Knowledge catalogue
arXiv:2609.35405v1 Announce Type: new Abstract: Pretrained 2D foundation models offer a practical alternative to dedicated 3D pretraining for brain structural magnetic resonance imaging (sMRI), but th
arXiv:2609.31771v1 Announce Type: new Abstract: Many planning and control tasks for tendon-driven continuum robots (TDCRs) require the complete Cartesian backbone geometry. We present a reduced Cartes
arXiv:2609.32415v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have become the standard backbone for high-quality generative modeling, yet deploying them in conditional generation tasks
arXiv:2609.32389v1 Announce Type: new Abstract: Filmmakers and visual artists routinely need to compose multiple references, actors, locations, props, cultural elements, into a single coherent shot, b
arXiv:2609.34904v1 Announce Type: new Abstract: Graph neural networks (GNNs) need to exploit improved message passing without surrendering control over predictions already trusted in deployment. We in
arXiv:2609.32516v1 Announce Type: cross Abstract: Security Operations Centers (SOCs) process large volumes of alerts daily. Alert triage prioritizes high-risk threats while reducing manual review of b
arXiv:2609.35078v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models for autonomous driving rely heavily on successful expert demonstrations, leaving model-specific failures underexploi
arXiv:2609.32669v1 Announce Type: cross Abstract: Attention weights depend on token counts, which change with the representation of a signal. We study refinement symmetry: splitting a representation w
You may think that jev, OpenClaw or TurboQuant are super cool, but actually the coolest LLM invention happened two years ago As we all know, the best source of reliable information about LLMs is YouTu
arXiv:2609.32068v1 Announce Type: new Abstract: Motion retargeting transfers motion across characters with different skeletal structures while preserving semantic intent and physical plausibility. Des
arXiv:2609.32298v1 Announce Type: new Abstract: Drifting Models train a one-step generator by recomputing a finite-sample drift field at every iteration and taking an optimizer step toward the drifted
arXiv:2609.32888v1 Announce Type: new Abstract: Gradient-based data selection methods such as LESS score each candidate by the alignment between its gradient and a target validation gradient, and reco
arXiv:2609.34231v1 Announce Type: new Abstract: Metamaterials are artificially engineered structures whose mechanical and physical behaviors are strongly shaped by geometry rather than composition. Vo
arXiv:2609.32716v1 Announce Type: new Abstract: Superpixel copula models provide stable regional evidence for heterogeneous remote sensing change detection, but a single label per region limits locali
arXiv:2609.34587v1 Announce Type: new Abstract: Reinforcement learning is increasingly used to post-train vision-language models for image-to-code generation, such as generating SVG code from a refere
arXiv:2511.20237v2 Announce Type: replace-cross Abstract: The Newton-Raphson (NR) method is widely used for solving power flow (PF) equations due to its quadratic convergence. However, its performance
arXiv:2505.12027v2 Announce Type: replace Abstract: In recent years, large language models (LLMs) have demonstrated remarkable capability to generalize across diverse natural language processing tasks
arXiv:2609.31816v1 Announce Type: new Abstract: What should a compressed representation preserve when the information of interest lies in relationships among elements rather than in the elements thems
arXiv:2604.04410v2 Announce Type: replace Abstract: Aligning language models with human preferences is essential for ensuring their safety and reliability. Although most existing approaches assume spe
arXiv:2609.33016v1 Announce Type: new Abstract: Large Language Model (LLM) pretraining performance is jointly shaped by three components of the training triplet: the optimizer, model architecture, and
We have released Qwen3.8-Flash-Next quantized with GSQ and RCO, together with a second, capability-targeted build in which half of the model's experts have been removed. Flash-Next is a sparse mixture
arXiv:2609.33304v1 Announce Type: new Abstract: Personal Graphical User Interface (GUI) agents rely on interaction history to infer what a user wants from ambiguous instructions and to anticipate recu
arXiv:2609.34163v1 Announce Type: new Abstract: Vision-language navigation (VLN) is typically evaluated as a one-way task, although deployed robots may need to return after reaching a goal. We study c
arXiv:2609.35764v1 Announce Type: new Abstract: Sparse inertial pose estimation promises camera-free motion capture from consumer devices, but consumer sensors are unreliable: firmware-fused orientati
arXiv:2609.33725v1 Announce Type: cross Abstract: Continually adapting models to new tasks requires retaining earlier knowledge under limited memory and computation. Experience replay addresses this c
arXiv:2609.33344v1 Announce Type: new Abstract: Scene Coordinate Regression (SCR) has recently emerged as a promising approach for LiDAR-based localization, achieving accurate localization without req
arXiv:2609.32460v1 Announce Type: new Abstract: Recent video generation models produce realistic videos and show potential as a foundation for world models. These advances create opportunities for gen
arXiv:2609.35612v1 Announce Type: new Abstract: Remote sensing novel view synthesis under sparse observations remains challenging due to insufficient geometric constraints and limited cross-view super
arXiv:2609.31694v1 Announce Type: new Abstract: Removing an invisible watermark and concealing the forensic evidence are distinct objectives: successfully disrupting the embedded watermark does not im
arXiv:2609.35264v1 Announce Type: new Abstract: Camera-LiDAR detectors can continue to consume unreliable features even when both sensors remain present, synchronized, and calibrated. We introduce Bou
