Knowledge catalogue
arXiv:2609.06668v1 Announce Type: new Abstract: Multimodal graphs couple node attributes in different modalities, such as text and images, with relational structure, enabling topological structure and
arXiv:2406.09385v3 Announce Type: replace Abstract: Vision-Language Foundation Models (VLFMs) have made remarkable progress on various multimodal tasks, such as image captioning, image-text retrieval,
arXiv:2512.00311v3 Announce Type: replace Abstract: Knowledge Tracing (KT) aims to model student's knowledge state and predict future performance to enable personalized learning in Intelligent Tutorin
arXiv:2609.08265v1 Announce Type: new Abstract: Multi-object tracking (MOT) is an essential computer vision task that simultaneously tracks multiple objects in video sequences, with various applicatio
arXiv:2609.06671v1 Announce Type: new Abstract: Group-based RLVR methods estimate advantages by repeatedly sampling multiple trajectories for each prompt, making long-horizon agent training expensive
arXiv:2609.07373v1 Announce Type: new Abstract: Modern Visual Place Recognition (VPR) methods excel on standard benchmarks yet remain brittle in feature-poor environments. By treating each query image
arXiv:2609.06806v1 Announce Type: new Abstract: Training strategy, namely whether to retrain from scratch or fine-tune from the previous checkpoint, is an overlooked decision variable in active learni
arXiv:2609.09054v1 Announce Type: new Abstract: Task vectors enable post-training model editing by identifying semantically meaningful directions in weight space, typically computed as the difference
arXiv:2609.08981v1 Announce Type: new Abstract: A growing body of work establishes that large language models are not mere statistical memorizers, but are capable of in-context learning: performing in
arXiv:2609.07610v1 Announce Type: new Abstract: We convert black-box clinical prediction models for tabular data into standalone nomograms that can be audited term by term. PRiSM (Partial Responses in
arXiv:2609.06665v1 Announce Type: new Abstract: Diffusion-based models enable monocular geometry estimation, yet their pixel-space precision is limited by a shared, under-studied error source: VAE rec
arXiv:2609.06011v1 Announce Type: cross Abstract: Omni-modal large language models (OLLMs) jointly process vision, audio, and text, yet their modality bias under cross-modal conflict remains underexpl
arXiv:2609.08659v1 Announce Type: new Abstract: Onboard object detection in Earth observation is constrained by limited computational resources and the absence of fully corrected imagery. While convol
arXiv:2609.08303v1 Announce Type: new Abstract: Pancreatic tumor segmentation in 3D CT volumes is challenged by extreme scale variability across both the pancreas and tumor, and highly irregular tumor
arXiv:2609.07051v1 Announce Type: new Abstract: World models increasingly serve as the predictive core of model-based reinforcement learning agents, enabling them to simulate future dynamics and reaso
Trump’s ignorance about AI is appalling and extremely dangerous. No, Mr. President. We won’t have a “little gear” to protect humanity from the threat of an uncontrolled AI. We must pause development N
arXiv:2609.07063v1 Announce Type: new Abstract: Built upon local differential privacy (LDP), locally private graph learning protocols have emerged as an important paradigm for decentralized graph lear
arXiv:2609.08341v1 Announce Type: new Abstract: On-Policy Distillation (OPD) facilitates the transfer of knowledge from domain expert to student in the post-training phase of Large Language Models (LL
arXiv:2609.07734v1 Announce Type: new Abstract: Gaussian Splatting has been effective in inferring scene representations that excel in novel view synthesis. Multiple splats cooperate seamlessly to syn
arXiv:2506.05626v3 Announce Type: replace Abstract: Real-world knowledge can take various forms, including structured, semi-structured, and unstructured data. Among these, Knowledge Graphs (KGs) are s
arXiv:2609.08034v1 Announce Type: new Abstract: Localized dimensionality reduction improves the scalability of operator learning for high-dimensional partial differential equations (PDEs), but indepen
arXiv:2606.14883v2 Announce Type: replace Abstract: Continual vision-language models are commonly addressed through sequential fine-tuning; however, although this paradigm enables adaptation to new en
arXiv:2307.02719v5 Announce Type: replace Abstract: Uncertainty sampling is a classical active-learning strategy, yet the statistical objective induced by its query rule is often implicit. We introduc
arXiv:2511.22341v2 Announce Type: replace Abstract: Previous works identify sensitivity to option order as a key issue in multiple-choice VQA (MC-VQA) evaluation and propose protocols to mitigate this
arXiv:2511.13032v2 Announce Type: replace Abstract: We present Uni-Inter, a unified framework for human motion generation that supports a wide range of interaction scenarios: including human-human, hu
arXiv:2609.06729v1 Announce Type: new Abstract: Accurate 3D brain tumour segmentation from multi-modal Magnetic Resonance Imaging (MRI) is essential for clinical diagnosis and treatment planning. Exis
arXiv:2511.03272v2 Announce Type: replace Abstract: Diffusion-based text-to-video models are increasingly capable, but mask-based editing over hundreds of frames remains challenging: naive long-video
arXiv:2609.06785v1 Announce Type: new Abstract: Out-of-Distribution (OOD) detection remains a fundamental challenge for neural networks, whose predictions can be overconfident on inputs that deviate f
arXiv:2609.07420v1 Announce Type: new Abstract: Vision cues are available and informative for pedestrian action prediction, but obtaining stable target-centric representations from video frames remain
arXiv:2609.05888v1 Announce Type: new Abstract: In this paper, we address the challenging problem of 4D reconstruction from sparse-view videos. This setup usually relies on monocular depth estimation
