Knowledge catalogue
arXiv:2607.18020v2 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) solve PDEs by incorporating physical constraints into neural-network training, but large-scale problems are
arXiv:2607.20500v1 Announce Type: new Abstract: Large Language Models (LLMs) perform strongly on well-specified reasoning tasks with a feasible answer. However, problems encountered in the open world
Introducing FLUX 3. One multi-modal model for Image, Video, Audio and Action-Prediction. Creations are truer to life in every kind of style. Blog Post : https://bfl.ai/blog/flux-3 submitted by /u/pmtt
arXiv:2511.05611v2 Announce Type: replace Abstract: The core challenge in Action Quality Assessment (AQA) lies in extracting fine-grained motion features from redundant and complex video backgrounds.
For my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety an
I copy-pasted this cooking recipe and accidentally hit enter before adding the actual prompt. Normally this would result in ChatGPT interpreting the text, trying to guess what I need, and giving a wal
arXiv:2607.21227v1 Announce Type: new Abstract: Force-conditioned reinforcement learning (RL) enables tight-clearance assembly under a commanded force ceiling, but practical deployment requires determ
arXiv:2607.20583v1 Announce Type: cross Abstract: The function of many genes is still unknown, and conventional driver-discovery methods, which rely on how frequently a gene is mutated, cannot assess
arXiv:2607.19195v2 Announce Type: replace-cross Abstract: Using large deviations theory, we solve and obtain a general expression for the free energy functional for a broad class of associative memori
arXiv:2607.20946v1 Announce Type: new Abstract: Grievance is one of the warning signs analysts look for when assessing threats of violence. It is increasingly measured at scale from online text, most
arXiv:2607.20668v1 Announce Type: cross Abstract: TextGrad improves language-model systems by revising text from feedback. Its core thesis is that natural-language feedback can act as a gradient for o
arXiv:2607.20540v1 Announce Type: cross Abstract: How should a diffusion model decide which noise levels to train on, and how much? Despite the importance of this choice, current noise schedules are b
arXiv:2511.10806v1 Announce Type: cross Abstract: Image deblurring is vital in computer vision, aiming to recover sharp images from blurry ones caused by motion or camera shake. While deep learning ap
arXiv:2606.14502v2 Announce Type: replace Abstract: Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable of reason
arXiv:2510.15236v2 Announce Type: replace Abstract: Contemporary AGI evaluations report multidomain capability profiles, yet they typically assign symmetric weights and rely on snapshot scores. This c
arXiv:2607.18961v2 Announce Type: replace Abstract: Large language models (LLMs) generate fluent text by incrementally predicting the next token from a prefix. Critics in the generative tradition argu
arXiv:2607.20497v1 Announce Type: new Abstract: Prompt optimization for text classification spans diverse approaches, from demonstration selection to exploration-based search to error-driven diagnosis
arXiv:2607.21069v1 Announce Type: new Abstract: The original ALPHA benchmark introduced a taxonomy-aware penalty for evaluating CWE-level vulnerability prediction in Python and proposed that the penal
arXiv:2605.11936v2 Announce Type: replace Abstract: Recent soft prompt research has tried to improve reasoning by inserting trained vectors into LLM inputs, yet whether the gain comes from the learned
arXiv:2607.21530v1 Announce Type: cross Abstract: Concurrent stateful library APIs expose behavior through evolving resource ownership, lifecycle states, and competing interleavings. Large language mo
arXiv:2607.20970v1 Announce Type: new Abstract: Implicit neural representations (INRs) for time-varying volumetric data are typically trained using dense sampling over spatiotemporal coordinates, wher
arXiv:2607.21327v1 Announce Type: cross Abstract: Bibliometric indicators - citation counts, h-indexes, co-authorship networks - have long anchored science, technology, and innovation (STI) analytics,
arXiv:2607.20645v1 Announce Type: cross Abstract: We introduce Frontier Financial Judgement, a challenging new benchmark developed in collaboration with professional equity analysts to assess agents'
arXiv:2607.20955v1 Announce Type: new Abstract: Accurate segmentation of brain stroke lesions in non-contrast computed tomography (NCCT) scans is critical for rapid clinical decision-making, yet remai
arXiv:2607.21471v1 Announce Type: new Abstract: Dynamic-scene reconstruction is almost always evaluated inside the observed time window, yet deployment settings such as AR overlays, robot interaction,
arXiv:2607.20757v1 Announce Type: cross Abstract: Transformers are known to have internal continuous symmetries that leave outputs invariant, while modifying quantization. GaugeQuant leverages this in
arXiv:2411.00631v2 Announce Type: replace-cross Abstract: This scoping review examines the relationship between Generative AI (GenAI) and agency in education, analyzing the literature available throug
arXiv:2511.03354v2 Announce Type: replace-cross Abstract: Generative artificial intelligence (GenAI) is transforming bioinformatics by advancing genomics, proteomics, transcriptomics, structural biolo
arXiv:2607.20521v1 Announce Type: new Abstract: The state of a dynamic system evolves over time, switching among several latent modes that govern its observable behavior. Filtering methods infer the l
arXiv:2607.21085v1 Announce Type: new Abstract: Despite remarkable progress in visual understanding, Multimodal Large Language Models (MLLMs) remain prone to hallucinations when reasoning about spatia
