Knowledge catalogue
arXiv:2609.35579v1 Announce Type: new Abstract: Residual stream pruning methods reduce inference cost by shrinking the model's hidden dimension, but existing approaches typically choose these dimensio
arXiv:2609.32832v1 Announce Type: new Abstract: Over-the-air computation has emerged as a scalable and efficient solution for deploying federated learning algorithms in wireless networks by exploiting
arXiv:2609.32270v1 Announce Type: cross Abstract: We study the generalization of spectral gradient descent (SpecGD) in overparameterized matrix classification with corrupted labels. Each input combine
arXiv:2603.21877v4 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) enhances Large Language Model (LLM) reasoning but is suffer from advantage collapse: when all
arXiv:2609.34391v1 Announce Type: cross Abstract: AIVC (AI Virtual Cell) is a learned simulator of cellular behavior across conditions. Predicting how a cell population responds transcriptionally to a
arXiv:2609.34867v1 Announce Type: new Abstract: Vision language models have achieved strong performance across a wide range of multimodal applications, yet their substantial computational and memory c
arXiv:2609.34487v1 Announce Type: new Abstract: Radiology report generation (RRG) increasingly incorporates heterogeneous clinical evidence, such as multi-view radiographs and previous reports, whose
arXiv:2512.18291v3 Announce Type: replace Abstract: Object detection in aerial imagery is challenging because targets are often small, backgrounds are cluttered, and imaging conditions vary substantia
arXiv:2609.31651v1 Announce Type: new Abstract: Historical manuscripts remain largely absent from modern vision-language benchmarks, leaving open how well multimodal large language models (MLLMs) hand
arXiv:2609.31717v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies have shown promising performance in language-conditioned robotic manipulation. However, most existing VLA systems
arXiv:2609.31780v1 Announce Type: new Abstract: Modern text-to-image models produce high-fidelity images but still struggle with compositional prompts that require instance identity, attribute ownersh
arXiv:2609.34759v1 Announce Type: new Abstract: Recent vision-language models (VLMs) have advanced vision-and-language navigation (VLN), enabling models to predict navigation actions from visual obser
arXiv:2609.31716v1 Announce Type: new Abstract: Persistent semantic occupancy mapping is essential for embodied scene understanding. However, perspective-based systems provide limited spatial coverage
arXiv:2609.33077v1 Announce Type: new Abstract: Three-dimensional dense convolutional networks are the strongest performers on volumetric medical image segmentation, but their parameter counts scale p
arXiv:2609.33236v1 Announce Type: new Abstract: Training-free online video anomaly detection (VAD) with frozen multimodal language models faces two coupled challenges: extracting reliable current-wind
arXiv:2509.23235v2 Announce Type: replace Abstract: Model inversion is a widely adopted technique in data-free learning that reconstructs synthetic inputs from a pretrained model through iterative opt
arXiv:2609.31803v1 Announce Type: new Abstract: Autonomous navigation in dynamic environments is a critical challenge, particularly when spaces are shared with other mobile agents whose future traject
arXiv:2603.15688v2 Announce Type: replace-cross Abstract: Background: Lung-sound classifiers are usually evaluated on recording- or event-level splits, although each child contributes many recordings.
arXiv:2601.22132v2 Announce Type: replace Abstract: Large Language Models (LLMs) deliver state-of-the-art performance on complex reasoning tasks, but their inference costs limit deployment at scale. S
arXiv:2609.35768v1 Announce Type: new Abstract: Modern video diffusion models require tens of denoising evaluations over long spatiotemporal token sequences. Distribution Matching Distillation (DMD) r
arXiv:2609.31742v1 Announce Type: new Abstract: Normal-inverse-gamma (NIG) regression is not identifiable from its marginal Student-t likelihood: three combinations of four NIG parameters are determin
arXiv:2609.35054v1 Announce Type: cross Abstract: Contemporary deep speech enhancement (SE) models are often trained with specific auxiliary terms in the loss function as a way to improve their perfor
arXiv:2609.32094v1 Announce Type: cross Abstract: Temporal dynamics in learning behavior can be revealed through period segmentation in Transition Network Analysis (TNA). Cristea et al. demonstrated t
arXiv:2609.32833v1 Announce Type: new Abstract: A trained neural network can be represented by a parameter vector in high dimensions. Learning distributions over these vectors enables the generation o
arXiv:2609.35402v1 Announce Type: new Abstract: When pricing agents meet repeatedly on a platform, the platform decides who faces whom. We ask whether that choice moves the prices the agents learn, an
arXiv:2609.33171v1 Announce Type: cross Abstract: Latent diffusion models serve as powerful priors for solving inverse problems in image restoration, such as deblurring, inpainting, and super-resoluti
arXiv:2601.04692v2 Announce Type: replace-cross Abstract: In this work, we examine hateful memes from three complementary angles - how to detect them, how to explain their content and how to intervene
arXiv:2609.32634v1 Announce Type: new Abstract: Reinforcement Fine-Tuning~(RFT) has emerged as a promising paradigm for improving Vision-Language-Action~(VLA) policies, yet sparse task-level outcomes
arXiv:2609.33558v1 Announce Type: new Abstract: 3D Visual Query Localization (3DVQL) retrieves the latest contiguous occurrence of a queried object in an RGB--point-cloud sequence and predicts a 9-DoF
arXiv:2609.32319v1 Announce Type: new Abstract: We circumvent the Sanford-Hsu-Telgarsky (SHT) single-layer induction obstruction within a linear, one-step, causal, bilinear, symplectically consistent
