Knowledge catalogue
arXiv:2605.22893v1 Announce Type: cross Abstract: We introduce a novel Longitudinal Focused Attention Meditation Electroencephalography (L-FAME) dataset and an accompanying benchmark, designed to fost
arXiv:2605.23198v1 Announce Type: new Abstract: Dataset pruning reduces the storage and training costs of deep learning by selecting an informative subset from a large dataset. However, most existing
arXiv:2511.02239v2 Announce Type: replace-cross Abstract: Learning generalizable policies for robotic manipulation increasingly relies on large-scale models that map language instructions to actions (
arXiv:2605.23878v1 Announce Type: new Abstract: Modern video generators produce visually compelling clips but still struggle with physical and motion consistency, limiting their use as reliable world
arXiv:2605.23287v1 Announce Type: new Abstract: We present LangFlash, a feed-forward framework for 3D Language Gaussian Splatting that reconstructs 3D scenes parameterized by Gaussian primitives enric
arXiv:2605.22863v1 Announce Type: new Abstract: LLM agents today communicate via text, which incurs considerable latency and information loss due to the need to autoregressively decode the sharer mode
arXiv:2605.22939v1 Announce Type: new Abstract: We aim to improve the reasoning capabilities of diffusion language models (DLMs). While SFT is a popular post-training recipe for autoregressive models,
arXiv:2605.22967v1 Announce Type: new Abstract: When Masked Diffusion Models (MDMs) generate sequences through iterative refinement, the rich internal computation over masked positions is discarded, f
arXiv:2605.23845v1 Announce Type: new Abstract: Data-driven learning approaches for physics simulation, sometimes referred to as world models, have emerged as promising alternatives to traditional phy
arXiv:2605.23255v1 Announce Type: new Abstract: Learning-augmented algorithms have emerged as a powerful paradigm to surpass traditional worst-case lower bounds by integrating potentially noisy predic
arXiv:2603.18551v2 Announce Type: replace-cross Abstract: We study how to construct compressed datasets that suffice to recover optimal decisions in linear programs with an unknown cost vector c lying
arXiv:2605.23708v1 Announce Type: new Abstract: The robustness of synchronization is typically characterized by scalar, per-node stability indices whose dependence on topology is studied via network s
arXiv:2605.23470v1 Announce Type: cross Abstract: Predicting how a dynamical unit evolves over time - how an individual ages, an epidemic spreads, or a physical system degrades - typically requires de
arXiv:2605.23650v1 Announce Type: cross Abstract: Human feedback often arrives as preferences rather than calibrated numeric rewards, motivating reinforcement learning from preferential feedback, also
arXiv:2605.23510v1 Announce Type: new Abstract: When learning dynamical systems from data, embedding physical structure can constrain the solution space and improve generalization, but many physics-in
arXiv:2603.22348v3 Announce Type: replace Abstract: Online learning algorithms often face a fundamental trilemma: balancing regret guarantees between adversarial and stochastic settings and providing
arXiv:2605.23645v1 Announce Type: cross Abstract: In the context of artificial neural networks, subliminal learning refers to the transfer of task-relevant knowledge or unintended biases from teacher
arXiv:2605.23643v1 Announce Type: cross Abstract: Tools like Tamarin and ProVerif have achieved notable success in analyzing and verifying complex real-world protocols such as EMV, 5G, and WPA2, even
arXiv:2605.23861v1 Announce Type: cross Abstract: Causal generative modeling is essential for developing reliable and transparent AI systems capable of counterfactual reasoning. While existing approac
arXiv:2605.22829v1 Announce Type: cross Abstract: Multimodal Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for enhancing Large Language Models (LLMs) with external knowledg
arXiv:2602.12534v2 Announce Type: replace-cross Abstract: In truncated linear regression, samples (x,y) are shown only when the outcome y falls inside a certain survival set S^star and the goal is to
arXiv:2605.23203v1 Announce Type: cross Abstract: The adoption of vision neural networks in regulated industries requires formal robustness guarantees, especially in safety-critical domains such as he
arXiv:2601.18685v3 Announce Type: replace-cross Abstract: The capabilities of generative AI in mathematics education are rapidly evolving, posing significant challenges for research to keep pace. Rese
arXiv:2605.22976v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software systems for diverse purposes, due to their versatility, flexibility, and abilit
arXiv:2605.23754v1 Announce Type: new Abstract: Developing constitutive models that capture how materials deform under load traditionally requires years of specialized expertise in continuum mechanics
arXiv:2605.23102v1 Announce Type: cross Abstract: Large language models (LLMs) offer a scalable mechanism to elicit domain-informed prior information for high-dimensional variable selection. However,
arXiv:2605.23901v1 Announce Type: cross Abstract: Existing scaling laws for Large Language Models (LLMs), predominantly monotonic power laws, fail to explain emerging non-monotonic phenomena such as c
arXiv:2601.14300v3 Announce Type: replace Abstract: Hard-label black-box attacks, relying solely on top-1 predictions, represent one of the most challenging yet practically threat models. Despite rece
arXiv:2605.23174v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) enables non-contact measurement of physiological signals from facial videos, offering strong potential for remote hea
arXiv:2605.23777v1 Announce Type: new Abstract: The grading of gemstones is currently a manual procedure performed by gemologists. A popular approach uses reference stones, where those are visually in
