Knowledge catalogue
arXiv:2606.22486v1 Announce Type: new Abstract: Medical expert annotators are scarce, and blind reliance on artificial intelligence (AI) can be misleading, motivating approaches in which humans, parti
'I do not believe that AI is likely to cure cancer anytime soon'—@2plus2makes5 https://www.theatlantic.com/technology/2026/06/ai-cancer-progress/687654/?gift=yDcMMOSj-3oJk8475KVVBne5CfZXWcmKJJrUAGArmL
arXiv:2606.22042v1 Announce Type: new Abstract: Diffusion-based video editing has made significant progress; however, achieving precise and temporally consistent object-level control, especially in mu
In software, complexity is a tax you pay on every future change. You might think, 'as long as it works, I'm good, I don't care about aesthetics', but elegant code is about maintainability, not aesthet
arXiv:2606.23198v1 Announce Type: cross Abstract: We study mirror flows generated by a convex quadratic loss and a general convex lower semicontinuous mirror potential. We show that, when initialized
arXiv:2606.20655v1 Announce Type: cross Abstract: Physics-informed and data-driven surrogates are increasingly used to approximate mechanics-governed flow fields, but the target quantities assigned to
arXiv:2606.16878v3 Announce Type: replace Abstract: Retail marketing measurement increasingly requires granular campaign-level insights without relying on user-level tracking. However, the two dominan
arXiv:2606.22905v1 Announce Type: new Abstract: Recent diffusion-based models have enabled realistic audio-driven avatar generation in real-time streaming. However, existing approaches struggle to mai
arXiv:2606.22353v1 Announce Type: new Abstract: Fidelity and perceptual quality are two inherently competing and conflicting objectives in the image super-resolution (SR) task. Different loss function
arXiv:2606.22473v1 Announce Type: cross Abstract: Speech language models (SLMs) have been extensively studied, with the common paradigm incorporating text data and pre-trained text LMs. A leading appr
arXiv:2606.23425v1 Announce Type: new Abstract: Accurate electricity load forecasting is a crucial prerequisite for stable power system operations. While prevalent deep learning models present competi
arXiv:2512.09021v3 Announce Type: replace-cross Abstract: Stellar and AGN-driven feedback processes affect the distribution of gas on a wide range of scales, from within galaxies well into the interga
arXiv:2606.22725v1 Announce Type: new Abstract: Facial expression recognition (FER) is inherently ambiguous: human annotators frequently disagree, and models deployed in real environments face distrib
arXiv:2606.21256v1 Announce Type: new Abstract: Quantum pure-state ensembles live on complex projective space, making flat Euclidean generative modeling geometrically mismatched. We introduce Intrinsi
arXiv:2412.00143v2 Announce Type: replace-cross Abstract: Oracle pruning, which selects unimportant weights by minimizing the pruned train loss, has served as the foundation for most neural network pr
arXiv:2511.14310v2 Announce Type: replace Abstract: Multi-source stationary computed tomography (CT) has recently attracted attention for its ability to achieve rapid image reconstruction, making it s
arXiv:2603.05538v3 Announce Type: replace Abstract: Data-driven surrogate models can significantly accelerate the simulation of continuous dynamical systems, yet the step-wise accumulation of errors d
arXiv:2511.18471v3 Announce Type: replace Abstract: Diffusion models provide powerful generative priors for solving inverse problems by sampling from a posterior distribution conditioned on corrupted
arXiv:2606.18853v2 Announce Type: replace-cross Abstract: A recent line of work has reframed individual decision trees as linear models on engineered features associated with their splits, opening rou
arXiv:2606.22369v1 Announce Type: cross Abstract: In this paper, we present Kiwano, an open-source toolkit designed to advance research and evaluation for speaker verification. Kiwano provides a light
arXiv:2606.19984v2 Announce Type: replace Abstract: Reservoir computing offers a lightweight framework for forecasting dynamical systems but may struggle to capture long-range dependencies due to limi
arXiv:2512.09678v2 Announce Type: replace-cross Abstract: In this article, we explore the use of various matrix norms for optimizing functions of weight matrices, a crucial problem in deep learning. M
arXiv:2509.06529v2 Announce Type: replace Abstract: In complex traffic scenarios, intention prediction of surrounding vehicles can improve the strategy of automated driving functions. Existing work on
arXiv:2604.05648v2 Announce Type: replace Abstract: We propose a method for the collective maneuvering of affine formations in the plane by modifying the original weights of the Laplacian matrix used
arXiv:2606.22801v1 Announce Type: new Abstract: Image restoration aims to recover high-quality images from degraded observations. Recent Mamba-based image restoration models have demonstrated strong p
arXiv:2606.20958v1 Announce Type: new Abstract: Modeling soft robot dynamics is challenging due to their continuum structure and typically nonlinear dynamics. Creating models based on first-order prin
arXiv:2502.15376v2 Announce Type: replace Abstract: Equivariant network architectures are a well-established tool for predicting invariant or equivariant quantities. However, almost all learning probl
arXiv:2606.22076v1 Announce Type: new Abstract: Single-reference unseen object 6D pose estimation reduces object onboarding by estimating poses of arbitrary novel objects from only one reference view.
