Knowledge catalogue
arXiv:2610.08829v1 Announce Type: new Abstract: Jev offers an alternative interface for language understanding: given an input and predefined questions, it returns probabilistic decisions rather than
Today, we announced the new Gemini agent, your single, universal agent for work. For small and midsize businesses, with lean teams and margins, Gemini will be a critical tool for you to operate effici
arXiv:2610.10297v1 Announce Type: cross Abstract: While energy efficiency is a critical objective for legged-robot locomotion control, achieving low energy consumption while maintaining robust perform
arXiv:2609.35308v2 Announce Type: replace Abstract: Large language models treat conversation history as unverified context, so false premises injected into prior turns can be adopted as fact, a failur
arXiv:2510.19773v2 Announce Type: replace Abstract: Membership inference attacks (MIAs) have emerged as the standard tool for evaluating the privacy risks of AI models. However, state-of-the-art attac
arXiv:2610.09427v1 Announce Type: new Abstract: World-Action Models (WAMs) utilize future visual prediction as an intermediate reasoning process to guide action generation. However, existing WAMs typi
arXiv:2610.09563v1 Announce Type: new Abstract: Effective traffic signal control (TSC) requires policies that respond to changing traffic demand and network conditions while meeting different control
arXiv:2610.10390v1 Announce Type: new Abstract: Vision-language-action~(VLA) models have emerged as a promising paradigm for autonomous driving. However, existing VLA models still suffer from a fundam
arXiv:2610.09288v1 Announce Type: cross Abstract: Many rare genetic diseases are associated with recognizable craniofacial features. However, traditional approaches for describing facial morphology re
arXiv:2610.10510v1 Announce Type: new Abstract: Tactile sim-to-real learning must bridge simulated contact and device-specific sensor responses while preserving information needed for control. We prop
arXiv:2610.09091v1 Announce Type: new Abstract: Decision-focused learning (DFL) trains predictive models for downstream optimization, but existing methods largely assume centralized data. In cross-sil
arXiv:2610.09202v1 Announce Type: cross Abstract: Extreme low-bit LLM compression is most challenging when weights, activations, and KV caches are quantized together: their distributions differ, and q
arXiv:2610.09768v1 Announce Type: new Abstract: Mean field limits describe the training dynamics of wide neural networks through the evolution of the empirical distribution of their parameters. Althou
arXiv:2610.09844v1 Announce Type: cross Abstract: The adoption of machine learning for socially relevant tasks requires effective explainable artificial intelligence (XAI) methods to better understand
arXiv:2610.10322v1 Announce Type: new Abstract: Recent methods have made promising progress in generating interactions between two humanoids, largely relying on physics-based tracking policies to conv
arXiv:2610.09449v1 Announce Type: new Abstract: Zero-shot Chinese character recognition (ZS-CCR) aims to recognize characters whose categories are never observed during training, and typically relies
arXiv:2610.09684v1 Announce Type: new Abstract: Test-time scaling (TTS) improves the reasoning capabilities of large language models by allocating additional inference computation. Existing approaches
arXiv:2610.10227v1 Announce Type: cross Abstract: While Large Language Models (LLMs) possess rich world knowledge and impressive generalization capabilities, their direct application to tabular data c
arXiv:2610.10396v1 Announce Type: new Abstract: This paper proposes a novel crowd counting approach, the Gaussian Density Splatting Network (GDSNet). Unlike methods that rely on conventional, grid-bas
arXiv:2610.08866v1 Announce Type: cross Abstract: Sparse-view computed tomography (CT) reduces radiation dose and acquisition time and may mitigate motion artifacts. However, angular undersampling pro
arXiv:2610.10238v1 Announce Type: new Abstract: Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation. However, these models still struggle to interpr
arXiv:2610.09456v1 Announce Type: new Abstract: Reconstructing fine-resolution column-averaged dry-air CO2 (XCO2) fields from sparse satellite observations requires models to infer spatial structure t
arXiv:2610.10524v1 Announce Type: new Abstract: Highly compressed video autoencoders offer an effective way to accelerate video diffusion models, as the Diffusion Transformer (DiT) operates on far few
arXiv:2610.10423v1 Announce Type: new Abstract: Adversarial example detectors are often tied to the classifier backbone they were trained on, limiting reuse when the protected model is replaced or upg
arXiv:2610.09068v1 Announce Type: new Abstract: Reliable robotic disassembly requires part-level representations that distinguish genuine component geometry from scanning and reconstruction artifacts.
arXiv:2610.10133v1 Announce Type: new Abstract: Real-world low-quality images suffer from complex mixed degradations, including but not limited to noise, blur, atmospheric effects, etc. Recent agentic
arXiv:2610.10156v1 Announce Type: new Abstract: Recent advances in Vision-Language Models (VLMs) have led to rapid progress in video understanding across a wide range of benchmark tasks. However, exis
arXiv:2606.13994v2 Announce Type: replace-cross Abstract: LLM-based Agents are becoming increasingly capable and widely deployed, creating growing incentives for adversarial misuse in the real-world.
