Knowledge catalogue
arXiv:2603.15814v2 Announce Type: replace Abstract: Longitudinal mammography screening has become an important source of information for improving future breast cancer risk prediction. However, the pe
arXiv:2412.04236v2 Announce Type: replace-cross Abstract: Data-driven approaches to philosophy have emerged as a valuable tool for studying the history of the discipline. However, most studies in this
arXiv:2609.10954v1 Announce Type: new Abstract: Continual world models must decide whether new data justify changing the model. Fixed replay schedules and prediction-error triggers specify when to upd
arXiv:2609.11041v1 Announce Type: new Abstract: No Free Lunch theorem implies that any performance gains achieved by a classifier on a particular image distribution are necessarily offset by a loss of
arXiv:2609.10749v1 Announce Type: new Abstract: Accurate plant growth estimation is essential for greenhouse monitoring, yet obtaining labeled data remains costly and time-consuming. To address this,
arXiv:2609.10743v1 Announce Type: new Abstract: Monocular 3D hand and body mesh recovery often suffers from severe occlusion and ambiguity. Traditional deterministic methods typically regress a single
arXiv:2609.11317v1 Announce Type: new Abstract: Iterative reference-conditioned image editing can introduce grid-like and granular textures, commonly described as digital ripple. We present Mi-Ripple,
arXiv:2508.05830v3 Announce Type: replace Abstract: Large Language Model (LLM) studies that use language responses elicited from depression assessments to predict scores on those same assessments ofte
arXiv:2609.11842v1 Announce Type: new Abstract: Diffusion and flow-matching schedules control the signal and noise coefficients that mix data and noise along affine probability paths. Minimizing a kin
arXiv:2609.11338v1 Announce Type: new Abstract: Closed kinematic chains complicate modular modeling by coupling active and passive coordinates through nonlinear closure constraints. This paper present
arXiv:2609.10664v1 Announce Type: cross Abstract: Language models leave a distinctive vocabulary in the prose they help write, and we measure how much of the astronomy literature now carries it. From
arXiv:2609.10545v1 Announce Type: cross Abstract: Federated Learning (FL) enables distributed model training without centralizing data but suffers from high communication overhead. To address this, we
arXiv:2609.11322v1 Announce Type: new Abstract: Computational recognition of verbal humour remains a challenging task, requiring an understanding of language, delivery style, emotions, and cultural co
arXiv:2609.11655v1 Announce Type: new Abstract: Muon has emerged as a highly effective optimizer for large language model training, often achieving superior convergence and performance compared with t
arXiv:2408.10077v4 Announce Type: replace-cross Abstract: We study the welfare-maximizing allocation of heterogeneous objects when screening uses costly effort rather than monetary transfers. No-scree
arXiv:2609.11582v1 Announce Type: new Abstract: As Omni-modal large language models (Omni-LLMs) take in audio, video and text together, their KV cache memory cost grows. KV cache quantization is the d
arXiv:2609.11335v1 Announce Type: new Abstract: As large language models are increasingly deployed in sensitive domains, anonymizing input data to protect personally identifiable information has becom
arXiv:2609.10610v1 Announce Type: cross Abstract: Context: Computational notebooks are the standard environment for machine learning (ML) development. Within the ML community, model performance is oft
arXiv:2601.02031v3 Announce Type: replace-cross Abstract: Pretraining of large language models is not only expensive but also prone to certain training instabilities. A specific instability that often
arXiv:2609.10811v1 Announce Type: new Abstract: We present 'overpainting', an image editing operation which offers both control over the location of the edit and awareness of the previous content in t
arXiv:2609.09789v1 Announce Type: cross Abstract: Organizational design in the era of artificial intelligence requires experimental methods that can test how human-AI groups coordinate, delegate, and
arXiv:2602.11498v2 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) have shown promising potential to generate high-scoring candidates with probability proportional to their rewar
arXiv:2609.11538v1 Announce Type: new Abstract: Generative Marginalization Models (MaMs) have been recently introduced as efficient neural sampling models for any-order autoregressive modelling of dis
arXiv:2503.17970v2 Announce Type: replace-cross Abstract: Breast cancer survival prediction in computational pathology presents a remarkable challenge due to tumor heterogeneity. For instance, differe
arXiv:2508.02604v4 Announce Type: replace Abstract: Robotic cutting is a challenging, contact-rich manipulation task where the robot must simultaneously negotiate unknown object mechanics, large conta
arXiv:2603.23821v2 Announce Type: replace Abstract: Linguistic representation learning in deep neural language models (LMs) has been studied for decades, but finding representations in LMs remains an
arXiv:2510.25566v2 Announce Type: replace-cross Abstract: We present PitchFlower, a flow-based neural audio codec with explicit pitch controllability. Our approach promotes pitch disentanglement throu
arXiv:2609.10905v1 Announce Type: new Abstract: Planning trajectories for robot manipulators under kinematic equality constraints restricts feasible motions to a measure-zero submanifold of the config
arXiv:2609.11227v1 Announce Type: new Abstract: We study the expressivity of spiking neural networks, which provide a natural framework for asynchronous, event-driven computation complementary to conv
arXiv:2609.11780v1 Announce Type: new Abstract: Membership inference attacks (MIAs) are widely used to audit the privacy disclosure risk of machine learning models, however current state-of-the-art at