arXiv:2606.22634v1 Announce Type: new Abstract: Learning Entropy (LE) has recently been extended to image analysis through Spatial Learning Entropy Maps (SLEMs), which are two-dimensional LE distribut
arXiv:2411.19468v4 Announce Type: replace Abstract: The random feature (RF) method is a powerful kernel approximation technique, but it typically uses fixed activation functions, limiting its adaptabi
arXiv:2606.22895v1 Announce Type: new Abstract: Graph theory is inherently descriptive, capturing what relationships exist but not why they arise, because it treats edges as primitive constructs. This
arXiv:2606.23027v1 Announce Type: new Abstract: Feed-forward Gaussian splatting (FFGS) facilitates real-time novel view synthesis, yet current methods often remain tied to view-dependent predictions.
arXiv:2506.01882v3 Announce Type: replace-cross Abstract: The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and othe
arXiv:2602.09402v2 Announce Type: replace Abstract: We study the problem of learning with multiple correct answers, where each instance admits a set of valid labels. We primarily focus on the online s
arXiv:2606.23414v1 Announce Type: new Abstract: In many online learning and bandit problems, the actions we consider possess inherent similarities--for instance because they share latent traits, tags,
arXiv:2606.21564v1 Announce Type: new Abstract: Transformers achieve strong performance, but their internal computations remain opaque. We view each Transformer layer as a dynamic graph whose nodes ar
arXiv:2606.22481v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) is widely used to capture and render real scenes. Compositing objects from one capture into another has applications in m
arXiv:2606.23539v1 Announce Type: new Abstract: Visual document retrieval requires rapidly locating relevant pages from large multi-modal corpora in response to user queries. While recent methods powe
arXiv:2606.21292v1 Announce Type: new Abstract: We present Casper3D, a lightweight probabilistic framework for converting noisy multi-view 2D foundation-model embeddings into a latent 3D semantic repr
arXiv:2606.23653v1 Announce Type: new Abstract: Accurate volume and surface area estimation is critical for diverse applications, from marine ecology to medical diagnostics. However, existing methods
arXiv:2606.21018v1 Announce Type: cross Abstract: As artificial intelligence advances into the era of Embodied AI, live musical interaction urgently needs to break free from the limitations of offline
arXiv:2606.22662v1 Announce Type: new Abstract: Forecasting chaotic dynamical systems such as the Lorenz attractor is notoriously difficult: small numerical errors are amplified exponentially over lon
arXiv:2304.12319v2 Announce Type: replace-cross Abstract: Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distort
arXiv:2606.20874v1 Announce Type: new Abstract: Cryopathy syndromes are difficult to classify because laboratory patterns often overlap across diagnostic categories, while some diagnoses are rare. Thi
arXiv:2606.21461v1 Announce Type: new Abstract: The Manipulider is a buoyancy-actuated underwater robot that enables thrusterless, glide-like locomotion and attitude-based manipulation, while providin
This post likely explores the concept that maps don't merely represent territory passively, but actively influence how we perceive, organize, and interact with physical space—a reversal of the traditi
Market efficiency is a myth. Markets are made of people, who are irrational both individually and in aggregate. Maybe if human irrationality were completely random, it might average out to a rational
arXiv:2606.22649v1 Announce Type: new Abstract: Foundation models provide highly descriptive representations for medical images, yet their reliability degrades under distribution shifts arising from c
arXiv:2606.20743v1 Announce Type: new Abstract: Trained transformers reliably develop massive activations, a small number of hidden dimensions whose magnitude is far above the median and which concent
arXiv:2606.23455v1 Announce Type: new Abstract: Recent advances integrate physically grounded Newtonian dynamics with neural rendering frameworks, narrowing the gap between photorealistic scene recons
arXiv:2606.21047v1 Announce Type: new Abstract: Acoustic microrobots have emerged as a promising frontier for targeted drug delivery and minimally invasive medicine due to their high-power density and
arXiv:2606.21540v1 Announce Type: new Abstract: Graph convolutional networks (GCNs) have demonstrated significant success in capturing complex user-item relationships for collaborative filtering (CF).
arXiv:2606.23489v1 Announce Type: cross Abstract: Meshes are among the most common 3D scene representations, but directly generating meshes is challenging because the representation contains important
When annotators disagree on a label, the disagreement itself carries signal—and the number of annotators needed to capture it depends on the evaluation metric. We fine-tune NLI models on label distrib
arXiv:2512.00324v3 Announce Type: replace-cross Abstract: Imitation learning provides a promising approach to dexterous hand manipulation, but its effectiveness is limited by the lack of large-scale,
arXiv:2601.07298v2 Announce Type: replace Abstract: While Multimodal Large Language Models (MLLMs) excel at single-image understanding, they exhibit significantly degraded performance in multi-image r
arXiv:2606.23096v1 Announce Type: new Abstract: Minimax risk and regret are expectation-based criteria and do not capture rare but consequential failures. To address this concern, we develop a elta-ex
arXiv:2508.13744v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) exhibit strong performance on single-image tasks. However, their performance degrades significantly when handli
arXiv:2501.03432v3 Announce Type: replace Abstract: The Large Hadron Collider at CERN produces immense volumes of complex data from high-energy particle collisions, demanding sophisticated analytical
arXiv:2606.20906v1 Announce Type: new Abstract: Molecular message-passing neural networks commonly propagate chemically diverse interactions through a single graph, which may mix interaction-specific