arXiv:2610.10203v1 Announce Type: new Abstract: Model organisms of alignment-relevant behaviors (e.g., backdoors, sycophancy, spurious correlations) have emerged as a key tool for evaluating whitebox
arXiv:2610.08970v1 Announce Type: cross Abstract: Humanoid loco-manipulation of large, heavy objects demands forceful interaction across the entire body. However, such payloads shift a humanoid's cent
arXiv:2610.10145v1 Announce Type: new Abstract: We present InterView-C, a German multimodal corpus of 27 survey interviews conducted entirely in virtual reality, with both interlocutors represented by
arXiv:2610.09398v1 Announce Type: new Abstract: In tropical regions, their agricultural sectors remain highly dependent on manual labor for fruit harvesting. On large-scale farms, this dependency ofte
arXiv:2610.09788v1 Announce Type: new Abstract: Reward modeling often requires jointly representing and reasoning over multiple evaluation criteria, yet verbalizing this process token by token can inc
arXiv:2610.09940v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) predict masked or future observations in representation space, offering a natural source of predictiv
arXiv:2610.08818v1 Announce Type: cross Abstract: Spatio-temporal forecasting is a cornerstone of logistics, urban planning, and intelligent transportation systems. However, constrained by deployment
arXiv:2610.09534v1 Announce Type: new Abstract: Rotational symmetry is an important prior in 6D pose estimation, improving pose accuracy and supporting symmetry-aware evaluation. However, current symm
arXiv:2610.10366v1 Announce Type: new Abstract: Feature caching accelerates diffusion sampling by replacing expensive network evaluations with predictions from previously computed activations. However
arXiv:2610.08811v1 Announce Type: new Abstract: As context windows scale to tens or hundreds of thousands of tokens, KV cache compression has become essential for efficient LLM inference. Existing met
arXiv:2610.09877v1 Announce Type: new Abstract: Mixed-precision post-training quantization is a network compression method that assigns bits layer by layer, under a global memory budget using a small
arXiv:2610.09590v1 Announce Type: cross Abstract: Long-running autonomous agents must reuse accumulated reasoning experience without allowing explicit historical memory and LLM context to grow indefin
arXiv:2610.09444v1 Announce Type: new Abstract: Forecasting future 3D occupancy from surround-view cameras is essential for autonomous driving, yet existing approaches rely on dense voxel or bird's-ey
arXiv:2610.10465v1 Announce Type: new Abstract: Real-time whole-body controllers for legged robots typically plan through a fixed nominal model and degrade when the deployed dynamics change. Adaptive
arXiv:2610.10232v1 Announce Type: new Abstract: Large language models (LLMs) have already been shown to match or exceed human experts in persuasion. While their persuasive capabilities hold promise fo
arXiv:2610.10528v1 Announce Type: cross Abstract: Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Long
arXiv:2610.10266v1 Announce Type: new Abstract: Dense self-expression matrices and full-affinity spectral clustering limit the scalability of subspace clustering. We introduce the Latent Orthogonal Op
arXiv:2601.01779v2 Announce Type: replace-cross Abstract: Based on a transformer based sequence-to-sequence architecture combined with a dynamic batching algorithm, this work introduces a machine lear
arXiv:2508.10838v3 Announce Type: replace Abstract: Learning disparity in occluded regions remains difficult for unsupervised stereo matching. Photometric supervision lacks valid target-view correspon
arXiv:2605.24928v2 Announce Type: replace Abstract: Sonar imaging is the primary modality for underwater target detection, yet small targets remain difficult to detect due to insufficient pixel covera
arXiv:2610.09092v1 Announce Type: new Abstract: State Space Models (SSMs) offer an efficient alternative to Transformers for sequence modeling, yet conditioning pre-trained SSMs for iterative generati
arXiv:2610.09985v1 Announce Type: cross Abstract: We study a sequential pricing problem in which a seller jointly posts a price and an advertisement generated by a large language model (LLM). The sell
arXiv:2610.10225v1 Announce Type: new Abstract: Whole slide image (WSI) analysis in computational pathology follows a multiple instance learning (MIL) pipeline where patch embeddings are extracted ind
arXiv:2610.09055v1 Announce Type: new Abstract: Human videos provide rich motion targets for humanoid learning, yet visually plausible references can still produce persistent failures under physics-ba
arXiv:2610.09795v1 Announce Type: new Abstract: The demand for personalized LLMs is shifting from style imitation toward content quality. We investigate whether self-distillation can bridge this gap i
arXiv:2610.08830v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities across vision and language tasks. However, their massive co
arXiv:2610.09473v1 Announce Type: new Abstract: Time-series anomaly detection (TSAD) identifies deviations from patterns learned from historical data. In non-stationary settings, distribution drift an
arXiv:2610.10457v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) achieve remarkable performance in video synthesis, but their iterative denoising process suffers from high inference laten
arXiv:2610.10245v1 Announce Type: new Abstract: Despite the ubiquity of sensors in wearable and mobile devices and the abundance of human movement data they generate, translating unlabeled recordings
arXiv:2610.09306v1 Announce Type: new Abstract: Naturalistic movie-fMRI provides a shared, temporally structured probe of brain dynamics, yet predictive models commonly rely on whole-run functional co
arXiv:2610.09355v1 Announce Type: cross Abstract: Learning EEG representations that generalize across cognitive tasks, subjects, and recording conditions remains a key challenge in electroencephalogra
arXiv:2610.09520v1 Announce Type: new Abstract: Large vision-language models (VLMs) provide powerful open-world perception and reasoning for autonomous driving, but their high computational cost and i