arXiv:2607.20581v1 Announce Type: cross Abstract: Perturbation techniques that turn unsuccessful jailbreak prompts into successful ones are continuously evolving, constituting a major security threat
arXiv:2607.21036v1 Announce Type: new Abstract: Adversarial attacks against large vision-language models (LVLMs) serve as an effective means of assessing their robustness in cross-modal semantic under
arXiv:2607.17521v2 Announce Type: replace Abstract: Autonomous driving requires both safe and efficient planning decisions in dynamic 3D environments. Although recent Vision/Video-Action models learn
OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock. Learn how to select a model, run inference through the Responses API on the bedrock-mantle endpoint, reduce cost with
If you want to get the most out of MTP. You have to run some tests / benchmarks to do so. Turning it on with defaults will get improvements, but for many models and card combinations, you are leaving
arXiv:2505.23869v5 Announce Type: replace-cross Abstract: A proposition that connects randomness and compression is put forward via Gibbs entropy over set of measurement vectors associated with a loss
arXiv:2607.18218v2 Announce Type: replace-cross Abstract: Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and
Glad to see Google sharing data on how Gemini is being used. Especially interesting is that the usefulness of multimodal AI for manual labor may be greater than expected. https://blog.google/innovatio
arXiv:2607.21416v1 Announce Type: cross Abstract: Existing Gaussian-splatting-based monocular Simultaneous Localization and Mapping (SLAM) systems are either tailored to short sequences, are not real-
arXiv:2607.20443v1 Announce Type: new Abstract: We release GLAN-QnA-KR, a 303,581-row openly redistributable Korean instruction-QA corpus produced via the seedless taxonomy-driven GLAN synthesis pipel
arXiv:2607.21117v1 Announce Type: cross Abstract: Preprocessing blood glucose time-series data is a critical yet often overlooked step in developing data-driven methods for diabetes management, partic
GOAT For my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safe
arXiv:2607.20730v1 Announce Type: cross Abstract: Large language models increasingly use search tools to retrieve up-to-date information, introducing a new attack surface in which retrieved documents
arXiv:2607.21353v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific training data while preserving model utility. Many state-of-the-art approaches pursue this g
arXiv:2607.21324v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems increasingly employ multiple LLM agents. Yet, most prior work optimizes components in isolation rather th
arXiv:2607.21448v1 Announce Type: new Abstract: Dynamic scene reconstruction with 3D Gaussian Splatting requires a balance between fine-grained motion modeling, structural stability, and compact repre
arXiv:2607.21561v1 Announce Type: new Abstract: Molecular property prediction from structure often uses a single representative conformation, even though many molecules exist as conformational ensembl
arXiv:2607.21580v1 Announce Type: cross Abstract: Controllable video generation remains challenging due to the difficulty of specifying precise multi-object interactions using text prompts or motion-c
arXiv:2607.21341v1 Announce Type: new Abstract: Bimanual object reorientation - picking an object, handing it over between two arms, and placing it in a desired target pose - is valuable when direct p
arXiv:2607.20533v1 Announce Type: cross Abstract: Portfolio construction under the Black-Litterman model requires investors to specify views on asset returns alongside explicit uncertainty estimates -
arXiv:2607.21027v1 Announce Type: new Abstract: Current identity customized video generation methodologies are predominantly limited to single-identity scenarios, as the lack of explicit identity sepa
arXiv:2607.21522v1 Announce Type: cross Abstract: Creating dynamic and physically realistic 4D worlds from natural language descriptions is both fascinating and challenging. Traditional computer graph
arXiv:2607.20982v1 Announce Type: new Abstract: As large language model agents increasingly operate autonomously with access to tools and external environments, ensuring their safe and reliable behavi
arXiv:2607.21049v1 Announce Type: new Abstract: End-to-end visuomotor policies provide little opportunity for humans to understand or correct the policy's visual attention. We propose GuidedAttention,
arXiv:2603.22216v2 Announce Type: replace Abstract: The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods. However, these non-AR model
arXiv:2607.21105v1 Announce Type: new Abstract: Although Multimodal Large Language Models have achieved strong performance across a wide range of vision-language tasks, they still suffer from hallucin
arXiv:2606.17028v2 Announce Type: replace-cross Abstract: Simple linear and frequency-domain models remain surprisingly competitive in long-horizon time-series forecasting, and recent mechanistic evid
arXiv:2607.21130v1 Announce Type: cross Abstract: By leveraging standard RISC-V extensions, namely Zfh (scalar float16) and Zvfh (vector float16), this work proposes an open-source framework to enable
arXiv:2607.20773v1 Announce Type: cross Abstract: Large language models (LLMs) have shifted human--computer interaction from `traditional'' interface journeys toward more conversational exchanges. Res
having an event stream like activegraph is the base of how we move to next level and that will probably be composition, which can let you achieve better results with smaller models. - get the core eve