Knowledge catalogue
arXiv:2603.03692v2 Announce Type: replace Abstract: Classifier-Free Guidance (CFG) has established the foundation for guidance mechanisms in diffusion models, showing that well-designed guidance proxi
arXiv:2410.04509v3 Announce Type: replace Abstract: As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to revolutionize artificial intelligence is particular
arXiv:2604.18452v1 Announce Type: cross Abstract: Vision-language modeling is rapidly increasing in popularity with an ever expanding list of available models. In most cases, these vision-language mod
arXiv:2505.15353v3 Announce Type: replace Abstract: Log-likelihood vectors define a common space for comparing language models as probability distributions, enabling unified comparisons across heterog
arXiv:2502.13464v2 Announce Type: replace Abstract: Commonsense plausibility estimation is critical for evaluating language models (LMs), yet existing generative approaches--reliant on likelihoods or
arXiv:2509.11206v4 Announce Type: replace-cross Abstract: Practitioners increasingly rely on Large Language Models (LLMs) to evaluate generative AI outputs through 'LLM-as-a-Judge' approaches. However
arXiv:2604.16744v1 Announce Type: new Abstract: We present a framework for evaluating adaptive personalization of educational reading materials with theory-grounded simulated learners. The system buil
arXiv:2604.16980v1 Announce Type: new Abstract: Background: Large language models (LLMs) are increasingly proposed for diagnostic support, but few evaluations use real-world multimodal inpatient data,
arXiv:2604.16575v1 Announce Type: new Abstract: Unsupervised anomaly detection is widely used to detect Distributed Denial-of-Service (DDoS) attacks in cloud-native 5G networks, yet most studies assum
arXiv:2508.17458v2 Announce Type: replace Abstract: Verbal multiword expressions (VMWEs) remain difficult for machine translation because their meanings are often not recoverable from their component
arXiv:2604.16706v1 Announce Type: cross Abstract: Automated evaluation of tool-using large language model (LLM) agents is widely assumed to be reliable, but this assumption has rarely been validated a
arXiv:2604.18320v1 Announce Type: new Abstract: Self-evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo-label-based methods suffer from progressive quality degr
Every docs team I've been on ends up punting on the same project: making code samples in docs testable in CI. We know it's important but the implementation cost is too great. Skills + deep agents fina
arXiv:2601.10306v2 Announce Type: replace-cross Abstract: While Reinforcement Learning (RL) has advanced LLM reasoning, applying it to long-context scenarios is hindered by sparsity of outcome rewards
arXiv:2604.17087v1 Announce Type: new Abstract: Recent Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language understanding tasks, yet their inference efficie
arXiv:2508.07809v5 Announce Type: replace Abstract: Reinforcement learning with verifiable reward (RLVR) has become a promising paradigm for post-training large language models (LLMs) to improve their
arXiv:2604.17753v1 Announce Type: cross Abstract: Merging multiple Low-Rank Adaptation (LoRA) experts into a single backbone is a promising approach for efficient multi-task deployment. While existing
arXiv:2604.18052v1 Announce Type: cross Abstract: Intrusion detection systems (IDSs) for 5G networks must handle complex, high-volume traffic. Although opaque 'black-box' models can achieve high accur
arXiv:2602.16090v2 Announce Type: replace-cross Abstract: The response of the climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and sl
Excited to partner with the SpaceX team to scale up Composer. A meaningful step on our path to build the best place to code with AI. SpaceXAI and @cursor_ai are now working closely together to create
arXiv:2604.16528v1 Announce Type: new Abstract: Embryo selection is one of multiple crucial steps in in-vitro fertilization, commonly based on morphological assessment by clinical embryologists. Altho
arXiv:2504.04814v3 Announce Type: replace-cross Abstract: Trustworthy artificial intelligence (AI) is essential in healthcare, particularly for high-stakes tasks like medical image segmentation. Expla
arXiv:2512.11108v3 Announce Type: replace Abstract: Good quality explanations strengthen the understanding of language models and data. Feature attribution methods, such as Integrated Gradient, are a
arXiv:2604.17879v1 Announce Type: new Abstract: Camouflaged Object Detection is challenging due to the high degree of similarity between camouflaged objects and their surrounding backgrounds. Current
arXiv:2604.18296v1 Announce Type: new Abstract: Static concreteness ratings are widely used in NLP, yet a word's concreteness can shift with context, especially in figurative language such as metaphor
arXiv:2502.13637v2 Announce Type: replace Abstract: Human affordance learning investigates contextually relevant novel pose prediction such that the estimated pose represents a valid human action with
arXiv:2604.16757v1 Announce Type: new Abstract: The expression of emotions that serve social purposes, such as asserting independence or fostering interdependence, is central to human interactions and
arXiv:2505.22226v2 Announce Type: replace Abstract: Recent theoretical advances reveal that the Hadamard product induces nonlinear representations and implicit high-dimensional mappings for the field
arXiv:2604.18168v1 Announce Type: new Abstract: Few-step generation has been a long-standing goal, with recent one-step generation methods exemplified by MeanFlow achieving remarkable results. Existin
arXiv:2604.16865v1 Announce Type: cross Abstract: In this paper, we consider the problem of extraction of most informative features from time series that are regarded as observed values of stochastic
