Knowledge catalogue
arXiv:2604.07518v1 Announce Type: new Abstract: Vision-Language Models often struggle with complex visual reasoning due to the visual information loss in textual CoT. Existing methods either add the c
A Reddit post in r/ChatGPT titled 'Deploying my vibe-coded app on production' discusses the experience of taking an AI-generated application to a live production environment. The post reflects a br...
arXiv:2604.06220v1 Announce Type: cross Abstract: Sign language recognition (SLR) is vital for bridging communication gaps between deaf and hearing communities. Vision-based approaches suffer from occ
arXiv:2604.07723v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) aims to segment arbitrary category regions in images using open-vocabulary prompts, necessitating that exis
arXiv:2604.06483v1 Announce Type: cross Abstract: Large language models that require multiple GPU cards to host are usually the most capable models. It is necessary to understand and steer these model
arXiv:2604.08284v1 Announce Type: new Abstract: Large language models store not only isolated facts but also rules that support reasoning across symbolic expressions, natural language explanations, an
arXiv:2402.02249v3 Announce Type: replace Abstract: We study how to best spend a budget of noisy labels to compare the accuracy of two binary classifiers. It's common practice to collect and aggregate
arXiv:2604.08369v1 Announce Type: cross Abstract: Inference-time compute scaling has emerged as a powerful technique for improving the reliability of large language model (LLM) agents, but existing me
arXiv:2604.05350v2 Announce Type: replace Abstract: Enterprise IT support interactions are fundamentally diagnostic: effective resolution requires iterative evidence gathering from ambiguous user repo
arXiv:2604.03540v2 Announce Type: replace Abstract: Although multi-step generative policies achieve strong performance in robotic manipulation by modeling multimodal action distributions, they require
arXiv:2512.03745v2 Announce Type: replace Abstract: Two-stage learning pipeline has achieved promising results in unsupervised visible-infrared person re-identification (USL-VI-ReID). It first perform
arXiv:2601.07994v4 Announce Type: replace Abstract: Large Language Models (LLMs) increasingly operate over long-form dialogues with frequent topic shifts. While recent LLMs support extended context wi
arXiv:2604.08543v1 Announce Type: new Abstract: Event cameras offer multiple advantages in monocular egocentric 3D human pose estimation from head-mounted devices, such as millisecond temporal resolut
arXiv:2510.05261v2 Announce Type: replace Abstract: The Lipschitz constant is a key measure for certifying the robustness of neural networks to input perturbations. However, computing the exact consta
arXiv:2604.07659v1 Announce Type: new Abstract: Large language models (LLMs) hold significant promise for healthcare, yet their reliability in high-stakes clinical settings is often compromised by hal
arXiv:2604.07490v1 Announce Type: new Abstract: Representation learning for geospatial and spatio-temporal data plays a critical role in enabling general-purpose geospatial intelligence. Recent geospa
arXiv:2604.08456v1 Announce Type: cross Abstract: Despite rapid progress, pretrained vision-language models still struggle when answers depend on tiny visual details or on combining clues spread acros
arXiv:2604.07072v1 Announce Type: new Abstract: Offline reinforcement learning learns policies from fixed datasets without further environment interaction. A key challenge in this setting is epistemic
arXiv:2604.08258v1 Announce Type: new Abstract: In the automated co-design of soft robots, precisely adapting the material stiffness field to task environments is crucial for unlocking their full phys
arXiv:2511.08409v4 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) frequently suffer from unfaithfulness, generating reasoning chains that drift from visual evidence or contr
arXiv:2512.24290v2 Announce Type: replace-cross Abstract: Optical-readout Time Projection Chambers (TPCs) produce megapixel-scale images whose fine-grained topological information is essential for rar
arXiv:2601.21708v2 Announce Type: replace Abstract: Large language models (LLMs) excel across many tasks, yet inference is still dominated by strictly token-by-token autoregression. Existing accelerat
arXiv:2604.07675v1 Announce Type: new Abstract: Accurate prediction of next-day wildfire spread is critical for disaster response and resource allocation. Existing deep learning approaches typically c
arXiv:2604.08526v1 Announce Type: new Abstract: Given a person and a garment image, virtual try-on (VTO) aims to synthesize a realistic image of the person wearing the garment, while preserving their
arXiv:2604.07084v1 Announce Type: cross Abstract: Open-loop end-to-end neural motion planners have recently been proposed to improve motion planning for robotic manipulators. These methods enable plan
arXiv:2604.06652v1 Announce Type: new Abstract: Adaptive moment methods such as Adam use a diagonal, coordinate-wise preconditioner based on exponential moving averages of squared gradients. This diag
arXiv:2604.07879v1 Announce Type: new Abstract: Diffusion-based image generation models have advanced rapidly but pose a safety risk due to their potential to generate Not-Safe-For-Work (NSFW) content
arXiv:2604.07394v1 Announce Type: cross Abstract: The quadratic computational complexity of standard attention mechanisms presents a severe scalability bottleneck for LLMs in long-context scenarios. W
arXiv:2604.08322v1 Announce Type: new Abstract: Fundus imaging such as CFP, OCT and UWF is crucial for the early detection of retinal anomalies and diseases. Fundus image understanding, due to its kno
arXiv:2604.07728v1 Announce Type: new Abstract: High-fidelity interactive digital assets are essential for embodied intelligence and robotic interaction, yet articulated objects remain challenging to
arXiv:2604.08230v1 Announce Type: new Abstract: Object detection models trained on a source domain often exhibit significant performance degradation when deployed in unseen target domains, due to vari
arXiv:2604.06767v1 Announce Type: new Abstract: Language models operate on discrete tokens but compute in continuous vector spaces, inducing a Voronoi tessellation over the representation manifold. We
arXiv:2510.23868v4 Announce Type: replace Abstract: This paper proposes extit{Group-relative Implicit Fine-Tuning (GIFT)}, a reinforcement learning framework for aligning large language models (LLMs
A Reddit post on r/ChatGPT highlighted that GPT Image 1.0 (referred to colloquially as 'GPT Image 2.0' in the thread title) marked a notable improvement in AI image generation by accurately renderi...