arXiv:2609.32928v1 Announce Type: new Abstract: Millimeter-wave (mmWave) radar and RGB-D cameras can record cardiac and respiratory waveforms continuously and without contact, but labeled recordings r
arXiv:2609.31855v1 Announce Type: new Abstract: Human demonstrations provide dense policy-level information but sometimes lack local precision. Human feedback presents accurate local critiques, but of
arXiv:2609.34804v1 Announce Type: cross Abstract: Federated learning enables industrial operators to train shared intrusion detection models without disclosing proprietary operational telemetry. Howev
arXiv:2609.35499v1 Announce Type: new Abstract: As the world moves towards sustainable energy sources, hydrogen (H2) can be treated as an eco-friendly alternative to fossil fuels due to its high energ
arXiv:2609.34795v1 Announce Type: new Abstract: Underwater image enhancement is crucial for improving visual perception in marine applications. Existing underwater image enhancement studies mainly foc
arXiv:2609.34916v1 Announce Type: cross Abstract: Accurate prediction of avalanche motion is essential for hazard assessment in mountainous terrain. This study develops and evaluates a physics-informe
arXiv:2603.02231v2 Announce Type: replace Abstract: Large-scale wave field reconstruction requires precise solutions but faces challenges with computational efficiency and accuracy. The physics-based
arXiv:2609.34466v1 Announce Type: new Abstract: Physiological foundation models encode how a signal was recorded alongside the physiology it reflects. When recording conditions are associated with dia
arXiv:2609.33857v1 Announce Type: new Abstract: Recovering hidden transport mechanisms from sparse spatiotemporal observations is a fundamental inverse problem in scientific machine learning. In brain
arXiv:2609.33318v1 Announce Type: new Abstract: Underwater image enhancement (UIE) aims to restore visibility, color fidelity, and structural detail from images degraded by wavelength-dependent attenu
arXiv:2609.32685v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) provide a learning-based framework for solving partial differential equations (PDEs), yet their training beha
arXiv:2609.35303v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) via Group-Relative Policy Optimization (GRPO) is widely used for multi-turn VLM agent training, ye
arXiv:2609.32732v1 Announce Type: cross Abstract: Human mobility generation aims to synthesize realistic point-of-interest (POI) visitation trajectories and has become an important tool for travel beh
arXiv:2609.32801v1 Announce Type: cross Abstract: Embodied task planners may produce multi-step plans whose subtask dependencies and interactions with the environment create physical risks during exec
arXiv:2609.32146v1 Announce Type: new Abstract: A quadrilateral block decomposition of a planar domain is judged by whether it is complete, whether its elements are well shaped, and how many of its ve
arXiv:2609.34314v1 Announce Type: new Abstract: Video-language models are increasingly used as judges of video understanding, both for evaluating model outputs and for training reward models. Whether
arXiv:2609.34605v1 Announce Type: new Abstract: Multi-teacher on-policy distillation (MOPD) has emerged as a popular post-training paradigm for integrating specialized capabilities in frontier languag
arXiv:2609.33127v1 Announce Type: new Abstract: Offline-to-Online Reinforcement Learning (O2O RL) has emerged as a practical paradigm that pre-trains the policy using static offline datasets and subse
arXiv:2602.02422v2 Announce Type: replace Abstract: The self-attention mechanism, at the heart of the Transformer model, is able to effectively model pairwise interactions between tokens. However, num
arXiv:2609.34812v1 Announce Type: new Abstract: We study Nash regret minimization in unknown finite matrix games with bandit payoff feedback and observed opponent actions. We develop Optimistic Payoff
arXiv:2609.32465v1 Announce Type: new Abstract: Decision-focused learning (DFL) trains predictors for downstream decision quality, but often relies on optimal reference decisions that are expensive to
arXiv:2609.32856v1 Announce Type: new Abstract: Vector polygon generation converts visual inputs, e.g., remote sensing (RS) images, into vectorized polygonal geometries, supporting applications such a
arXiv:2609.33258v1 Announce Type: cross Abstract: Recent task-driven and just-in-time 3D Scene Graph (3DSG) methods reduce per-task representations by constructing or activating only task-relevant inf
arXiv:2609.34021v1 Announce Type: new Abstract: Test-Time Training (TTT) adapts models to incoming test samples (e.g. out-of-distribution, (OOD)) when conventional fine-tuning is infeasible. Existing
arXiv:2609.33759v1 Announce Type: cross Abstract: When a fact changes, how should a language model update the history stored in its key-value (KV) cache? Hiding the old record is cheap, but it may sti
arXiv:2609.34031v1 Announce Type: new Abstract: We present a three-tier progression of Hidden Markov Models: maximum-likelihood (Baum-Welch), variational Bayesian (VBEM), and a neural variational exte
arXiv:2609.33746v1 Announce Type: new Abstract: At each decoding step a language model attends over the key-value (KV) cache of every earlier token, so at long context the attention call is bounded by
arXiv:2609.32163v1 Announce Type: new Abstract: Temporal context is essential for camera-only multi-view 3D object detection. Existing streaming detectors maintain and propagate query states from one
arXiv:2609.34292v1 Announce Type: cross Abstract: We report a three-month autonomous research-agent program testing five solid-state-physics-inspired compression mappings on pretrained language models
arXiv:2609.34054v1 Announce Type: new Abstract: Multi-LoRA agent systems enable efficient role specialization by sharing a common backbone model. However, each agent repeatedly processes the growing s