arXiv:2605.23007v1 Announce Type: cross Abstract: We explore the application of LLM-driven algorithm optimization to several common tasks in quantitative finance. MadEvolve, a general-purpose algorith
arXiv:2602.04431v2 Announce Type: replace Abstract: LLM-based multi-agent systems have demonstrated impressive capabilities, but they also introduce significant safety risks when individual agents fai
arXiv:2603.10688v2 Announce Type: replace-cross Abstract: Autonomous vehicles rely on map information to understand the world around them. However, the creation and maintenance of offline high-definit
arXiv:2605.22949v1 Announce Type: new Abstract: Foundation model agents increasingly operate in multi-agent deployments where a coordinator must decide which agent's response to trust. The standard ap
arXiv:2605.23563v1 Announce Type: new Abstract: Comprehensive evaluation of machine learning models is the key to make sure that they perform as robustly and consistently as desired. In order to summa
arXiv:2601.14652v5 Announce Type: replace Abstract: While multi-agent systems (MAS) promise elevated intelligence through coordination of agents, current approaches to automatic MAS design under-deliv
arXiv:2605.23507v1 Announce Type: new Abstract: The DEtection TRansformer (DETR) is a powerful end-to-end object detector, yet its one-to-one matching strategy suffers from slow convergence and low re
arXiv:2605.22872v1 Announce Type: cross Abstract: Experienced physicians develop diagnostic expertise through clinical practice, acquiring not only disease knowledge but also the ability to differenti
arXiv:2605.22900v1 Announce Type: new Abstract: Mediative Fuzzy Logic was conceived as a practical scheme for reconciling hesitant or conflicting assessments in fuzzy control and decision-making. Howe
arXiv:2510.26411v2 Announce Type: replace Abstract: Artificial intelligence in healthcare requires models that are accurate and interpretable. We advance mechanistic interpretability in medical vision
arXiv:2602.13480v2 Announce Type: cross Abstract: Launchpads have become the dominant mechanism for issuing memecoins, exposing investors to a new class of high-risk launches that existing rug-pull de
arXiv:2605.23723v1 Announce Type: new Abstract: Large language model agents increasingly rely on persistent memory to store past interactions, retrieve relevant demonstrations, and improve long-horizo
arXiv:2605.22981v1 Announce Type: cross Abstract: Fill-in-the-middle (FIM) is a pretraining objective widely used to equip causal language models with infilling ability, yet its effect on verbatim mem
arXiv:2605.23384v1 Announce Type: cross Abstract: Recent RL methods have substantially improved the reasoning abilities of LLMs. Existing reward designs mainly follow two paradigms: (1) Reinforcement
arXiv:2605.23701v1 Announce Type: new Abstract: We study a protocol-level test for weak-label benchmarks: whether benchmark outputs change when the provided evidence is intervened on. Metadata-only sh
arXiv:2605.23064v1 Announce Type: new Abstract: Body shape and circumferences are clinically informative biomarkers for risk stratification, including measures such as waist to hip ratio, limb and tru
arXiv:2406.09250v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly susceptible to sophisticated adversarial attacks, including adaptive strategies specifically de
arXiv:2507.12455v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have revolutionized cross-modal understanding but continue to struggle with hallucinations - fabricated con
arXiv:2605.23054v1 Announce Type: cross Abstract: Model collapse, the progressive degradation of LLMs trained on their own outputs, has been characterized statistically but lacks a linguistic explanat
arXiv:2605.23057v1 Announce Type: cross Abstract: ModeSwitch-LLM is a lightweight request-boundary controller for improving single-GPU large language model inference efficiency by routing each request
arXiv:2402.14212v4 Announce Type: replace-cross Abstract: Backpropagation's main limitation is its need to store intermediate activations (residuals) during the forward pass, which restricts the depth
arXiv:2605.23871v1 Announce Type: cross Abstract: We develop a gradient flow on the space of probability measures defined on matrix-valued parameters induced by regularized Muon, an analytically smoot
arXiv:2605.23840v1 Announce Type: new Abstract: Mueller matrix imaging provides rich, physically meaningful contrast for biomedical tissue analysis, but supervised learning is hindered by scarce dense
arXiv:2605.23350v1 Announce Type: new Abstract: Autonomous exploration of multi-floor buildings remains challenging for ground robots because conventional 2D and 2.5D maps cannot represent overlapping
arXiv:2605.23259v1 Announce Type: cross Abstract: While Attention Residuals has shown some effectiveness in addressing the widespread issue of unbounded activation growth across deep residual layers,
arXiv:2505.17015v2 Announce Type: replace-cross Abstract: Multi-modal large language models (MLLMs) have rapidly advanced in visual tasks, yet their spatial understanding remains limited to single ima
arXiv:2605.23885v1 Announce Type: new Abstract: Cross-lingual knowledge transfer is critical for building high-performing multilingual language models for languages with insufficient training data. Wh
arXiv:2605.23036v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) enable feature-level mechanistic interpretability and activation steering in large language models (LLMs), but SAE-based lang
arXiv:2602.20210v2 Announce Type: replace-cross Abstract: Crystal modeling spans a family of conditional and unconditional generation tasks, including crystal structure prediction (CSP) and de novo ge
arXiv:2605.23482v1 Announce Type: cross Abstract: Dataset distillation compresses large training sets into compact synthetic datasets while preserving downstream performance. As modern systems increas