health records (EHRs) and complex eligibility criteria, posing significa
arXiv:2604.02861v2 Announce Type: replace-cross Abstract: The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing s
arXiv:2411.08027v3 Announce Type: replace-cross Abstract: Most learning-based approaches to complex physical reasoning sidestep the crucial problem of parameter identification (e.g., mass, friction) t
arXiv:2601.08919v2 Announce Type: replace-cross Abstract: A good deal of recent research has focused on how Large Language Models (LLMs) may be used as judges in place of humans to evaluate the qualit
arXiv:2604.22161v1 Announce Type: new Abstract: We study the K-armed logistic bandit problem, where at each round, the agent observes K feature vectors associated with K actions. Existing approaches t
arXiv:2604.22714v1 Announce Type: new Abstract: Internet photo collections exhibit an extremely long-tailed distribution: a few famous landmarks are densely photographed and easily reconstructed in 3D
arXiv:2604.22034v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) are a recent neural network architecture offering an alternative to Multilayer Perceptrons (MLPs) with improved expl
arXiv:2602.09329v2 Announce Type: replace Abstract: Quality benchmarks are essential for fairly and accurately tracking scientific progress and enabling practitioners to make informed methodological c
arXiv:2604.21957v1 Announce Type: cross Abstract: Recent works have demonstrated that attention-based transformer and large language model (LLM) architectures can achieve strong channel state predicti
arXiv:2506.16494v3 Announce Type: replace Abstract: Electrocardiograms (ECGs) provide non-invasive measurements of heart activity and are established tools for detecting cardiac arrhythmias. Although
arXiv:2604.21935v1 Announce Type: new Abstract: Although language models demonstrate remarkable proficiency on mathematical benchmarks, it remains unclear whether this reflects true mathematical reaso
arXiv:2604.22503v1 Announce Type: new Abstract: Hundreds of millions of people use artificial intelligence (AI) for writing assistance. Here, we evaluated how AI writing assistance distorts writer per
arXiv:2512.05794v2 Announce Type: replace-cross Abstract: Sparse autoencoders (SAEs) are a mechanistic interpretability technique that have been used to provide insight into learned concepts within la
arXiv:2604.22085v1 Announce Type: new Abstract: The transition from stateless language model inference to persistent, multi session autonomous agents has revealed memory to be a primary architectural
arXiv:2604.22633v1 Announce Type: cross Abstract: The Gaussian mixture model is widely used in unsupervised learning, owing to its simplicity and interpretability. However, a fundamental limitation of
arXiv:2604.22031v1 Announce Type: cross Abstract: We propose Mochi, a Graph Foundation Model that addresses task unification and training efficiency by adopting a meta-learning based training framewor
arXiv:2604.21989v1 Announce Type: cross Abstract: The problem of controlling hybrid dynamical systems using model predictive control (MPC) is formulated and sufficient conditions for asymptotic stabil
arXiv:2604.21937v1 Announce Type: new Abstract: Computational drug discovery, particularly the complex workflows of drug molecule screening and optimization, requires orchestrating dozens of specializ
arXiv:2509.22166v4 Announce Type: replace-cross Abstract: The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N
arXiv:2604.22492v1 Announce Type: cross Abstract: Understanding social dominance in animal behavior is critical for neuroscience and behavioral studies. In this work, we explore the capability of Mult
arXiv:2604.22548v1 Announce Type: cross Abstract: Problem definition: Data-driven models in machine learning have enabled efficient management of production systems. However, a majority of machine lea
arXiv:2604.21991v1 Announce Type: cross Abstract: Multi-task optimization is a powerful approach for solving a large number of tasks in parallel. However, existing algorithms face distinct limitations
arXiv:2602.06019v2 Announce Type: replace Abstract: Existing techniques for accelerating language model inference, such as speculative decoding, require training auxiliary speculator models and buildi
arXiv:2604.22212v1 Announce Type: cross Abstract: In spite of the utility of 3-D electron back-scattered diffraction (EBSD) microscopy, the data collection process can be time-consuming with serial-se
arXiv:2510.03248v3 Announce Type: replace-cross Abstract: Background: Traumatic brain injury modeling requires integrating volumetric neuroimaging, demographic parameters, and acquisition metadata. Fi
arXiv:2410.21548v3 Announce Type: replace Abstract: Large language models have drastically changed the prospects of AI by introducing technologies for more complex natural language processing. However
arXiv:2604.22239v1 Announce Type: cross Abstract: This paper introduces the task of analytical question answering over large, semi-structured document collections. We present MuDABench, a benchmark fo
arXiv:2408.16286v5 Announce Type: replace Abstract: Designing a safe policy for uncertain environments is crucial in real-world control systems. However, this challenge remains inadequately addressed
arXiv:2604.22158v1 Announce Type: cross Abstract: We study the problem of adaptive control of the stochastic linear quadratic regulator (LQR) with constraints that must be satisfied at every time step
arXiv:2604.22730v1 Announce Type: cross Abstract: We investigate whether neural models trained exclusively on modern morphological data can recover cross-lingual lexical structure consistent with hist
arXiv:2510.25977v4 Announce Type: replace Abstract: Emerging AI accelerators have started to gain attention and offer new opportunities for efficient inference of large language models (LLMs). Trainiu
arXiv:2511.07003v2 Announce Type: replace Abstract: Large language models have significantly advanced Multilingual Machine Translation (MMT), yet scaling to many languages while keeping quality robust
arXiv:2604.22518v1 Announce Type: new Abstract: We introduce NONSAC (Non-Minimal Sampling and Consensus), a general framework for robust and scalable model estimation from arbitrarily large datasets c
arXiv:2604.22439v1 Announce Type: new Abstract: We propose a neural regularization method that refines the noisy 3D semantic field produced by lifting multi-view inconsistent 2D features, in order to
arXiv:2509.20886v2 Announce Type: replace Abstract: Video sequences often contain structured noise and background artifacts that obscure dynamic content, posing challenges for accurate analysis and re
arXiv:2604.22005v1 Announce Type: cross Abstract: Accurate yet low-latency channel state information (CSI) acquisition is essential for multiple-input multiple-output (MIMO) communication systems. Whi