Knowledge catalogue
arXiv:2604.10452v1 Announce Type: new Abstract: Olfaction lies at the intersection of chemical structure, neural encoding, and linguistic perception, yet existing representation methods fail to fully
arXiv:2604.11720v1 Announce Type: cross Abstract: The proliferation of autoregressive (AR) image generators demands reliable detection and attribution of their outputs to mitigate misinformation, and
arXiv:2604.09581v1 Announce Type: new Abstract: Evaluating web usability typically requires time-consuming user studies and expert reviews, which often limits iteration speed during product developmen
arXiv:2604.11004v1 Announce Type: cross Abstract: In this work, we introduce a new perspective on comparative image assessment by representing an image pair as a structured composition of its regions.
arXiv:2603.20725v2 Announce Type: replace Abstract: Text-to-image generation has advanced rapidly, yet it still struggles to capture the nuanced user preferences. Existing approaches typically rely on
arXiv:2507.17596v3 Announce Type: replace-cross Abstract: While end-to-end autonomous driving models show promising results, their practical deployment is often hindered by large model sizes, a relian
arXiv:2604.11178v1 Announce Type: cross Abstract: Forecasting neural activity in response to naturalistic stimuli remains a key challenge for understanding brain dynamics and enabling downstream neuro
arXiv:2604.10982v1 Announce Type: new Abstract: Open-vocabulary panoptic reconstruction is essential for advanced robotics perception and simulation. However, existing methods based on 3D Gaussian Spl
arXiv:2604.10960v1 Announce Type: new Abstract: Knowledge Tracing (KT) infers a student's knowledge state from past interactions to predict future performance. Conventional Deep Learning (DL)-based KT
arXiv:2604.11554v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training has proven effective at unlocking reasoning, self-reflection, and tool-use capabilities in large language mode
arXiv:2604.11156v1 Announce Type: new Abstract: Unsupervised remote photoplethysmography (rPPG) promises to leverage unlabeled video data, but its potential is hindered by a critical challenge: traini
arXiv:2510.06687v2 Announce Type: replace-cross Abstract: Semantic segmentation serves as a cornerstone of scene understanding in autonomous driving but continues to face significant challenges under
arXiv:2603.03197v3 Announce Type: replace Abstract: Classifying fine-grained visual concepts under open-world settings, i.e., without a predefined label set, demands models to be both accurate and spe
arXiv:2602.00793v2 Announce Type: replace-cross Abstract: Speaking aloud to a wearable AR assistant in public can be socially awkward, and re-articulating the same requests every day creates unnecessa
arXiv:2604.10371v1 Announce Type: new Abstract: This study proposes Structural Gating and Effect-aligned Discovery for Temporal Causal Discovery (SGED-TCD), a novel and general framework for lag-resol
arXiv:2604.09781v1 Announce Type: new Abstract: Vision-Language Models (VLMs) exhibit strong visual reasoning capabilities, yet they still struggle with 3D understanding. In particular, VLMs often fai
arXiv:2604.11679v1 Announce Type: new Abstract: Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are
arXiv:2604.11077v1 Announce Type: new Abstract: Customer service chatbots are increasingly expected to serve not merely as reactive support tools for users, but as strategic interfaces for harvesting
arXiv:2602.21484v2 Announce Type: replace Abstract: 3D object detection is essential for autonomous driving and robotic perception, yet its reliance on large-scale manually annotated data limits scala
arXiv:2604.11250v1 Announce Type: new Abstract: Face recognition embeddings encode identity, but they also encode other factors such as gender and ethnicity. Depending on how these factors are used by
arXiv:2505.09591v3 Announce Type: replace-cross Abstract: Despite remarkable progress in recent years, Vision Language Models (VLMs) remain prone to overconfidence and hallucinations on tasks such as
arXiv:2604.02467v2 Announce Type: replace-cross Abstract: Cinematic camera control relies on a tight feedback loop between director and cinematographer, where camera motion and framing are continuousl
arXiv:2604.10542v1 Announce Type: cross Abstract: Video-to-Audio (V2A) generation is essential for immersive multimedia experiences, yet its evaluation remains underexplored. Existing benchmarks typic
arXiv:2604.10167v1 Announce Type: cross Abstract: Multi-vector models dominate Visual Document Retrieval (VDR) due to their fine-grained matching capabilities, but their high storage and computational
arXiv:2604.10809v1 Announce Type: new Abstract: Recent advancements in learning from human demonstration have shown promising results in addressing the scalability and high cost of data collection req
arXiv:2604.09649v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) have opened new platforms for human-computer interaction, medical diagnostics, and neurorehabilitation. Wearable BCI
arXiv:2602.13934v4 Announce Type: replace-cross Abstract: This paper offers a new perspective on the limits of machine learning: the ceiling on progress is set not by model size or algorithm choice bu
arXiv:2604.03512v2 Announce Type: replace Abstract: Outage management in large-scale cloud operations remains heavily manual, requiring rapid triage, cross-team coordination, and experience-driven dec
arXiv:2604.09437v1 Announce Type: new Abstract: A novel regularization technique, AdaCubic, is proposed that adapts the weight of the cubic term. The heart of AdaCubic is an auxiliary optimization pro
arXiv:2604.04161v2 Announce Type: replace Abstract: In Vision-Language-Action (VLA) models, action chunking (i.e., executing a sequence of actions without intermediate replanning) is a key technique t