arXiv:2604.07517v1 Announce Type: new Abstract: Building generalist robots capable of performing functional grasping in everyday, open-world environments remains a significant challenge due to the vas
arXiv:2604.07808v1 Announce Type: new Abstract: Full-parameter fine-tuning of large language models is constrained by substantial GPU memory requirements. Low-rank adaptation methods mitigate this cha
arXiv:2604.08046v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs) by providing access to external knowledge. However, current res
arXiv:2604.06191v1 Announce Type: cross Abstract: Automated phoneme-level pronunciation assessment is vital for scalable speech therapy and language learning, yet validated tools for Arabic remain sca
arXiv:2604.07812v1 Announce Type: new Abstract: In multimodal large language models (MLLMs), the surge of visual tokens significantly increases the inference time and computational overhead, making th
Nous Research draws its name from the ancient Greek concept of *nous* — the faculty of directly perceiving truth, reason, and divine reality — while its flagship model series, **Hermes**, is named ...
arXiv:2603.20843v2 Announce Type: replace Abstract: Long-context language modeling is commonly framed as a scalability challenge of token-level attention, yet local-to-global information structuring r
arXiv:2512.09928v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have recently enabled robotic manipulation by grounding visual and linguistic cues into actions. However, most V
arXiv:2604.08305v1 Announce Type: cross Abstract: Immunohistochemistry (IHC) is essential for assessing specific immune biomarkers like Human Epidermal growth-factor Receptor 2 (HER2) in breast cancer
arXiv:2604.06614v1 Announce Type: cross Abstract: Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language models to downstream
Healthcare IT finds itself at a critical intersection where significant data opportunities are colliding with escalating cybersecurity threats. Rural and community hospitals, in particular, are facing
arXiv:2511.09170v2 Announce Type: replace Abstract: This article presents HOTFLoc++, an end-to-end hierarchical framework for LiDAR place recognition, re-ranking, and 6-DoF metric localisation in fore
arXiv:2511.03295v3 Announce Type: replace-cross Abstract: Automatic evaluation of ST systems is typically performed by comparing translation hypotheses with one or more reference translations. While e
arXiv:2604.06715v1 Announce Type: cross Abstract: Remote sensing semantic segmentation requires models that can jointly capture fine spatial details and high-level semantic context across complex scen
arXiv:2604.08435v1 Announce Type: new Abstract: It remains challenging to assess driver fatigue from untrimmed videos under constrained computational budgets, due to the difficulty of modeling long-ra
arXiv:2604.08256v1 Announce Type: new Abstract: Long-term memory is essential for conversational agents to maintain coherence, track persistent tasks, and provide personalized interactions across exte
arXiv:2508.13657v2 Announce Type: replace-cross Abstract: Combined Algorithm Selection and Hyperparameter Optimization (CASH) has been fundamental to traditional AutoML systems. However, with the adva
arXiv:2604.06796v1 Announce Type: cross Abstract: Latent variable models, including variational autoencoders (VAE), remain a central tool in modern deep generative modeling due to their scalability an
arXiv:2603.11090v2 Announce Type: replace Abstract: Prior-data fitted networks (PFNs) have emerged as powerful foundation models for tabular causal inference, yet their extension to time series remain
arXiv:2510.05524v2 Announce Type: replace Abstract: We present Knowledge Extraction on OMIn (KEO), a domain-specific knowledge extraction and reasoning framework with large language models (LLMs) in s
La France a parmi les meilleurs mathématiciens et ingénieurs IA du monde. On le sait. On les embauche partout ailleurs. Et la première chose qu'on fait au moment où on pourrait enfin capitaliser dessu
arXiv:2604.07802v1 Announce Type: new Abstract: Large-scale vision-language models (VLMs) exhibit remarkable zero-shot capabilities, yet the internal mechanisms driving their anomaly detection (AD) pe
arXiv:2604.07569v1 Announce Type: cross Abstract: Despite the increasing prevalence of large language models (LLMs), we still have a limited understanding of how their representational spaces are stru
arXiv:2604.06255v1 Announce Type: cross Abstract: Stellar astrophysics relies critically on accurate descriptions of the physical conditions inside stars. Traditional solvers such as exttt{MESA} (Mo
arXiv:2604.07027v1 Announce Type: new Abstract: Nonstationarity is ubiquitous in practical classification settings, leading deployed models to perform poorly even when they generalize well to holdout
arXiv:2604.08425v1 Announce Type: cross Abstract: When humans label subjective content, they disagree, and that disagreement is not noise. It reflects genuine differences in perspective shaped by anno