arXiv:2609.11277v1 Announce Type: cross Abstract: Reliable railway operations depend increasingly on real-time environmental intelligence delivered through wireless sensor infrastructures, a capabilit
arXiv:2609.11330v1 Announce Type: new Abstract: Optical coherence tomography is a promising modality for markerless motion tracking due to its high spatial resolution and inherent depth perception. Ho
arXiv:2609.11449v1 Announce Type: new Abstract: Many ML datasets are constructed by running a detector, heuristic, or model over candidate pools; accepted items become labels. Dataset precision is the
arXiv:2609.10826v1 Announce Type: new Abstract: This study proposes an integrated framework for the processing and classification of invasive bird species vocalizations within natural soundscapes, cha
arXiv:2609.11514v1 Announce Type: new Abstract: Estimating reliable cross-modality association is crucial to unsupervised visible-infrared person re-ID. While optimal transport is shown to be a practi
Evaluating video captioning remains a critical challenge for Visual Large Language Models (VLLMs). Existing metrics primarily rely on matching generated text against ground-truth references. This para
arXiv:2609.10657v1 Announce Type: cross Abstract: Neural networks trained past memorization frequently undergo a delayed transition to generalization, a phenomenon known as grokking. Despite theoretic
arXiv:2503.13647v2 Announce Type: replace-cross Abstract: In this work, a novel algorithm structured on Lie algebras for the approximate quantum state preparation problem is proposed, addressing a cha
arXiv:2609.11579v1 Announce Type: new Abstract: Underactuated robotic hands achieve adaptive and robust grasping with a reduced number of actuators, but predicting the stable equilibrium pose of the g
arXiv:2609.11775v1 Announce Type: new Abstract: Dexterous in-hand manipulation of a grasped object with an anthropomorphic hand is an unsolved frontier for robot dexterity. The contact-richness and hi
arXiv:2609.11029v1 Announce Type: new Abstract: Large language models are typically trained under uniform token weighting, which allows frequent and low-information tokens to dominate learning and can
arXiv:2609.11460v1 Announce Type: new Abstract: Reviewer comments naturally relate to specific parts of the reviewed paper, yet grounding these comments to the underlying evidence is difficult due to
arXiv:2609.10886v1 Announce Type: new Abstract: We continue the study of relatively smart learning, introduced by Dughmi and Pour (2026), which asks a supervised learner to compete, marginal by margin
arXiv:2609.10996v1 Announce Type: new Abstract: Verbalized confidence, long dismissed as overconfident, coarse, and prone to round-number clustering, is now the more robust soft-scoring mechanism for
arXiv:2609.11209v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves knowledge-intensive large language model (LLM) applications by conditioning generation on retrieved docu
arXiv:2609.11722v1 Announce Type: new Abstract: The representation of 3D clothed humans as standardized 2D UV texture and displacement maps over an underlying body model has long been studied. This co
arXiv:2609.11079v1 Announce Type: new Abstract: Render--match--PnP relocalization establishes correspondences between query image pixels and 3D map points for camera pose recovery, but their potential
arXiv:2609.11131v1 Announce Type: new Abstract: Human simultaneous interpreting (SI) is commonly assessed with analytic rubrics separating meaning transfer, delivery quality, and temporal synchrony, y
Sakana AI has released an updated version of its “Fugu” platform that exceeds performance metrics in some areas compared to the GPT‑6 Astra and Fable 5.1 models. The update does not integrate these tw
arXiv:2609.11279v1 Announce Type: new Abstract: With the rising demand to decouple objects from 3D scenes, we propose SAMV-DUSt3R, an end-to-end model that injects SAM2 2D masks into MV-DUSt3R reconst
arXiv:2609.10825v1 Announce Type: cross Abstract: Detecting brain metastases in magnetic resonance imaging (MRI) remains challenging because lesions vary widely in size and appearance, with very small
arXiv:2609.10831v1 Announce Type: cross Abstract: Longitudinal single cell atlases now capture matched pre treatment and post treatment states from responders and non responders, presenting an opportu
arXiv:2609.09882v1 Announce Type: new Abstract: Language models fine-tuned on customer behavior can predict outcomes and generate explanations, but these readouts are often treated as interchangeable.
arXiv:2609.10914v1 Announce Type: cross Abstract: Label-free virtual staining offers a compelling, non-destructive alternative to standard histopathology; however, its clinical adoption is hindered by
arXiv:2412.19990v3 Announce Type: replace-cross Abstract: Hepatic vessels in computed tomography scans often suffer from image fragmentation and noise interference, making it difficult to maintain ves
arXiv:2609.11168v1 Announce Type: new Abstract: Tensor singular value decomposition (T-SVD), which is built upon the tensor-tensor product (t-product), has emerged as a powerful tool for processing hi
arXiv:2601.12502v2 Announce Type: replace Abstract: The problem of reconstructing a quantum channel from a sample of classical data is considered. When the total fidelity can be represented as a ratio
arXiv:2609.09627v1 Announce Type: new Abstract: Capabilities relevant to Physical AI can arise from materially different formation histories, yet existing taxonomies organized by morphology, architect
arXiv:2609.09864v1 Announce Type: new Abstract: Affective computing has largely followed an individual-state paradigm, extracting discrete emotion labels or arousal/valence from isolated speakers. We
arXiv:2609.11680v1 Announce Type: new Abstract: 3D skeleton-based gait emotion recognition faces high annotation costs, data scarcity, and poor generalization on heterogeneous data. This paper propose