arXiv:2604.09280v1 Announce Type: cross Abstract: Extranodal extension (ENE) is an emerging prognostic factor in human papillomavirus (HPV)-associated oropharyngeal cancer (OPC), although it is curren
arXiv:2604.09451v1 Announce Type: cross Abstract: The accelerometer has become an almost ubiquitous device, providing enormous opportunities in healthcare monitoring beyond step counting or other aver
arXiv:2511.05168v2 Announce Type: replace Abstract: Vision foundation models achieve strong performance on both global and locally dense downstream tasks. Pretrained on large images, the recent DINOv3
arXiv:2509.02967v3 Announce Type: replace-cross Abstract: Traditional neural networks struggle to capture the spectral structure of complex signals. Fourier neural networks (FNNs) attempt to address t
arXiv:2604.09364v1 Announce Type: cross Abstract: When a Vision-Language Model (VLM) sees a blue banana and answers 'yellow', is the problem of perception or arbitration? We explore the question in te
arXiv:2603.24245v3 Announce Type: replace Abstract: Micro-actions, fleeting and low-amplitude motions, such as glances, nods, or minor posture shifts, carry rich social meaning but remain difficult fo
Build **b8772** is a recent incremental release of [llama.cpp](https://github.com/ggml-org/llama.cpp), the open-source C/C++ library for local LLM inference. Based on surrounding release activity, it
Build **b8779** is an incremental release of [llama.cpp](https://github.com/ggml-org/llama.cpp), an open-source C/C++ framework for running large language model inference locally and in the cloud. Lik
arXiv:2604.09308v1 Announce Type: new Abstract: Large language models are making autonomous drug discovery agents increasingly feasible, but reliable success in this setting is not determined by any s
arXiv:2604.09155v1 Announce Type: cross Abstract: Graphical user interface (GUI) agents powered by vision language models (VLMs) are rapidly moving from passive assistance to autonomous operation. How
arXiv:2604.08643v1 Announce Type: new Abstract: User interactions in online recommendation platforms create interdependencies among content creators: feedback on one creator's content influences the s
arXiv:2604.08584v1 Announce Type: cross Abstract: Long-context LLMs increasingly rely on extended, reusable prefill prompts for agents and domain Q&A, pushing attention and KV-cache to become the domi
arXiv:2604.09425v1 Announce Type: new Abstract: Vision Language Models (VLMs) have achieved remarkable success by integrating visual encoders with large language models (LLMs). While VLMs process dens
arXiv:2604.08644v1 Announce Type: new Abstract: This technical report introduces EXAONE 4.5, the first open-weight vision language model released by LG AI Research. EXAONE 4.5 is architected by integr
arXiv:2604.09142v1 Announce Type: new Abstract: Despite remarkable advances in image-driven stereo matching over the past decade, Synthetic-to-Realistic Zero-Shot (Syn-to-Real) generalization remains
At Google Cloud, we often see customers asking themselves: 'How can we manage our generative AI costs effectively without sacrificing the performance and availability our applications demand?' This is
This Reddit post from r/StableDiffusion shares ComfyUI inpainting workflows for several modern AI image models, including Z-Image, Qwen Image/Edit, and Flux-series models . Flux Fill is a dedicated in
arXiv:2604.09512v1 Announce Type: new Abstract: Transformers have emerged as the dominant neural-network architecture, achieving state-of-the-art performance in language processing and computer vision
arXiv:2604.08915v1 Announce Type: cross Abstract: Existing defect/anomaly generation methods often rely on few-shot learning, which overfits to specific defect categories due to the lack of large-scal
Maybe hot take - I’ve read a bunch of RL for image generation papers over last few months and honestly it’s been pretty disappointing. All of them are variations of GRPO and all of them are incrementa
arXiv:2604.08868v1 Announce Type: cross Abstract: To ensure safe clinical integration, deep learning models must provide more than just high accuracy; they require dependable uncertainty quantificatio
arXiv:2604.09021v1 Announce Type: cross Abstract: Auditory large language models (ALLMs) have demonstrated strong general capabilities in audio understanding and reasoning tasks. However, their reliab
arXiv:2604.09406v1 Announce Type: new Abstract: Training large language models (LLMs) is constrained by memory requirements, with activations accounting for a substantial fraction of the total footpri
arXiv:2412.12242v2 Announce Type: replace-cross Abstract: Creative visual concept generation often draws inspiration from specific concepts in a reference image to produce relevant outcomes. However,
arXiv:2604.09028v1 Announce Type: cross Abstract: Unmanned aerial vehicles serving as aerial base stations can rapidly restore connectivity after disasters, yet abrupt changes in user mobility and tra
arXiv:2603.27979v2 Announce Type: replace Abstract: We propose RetinexDualV2, a unified, physically grounded dual-branch framework for diverse Ultra-High-Definition (UHD) image restoration. Unlike gen
arXiv:2604.09511v1 Announce Type: new Abstract: Universal image restoration (UIR) aims to recover clean images from diverse and unknown degradations using a unified model. Existing UIR methods primari
arXiv:2604.09143v1 Announce Type: new Abstract: This paper introduces a score-driven rating system, a generalization of the classical Elo rating system that employs the score, i.e. the gradient of the
arXiv:2507.20185v2 Announce Type: replace Abstract: Session history is a common way of recording user interacting behaviors throughout a browsing activity with multiple products. For example, if an us
arXiv:2604.09487v1 Announce Type: cross Abstract: Tendon drives paired with soft muscle actuation enable faster and safer robots while potentially accelerating skill acquisition. Still, these systems