Knowledge catalogue
arXiv:2512.08309v4 Announce Type: replace Abstract: For decades, procedural worlds have been built on procedural noise functions such as Perlin noise, which are fast and infinite, yet fundamentally li
arXiv:2605.01409v1 Announce Type: cross Abstract: The growing availability of health-related instructional videos creates new opportunities for clinical training, patient rehabilitation, and health ed
arXiv:2412.15251v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) are effective at capturing the semantics of short video content; however, they often fail to attend to the
arXiv:2605.01742v1 Announce Type: new Abstract: Vision Transformers (ViTs) have achieved strong performance in visual recognition, yet their deployment in resource-constrained industrial environments
arXiv:2605.02288v1 Announce Type: new Abstract: Automated laboratories hold the promise of accelerating scientific discovery, yet their deployment is bottlenecked by the difficulty of designing safe a
arXiv:2605.00899v1 Announce Type: new Abstract: We present LatentDiff, a scalable framework for semantic dataset comparison that operates directly in the latent space of pretrained vision encoders. By
arXiv:2509.10468v2 Announce Type: replace-cross Abstract: Recent advances in generative recommenders adopt a two-stage paradigm: items are first tokenized into semantic IDs using a pretrained tokenize
arXiv:2605.01835v1 Announce Type: new Abstract: Nonlinear coupled systems are ubiquitous in science and engineering. The analysis and modeling of such systems is challenging due to their high dimensio
arXiv:2605.01735v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in real-world systems, they must support post-hoc removal of specific content to meet privacy
arXiv:2509.21173v5 Announce Type: replace Abstract: Vision-Language Models (VLMs) such as CLIP have revolutionized zero-shot classification and safety-critical tasks, including Out-of-Distribution (OO
arXiv:2605.01874v1 Announce Type: new Abstract: The performance of machine learning models often relies on large labeled datasets; however, data collected from diverse sources can contain label noise.
arXiv:2605.01350v1 Announce Type: new Abstract: Detecting machine-generated text is essential for transparency and accountability when deploying large language models (LLMs). Among detection approache
arXiv:2605.01221v1 Announce Type: new Abstract: While diffusion models enable new approaches for estimating Local Intrinsic Dimension (LID), existing methods fail in high-dimensional spaces where nois
arXiv:2506.13157v3 Announce Type: replace-cross Abstract: Artificial Neural Networks (ANNs) are powerful machine-learning models capable of capturing intricate non-linear relationships. They are widel
arXiv:2605.02695v1 Announce Type: new Abstract: SemEval-2026 Task 9 is focused on multilingual polarization detection. Specifically, it covers the identification of multilingual, multicultural and mul
arXiv:2605.02442v1 Announce Type: cross Abstract: In this paper, we offer a guide for researchers on evaluating reasoning in language models, building the case that reasoning should be assessed throug
arXiv:2601.12539v3 Announce Type: replace-cross Abstract: Memes are a dominant medium for online communication and manipulation because meaning emerges from interactions between embedded text, imagery
arXiv:2605.01579v1 Announce Type: cross Abstract: Empirical causal claims depend on many analyst decisions, from selecting covariates to choosing estimators. Existing robustness tools summarize how re
arXiv:2506.05952v4 Announce Type: replace Abstract: Recent advances in transformer-based text-to-motion generation have led to impressive progress in synthesizing high-quality human motion. Neverthele
arXiv:2605.02351v1 Announce Type: new Abstract: Molecular Vibe Coding, a paradigm where chemists interact with LLMs to generate executable programs for molecular tasks, has emerged as a flexible alter
arXiv:2507.14061v2 Announce Type: replace Abstract: What if a robot could rethink its own morphological representation to better meet the demands of diverse tasks? Most robotic systems today treat the
arXiv:2605.01961v1 Announce Type: new Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents. However
Ethan Mollick suggests that Mythos, likely an AI model or system, demonstrates strong capability in identifying and diagnosing pig diseases, possibly indicating its effectiveness in agricultural or ve
arXiv:2605.01075v1 Announce Type: new Abstract: Propagation-based X-ray phase-contrast imaging (PBI) enables high-contrast visualization of lung structures and holds strong medical potential. However,
arXiv:2604.03380v2 Announce Type: replace Abstract: Generating diverse, pedagogically valid stories for Arabic early-grade reading assessments requires balancing tight constraints on vocabulary, readi
arXiv:2605.01638v1 Announce Type: new Abstract: Multimodal deepfakes are proliferating on social media and threaten authenticity, information integrity, and digital forensics. Existing benchmarks are
arXiv:2603.29258v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have demonstrated strong capabilities across a wide range of multimodal tasks. However, recent studies have shown that
arXiv:2511.00510v2 Announce Type: replace Abstract: To address panoramic distortion, large search space, and identity ambiguity under a 360{eg} FoV, OmniTrack++ adopts a feedback-driven framework that
arXiv:2506.11499v2 Announce Type: replace Abstract: Multimodal chatbots have become one of the major topics for dialogue systems in both research community and industry. Recently, researchers have she
arXiv:2605.02141v1 Announce Type: new Abstract: Kullback-Leibler (KL) regularization is widely used in offline decision-making and offers several benefits, motivating recent work on the sample complex