arXiv:2609.33217v1 Announce Type: new Abstract: Generative models learn visual structure through denoising, yet their internal states are entangled with both noise level and network depth, making it d
arXiv:2609.31630v1 Announce Type: new Abstract: Replay-based continual learning almost always consolidates in a dedicated offline phase or by interleaving replayed samples with the input stream, where
arXiv:2609.34177v1 Announce Type: cross Abstract: When an agent reuses logged experience, a changed decision may reflect the recorded score, the action's name, or the record's position in storage. Sta
arXiv:2606.16575v3 Announce Type: replace Abstract: Deep neural networks (DNNs) have achieved remarkable success in scientific computing, yet they often suffer from spectral bias in capturing oscillat
arXiv:2609.32263v1 Announce Type: new Abstract: Multimodal test-time adaptation (TTA) aims to adapt a pretrained multimodal model online to distribution shift across modalities using unlabeled test da
arXiv:2609.32018v1 Announce Type: new Abstract: Modern neural predictors can model highly nonlinear maps, but many scientific and engineering tasks require reasoning in the opposite direction: given a
arXiv:2609.34287v1 Announce Type: cross Abstract: LLM pretraining corpora are normally cleaned by a stack of hand-written heuristics. A heuristic scraper extracts the main content from HTML, and dozen
arXiv:2609.33749v1 Announce Type: new Abstract: In dyadic human speaker-listener conversations, the listener's facial reactions allows the speaker to accurately perceive the listener's emotional state
arXiv:2609.32129v1 Announce Type: new Abstract: Pretrained robot policies offer strong manipulation skills but are typically limited to single-agent settings, where a robot acts in isolation. In this
arXiv:2609.33020v1 Announce Type: new Abstract: Diffusion models achieve state-of-the-art results across multiple tasks. However, in inverse problems, standard initialization from pure Gaussian noise
arXiv:2609.33892v1 Announce Type: new Abstract: This paper proposes a residual learning-based control framework for heterogeneous vehicle platoons subject to parametric uncertainty and external distur
arXiv:2609.33895v1 Announce Type: new Abstract: In diffusion-based generation, a neural network can be trained to predict the clean data, the noise, or the velocity from a noisy input. These predictio
arXiv:2609.32241v1 Announce Type: cross Abstract: Neural image watermarks can be forged by extracting watermark-bearing residuals from released images and transferring them to unrelated content. While
arXiv:2609.34905v1 Announce Type: new Abstract: Power sampling has emerged as a training-free approach to LLM reasoning, eliciting capabilities comparable to reinforcement learning by sharpening the m
arXiv:2609.34942v1 Announce Type: new Abstract: The inference efficiency of Multimodal Large Language Models (MLLMs) is severely constrained by massive visual token sequences induced by high-resolutio
arXiv:2609.33412v1 Announce Type: new Abstract: Despite rapid progress in vision-language-action (VLA) models, existing reasoning paradigms still face a fundamental state-representation mismatch in op
arXiv:2609.34701v1 Announce Type: cross Abstract: Multi-agent systems (MAS) are increasingly used to automate enterprise workflows involving multiple specialized agents, external tools, and long-runni
arXiv:2609.31932v1 Announce Type: cross Abstract: Low-Earth-orbit (LEO) satellites are increasingly expected to perform onboard learning for applications such as disaster response and environmental mo
arXiv:2609.33687v1 Announce Type: new Abstract: Onboard satellite models often require frequent updates, but the weights adapted to earlier data distributions can quickly become outdated. However, upd
arXiv:2609.35433v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) frequently reuses rollouts across multiple policy updates, increasing the mismatch between the cur
arXiv:2609.35593v1 Announce Type: new Abstract: 3D vision transformers such as VGGT predict camera poses and scene geometry from multi-view images in a single forward pass, but their global attention
arXiv:2609.33944v1 Announce Type: new Abstract: Jointly generating future video and actions has become a standard recipe for world-action models, and the strongest systems denoise the two streams on s
arXiv:2609.35469v1 Announce Type: cross Abstract: Autoregressive Vision-Language-Action (VLA) models offer a scalable path to robot learning, yet existing action tokenizers treat tokenization as a com
arXiv:2609.35686v1 Announce Type: new Abstract: Mechanistic interpretability (MI) aims to explain a model's behaviour through analyzing its internal computations; circuit-based explanations aim to iso
arXiv:2609.33851v1 Announce Type: new Abstract: Contextualization, the core operation of language modeling, transmits information across words to build sentence-specific word representations. Prior wo
arXiv:2609.32187v1 Announce Type: new Abstract: Cross-channel modeling is central to multivariate time-series forecasting, yet channels that are statistically related, predictively useful, and actuall
arXiv:2609.34563v1 Announce Type: new Abstract: Latent visual reasoning (LVR) enables multimodal large language models (MLLMs) to perform intermediate computation in continuous latent tokens rather th
arXiv:2609.35695v1 Announce Type: new Abstract: Aligning large language models (LLMs) to diverse user preferences is fundamentally hindered by standard alignment paradigms that optimize for monolithic
arXiv:2609.32899v1 Announce Type: new Abstract: Hyperbolic space can embed intrinsic hierarchies in data with low distortion due to the exponential growth of volume with distance from the origin. Howe
arXiv:2609.32444v1 Announce Type: new Abstract: We study training-inference mismatch in reinforcement learning with verifiable rewards (RLVR) for large language models, where rollouts are sampled by a