arXiv:2604.16450v1 Announce Type: cross Abstract: Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess de
arXiv:2604.16610v1 Announce Type: cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability. Conventional fairness
arXiv:2604.16780v1 Announce Type: new Abstract: This paper presents FairNVT, a lightweight debiasing framework for pretrained transformer-based encoders that improves both representation and predictio
arXiv:2506.12176v5 Announce Type: replace Abstract: In explainable AI, surrogate models are commonly evaluated by their fidelity to a neural network's predictions. Fidelity, however, measures alignmen
arXiv:2601.11886v2 Announce Type: replace Abstract: In high-stakes domains like medicine, it may be generally desirable for models to faithfully adhere to the context provided. But what happens if the
arXiv:2512.20182v3 Announce Type: replace Abstract: Recognizing whether outputs from large language models (LLMs) contain faithfulness hallucination is crucial for real-world applications, e.g., retri
SpaceX's Falcon rockets conduct frequent launches occurring every few days, with public viewing opportunities available at launch facilities in Florida and California. This statement reflects SpaceX's
Famegrid Checkpoint ZIB is built on Z-Image Base and produces modern, social-media-style photography with a natural look, designed to create polished yet believable images with good composition and li
arXiv:2604.16522v1 Announce Type: new Abstract: This paper proposes a fast and online method for jointly performing 3D multi-object tracking and pose estimation using multiple monocular cameras. Our a
arXiv:2604.18491v1 Announce Type: new Abstract: Computational Fluid Dynamics (CFD) is central to race-car aerodynamic development, yet its cost -- tens of thousands of core-hours per high-fidelity eva
arXiv:2604.17956v1 Announce Type: new Abstract: Machine learning has become integral to medical research and is increasingly applied in clinical settings to support diagnosis and decision-making; howe
arXiv:2604.16778v1 Announce Type: new Abstract: LLM-powered agents often reason from scratch when presented with a new problem instance and lack automatic mechanisms to transfer learned skills to othe
arXiv:2604.16612v1 Announce Type: new Abstract: Traffic prediction plays a central role in intelligent transportation systems (ITS) by supporting real-time decision-making, congestion management, and
arXiv:2604.16574v1 Announce Type: new Abstract: Federated Learning (FL) faces challenges from client data heterogeneity and resource-constrained mobile devices, which can degrade model accuracy. Perso
Ashley Gold / Axios: Filing: Anthropic spent 1.6M and OpenAI 1M on lobbying in Q1, vs. 360K and 560K in Q1 2025, respectively; Meta topped Big Tech lobbying spending with 7.1M — Both Anthropic and Ope
Echo Wang / Reuters: Filing: SpaceX tells investors that orbital AI data centers use “unproven technologies” and may not achieve “commercial viability” due to space-related risks — SpaceX warned inves
Bloomberg: Filings: crypto PACs amassed a ~180M war chest for the 2026 midterms, including Fairshake's 166M, giving them the ability to outspend some GOP super PACs — Political groups tied to the cryp
arXiv:2510.24942v2 Announce Type: replace-cross Abstract: Despite their impressive performance, vision-language models (VLMs) still struggle on culturally situated inputs. To understand how VLMs proce
arXiv:2511.17171v4 Announce Type: replace Abstract: Predicting wildfire risk is a reasoning-intensive spatial problem that requires the integration of visual, climatic, and geographic factors to infer
arXiv:2604.17919v1 Announce Type: new Abstract: Recent advances in flow-based offline reinforcement learning (RL) have achieved strong performance by parameterizing policies via flow matching. However
The 2026 edition of Adobe Summit this week marks a historic turning point for the software giant. It not only showcased the next frontier of “agentic artificial intelligence” but also served as the sw
arXiv:2604.16649v1 Announce Type: new Abstract: Directed energy deposition (DED) produces complex thermo-mechanical responses that can lead to distortion and reduced dimensional accuracy of a manufact
arXiv:2604.17344v1 Announce Type: cross Abstract: When task-specific labels are not available, it becomes difficult to select an embedding model for a specific target corpus. Existing labelless measur
arXiv:2604.17513v1 Announce Type: new Abstract: Simulation frameworks such as Isaac Sim have enabled scalable robot learning for locomotion and rigid-body manipulation; however, contact-rich simulatio
arXiv:2604.17720v1 Announce Type: cross Abstract: Point-based Neural Networks (PNNs) have become a key approach for point cloud processing. However, a core operation in these models, Farthest Point Sa
Flexible Aspect Ratios ChatGPT Images 2.0 supports aspect ratios as wide as 3:1 and as tall as 1:3. It can generate outputs that are ready to fit the formats you need, from wide banners and presentati
arXiv:2511.00868v2 Announce Type: replace Abstract: Large Language Model (LLM) serving is increasingly constrained by the growing size of the key-value (KV) cache, which scales with both context lengt
arXiv:2604.18109v1 Announce Type: new Abstract: This paper presents factorized linear projection (FLiP) models for understanding pretrained sentence embedding spaces. We train FLiP models to recover t
Alex Ebert / Bloomberg Law: Florida AG James Uthmeier issues criminal subpoenas to OpenAI to investigate if ChatGPT's role in planning a mass shooting constitutes criminal liability — Florida is sendi
Florida is opening a criminal investigation into ChatGPT following new details in the mass shooting at Florida State University last year. Investigators say the shooter used ChatGPT to plan the attack