arXiv:2609.06591v1 Announce Type: new Abstract: Developing unified physics-based humanoid controllers that can navigate complex 3D scenes and manipulate objects remains a longstanding challenge. Exist
arXiv:2603.22851v2 Announce Type: replace Abstract: We present UniQueR, a unified query-based feedforward framework for efficient and accurate 3D reconstruction from unposed images. Existing feedforwa
arXiv:2609.07213v1 Announce Type: cross Abstract: We observe a phenomenon that current algorithmic research in the field of explainable artificial intelligence primarily pursues better performance on
arXiv:2609.07159v1 Announce Type: new Abstract: The decipherment of historical encrypted manuscripts poses a fundamental challenge in Digital Humanities: before any transcription can begin, the symbol
arXiv:2609.05636v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are able to achieve impressive zero-shot classification performance by aligning visual and textual representations, but ea
https://www.reuters.com/technology/us-accuses-chinese-ai-firms-industrial-scale-theft-ai-technology-2026-09-08/ https://www.nbcnews.com/tech/tech-news/us-accuses-china-ai-developers-deepseek-alibaba-c
arXiv:2602.19322v2 Announce Type: replace Abstract: Ultrasound (US) imaging poses unique challenges for representation learning due to its inherently noisy acquisition process. The low signal-to-noise
Use Ollama models in ChatGPT Desktop Ollama models can now be used directly in ChatGPT Desktop, so you can keep your existing workflow while running open models. Setup is available from the Ollama app
arXiv:2609.06245v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) perform strongly on general visual understanding tasks such as visual question answering, yet they often strugg
Cloud platform and AI tooling company — AI SDK, AI Gateway, Eve agent framework, Sandbox, and Observability used by this wiki’s public Ask surface.
arXiv:2609.08342v1 Announce Type: new Abstract: World models take multimodal inputs like text, photos, and diagrams to generate dynamic scenes in accordance with the laws of physics, thus opening a co
arXiv:2609.06100v1 Announce Type: new Abstract: Verifiable outcome rewards guide language-model post-training, but sequence-level advantages do not identify which token-level decisions should be prese
arXiv:2609.08040v1 Announce Type: cross Abstract: The software supply chain has become an increasingly exposed attack surface because of its reliance on intricate yet fragile dependencies. Existing de
arXiv:2609.08079v1 Announce Type: new Abstract: Recent advances in video generation have made prompt-based control increasingly central to AIGC video generation. Prompts specify what a video should de
arXiv:2609.06652v1 Announce Type: new Abstract: Video foundation models increasingly rely on large-scale pretraining data, yet the end-to-end data pipelines behind them remain largely closed and diffi
arXiv:2609.05541v1 Announce Type: new Abstract: Implicit Neural Representations (INR) provide a compact and content-adaptive paradigm for video compression, typically representing a video through shar
arXiv:2609.05537v1 Announce Type: new Abstract: Tracking social media posts can help spot early signs of depression. Recent studies show that combining text and images works better for detecting depre
arXiv:2603.17859v3 Announce Type: replace Abstract: Human perceptual priors have shown promise in saliency-guided deep learning training, particularly in the domain of iris presentation attack detecti
arXiv:2609.08905v1 Announce Type: new Abstract: Workspace analysis measures where a robot can place its end effector. For visually guided manipulation, reachability alone is insufficient: a kinematica
arXiv:2609.08216v1 Announce Type: cross Abstract: Agentic AI systems built on large language models fail in two persistent ways that scaling does not fix: they break under distribution shift, and they
arXiv:2606.14787v2 Announce Type: replace Abstract: We study six production image-to-image AI systems (gpt-image-1, Gemini 2.5 Flash Image, Flux Kontext, SDXL img2img, SD3 img2img, and Qwen Image Edit
arXiv:2609.06497v1 Announce Type: cross Abstract: Multimodal sentiment analysis requires effective modeling of both verbal semantics and non-verbal affective cues. A central challenge is to calibrate
arXiv:2609.06410v1 Announce Type: cross Abstract: Existing approaches to visual attribute value extraction (AVE) primarily rely on static product images, failing to capture temporal cues, multi-angle
arXiv:2609.05579v1 Announce Type: new Abstract: Predicting postoperative spinal morphology from preoperative radiographs could provide valuable support for scoliosis surgical planning, but remains cha
arXiv:2609.06508v1 Announce Type: new Abstract: Long-horizon robot manipulation with Vision-Language-Action (VLA) policies remains vulnerable to execution-time deviations, as final task success provid
arXiv:2609.07815v1 Announce Type: new Abstract: Current text-to-image systems typically employ a 'text encoder plus diffusion decoder' paradigm, in which text semantics directly modulate continuous la
arXiv:2411.08727v2 Announce Type: replace Abstract: Robots in human-centered environments require accurate scene understanding to perform high-level tasks effectively. This understanding can be achiev
We are at a watershed moment. The vibe is shifting. It's not over yet! We can still stop ASI! This week an Anthropic researcher quit, warning AI companies are 'gambling with our lives', and ControlAI'
We benchmarked DeepSeek V4.1 Flash by @deepseek_ai . It reached 98% of GPT-6 Astra’s score at 1.4% of the cost on everyday design tasks based on user requests. Every model except Astra scored lower AN
we broke down the full open model stack for agentic coding and how to swap each layer independently model → inference → gateway/router → harness → tools the stack stays flexible as better models, prov