arXiv:2510.02898v5 Announce Type: replace Abstract: Zero-shot captioners are recently proposed models that utilize common-space vision-language representations to caption images without relying on pai
arXiv:2605.02537v1 Announce Type: new Abstract: Autonomous 3D indoor scene synthesis breaks down in non-convex rooms with tightly coupled spatial constraints. Data-driven generators lack topological p
arXiv:2605.01358v1 Announce Type: new Abstract: Unsupervised Environment Design (UED) offers a promising paradigm for improving reinforcement learning generalization by adaptively shaping training env
arXiv:2605.02069v1 Announce Type: new Abstract: Many scoring applications require absolute predictions, while pairwise comparisons can provide a simpler learning objective. We present Pair2Score, a tw
arXiv:2605.02143v1 Announce Type: new Abstract: Personalized federated learning (pFL) aims to adapt models to client specific data distributions, yet it often fails to reliably preserve personalized i
arXiv:2603.26816v2 Announce Type: replace Abstract: High-dimensional low-sample-size (HDLSS) datasets constrain reliable environmental model development, where labeled data remain sparse. Reinforcemen
arXiv:2605.00942v1 Announce Type: cross Abstract: Developing effective test cases capable of thoroughly exercising large-scale software systems is inherently difficult, especially if such systems have
arXiv:2605.02358v1 Announce Type: new Abstract: Current models for predicting social media virality rely heavily on static textual and structural features, effectively ignoring the highly dynamic natu
Proud of my analyst, Hermes This is what he has achieved after 1.5+ months of internship with me - Got me off X (doomscrolling cut from 2 hrs → 30 mins) - Practically paid for his salary via PMs b
arXiv:2605.02552v1 Announce Type: new Abstract: Chemotherapy dose optimization can be formulated as a dynamic treatment regime, requiring sequential decisions under uncertainty that must balance tumor
arXiv:2605.02469v1 Announce Type: new Abstract: Online reinforcement learning with verifiable rewards (RLVR) turns checkable outcomes into a scalable training signal, but it keeps rollout generation,
arXiv:2511.17340v3 Announce Type: replace Abstract: Generative image models can produce convincingly real images, with plausible shapes, textures, layouts and lighting. However, one domain in which th
arXiv:2605.02589v1 Announce Type: new Abstract: Optical Coherence Tomography (OCT) has become one of the most used imaging modality in ophthalmology. It provides high-resolution, non-invasive visualiz
arXiv:2605.00915v1 Announce Type: new Abstract: Standard representation probing for visual models relies on mathematically permutation-invariant operations like Global Average Pooling (GAP) or CLS tok
arXiv:2605.00966v1 Announce Type: new Abstract: Hierarchical Gaussian Filtering (HGF) networks allow for efficient updating of posterior distributions (beliefs) about hidden states of an agent's envir
arXiv:2605.01429v1 Announce Type: cross Abstract: Libraries of Low-Rank Adaptation (LoRA) adapters are becoming a practical by-product of parameter-efficient adaptation. Once such adapters accumulate,
See you all soon! We've got some fun announcements ahead. I'll also be doing a workshop on 'how we Claude Code' with some workflows I'm excited to share. Don't worry if you're not there, everything wi
arXiv:2303.10971v2 Announce Type: replace Abstract: The matching of 3D shapes has been extensively studied for shapes represented as surface meshes, as well as for shapes represented as point clouds.
arXiv:2605.00515v1 Announce Type: cross Abstract: Leveraging continuous solar energy harvesting at high efficiency, space data centers are envisioned as a promising platform for executing energy-inten
arXiv:2604.07421v2 Announce Type: replace Abstract: Full-waveform inversion (FWI) is pivotal for reconstructing high-resolution subsurface velocity models but remains computationally intensive and ill
arXiv:2605.00835v1 Announce Type: new Abstract: Choosing between classical and Bayesian sparse regression methods involves a real trade-off: penalized estimators like Lasso run in milliseconds but giv
arXiv:2605.02152v1 Announce Type: new Abstract: Diffusion-based image editing offers strong semantic controllability, but remains computationally expensive due to iterative high-resolution denoising o
arXiv:2605.01466v1 Announce Type: new Abstract: Although multi-modal learning has advanced point cloud completion, the theoretical mechanisms remain unclear. Recent works attribute success to the conn
arXiv:2605.02586v1 Announce Type: new Abstract: Existing cross-subject fMRI decoding methods typically train a model on multiple scanned subjects and then adapt it to a new subject using substantial p
arXiv:2510.13668v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalan
arXiv:2605.02116v1 Announce Type: new Abstract: Contrastive representation learning (CRL) underpins many modern foundation models. Despite recent theoretical progress, existing analyses suffer from se
arXiv:2504.17401v2 Announce Type: replace Abstract: Stereo disparity estimation is crucial for obtaining depth information in robot-assisted minimally invasive surgery (RAMIS). While current deep lear
arXiv:2605.02568v1 Announce Type: new Abstract: DeepSeek-V3.2 and V4 introduce Compressed Sparse Attention (CSA): a lightning indexer (a learned scoring projection over compressed keys) scores them, t
arXiv:2605.02308v1 Announce Type: new Abstract: Against the backdrop of rapid advances in artificial intelligence, legal argument mining has emerged as an important research area linking legal texts w
arXiv:2605.01510v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success in high-quality image synthesis, sparking interest in image-guided generation tasks such as subject-dr