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Showing 20 of 512 matching collected records. Text matches can include mentions by other organizations.
Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod
Learn how to run SkyRL, an open-source reinforcement learning framework, on Amazon SageMaker HyperPod to post-train a Qwen3-VL-8B vision-language model with GRPO. This walkthrough covers building the container image, launching a Ray cluster from SageMaker Studio, submitting and monitoring the job, and hosting the trained LoRA adapter for inference.
Object-Centric Low-Data Datasets for Artificial Intelligence Research: A Multi-Domain Data Resource
Research Square · Advanced Neural Network Applications · The University of Queensland; Swinburne University of Technology
Artificial Intelligence-Driven Learning Systems and Training Efficiency: Evidence from the Nigerian Banking Sector
Zenodo (CERN European Organization for Nuclear Research) · Human Resource Development and Performance Evaluation · University of Abuja
AI-Driven Cloud Cost Optimization: A Review of Machine Learning and Large Language Model Approaches and a Proposed Hybrid Architecture
Zenodo (CERN European Organization for Nuclear Research) · Cloud Computing and Resource Management
Smartphone-Based Method for Automated Speed Enforcement
Smartphone cameras and computer vision (CV) hold significant promise in assisting public agencies with enforcing traffic laws and enhancing road safety. This work designs and tests a smartphone-based method for automated speed estimation and vehicle identification (license plate, make/model, and color recognition) via an automated pipeline to assist enforcement agencies in reliably identifying speeders. The CV code accurately recognizes nearly half (46%) of the license plates' text on 1,800 images from a Brazil open-source dataset, called UFPR-ALPR. Code tests on daytime recordings from hand-h
VietPrism: A large-scale Vietnamese speech and deepfake corpus with diverse dialects and code-switching
Vietnamese speech research is constrained by resources that isolate automatic speech recognition from speaker, dialect, code-switching, and deepfake analysis. We introduce VietPrism, an open, multi-domain corpus that brings these dimensions together at scale: 993.4 hours and 403,941 bona fide utterances from 1,262 verified speakers across 8,388 real-world videos. To our knowledge, it is the first large-scale Vietnamese corpus to jointly provide transcripts, consistent speaker identities, five dialect groups, and naturally occurring Vietnamese--English code-switching, which constitutes nearly h
ChunkRank: Model-Aware Text Chunking and Abstention-Aware Answer Selection for LLM Pipelines
We present ChunkRank, an open-source Python library that derives chunk boundaries from a target model's tokenizer and context window, and selects an answer among candidates produced independently per chunk. It ships a validated registry of 90 models across 15 providers and six answer-selection methods, and needs only three core dependencies. For chunking, ChunkRank avoids context-window overflow automatically from the model name, whereas character-based splitters overflow or waste the budget, and a fidelity study across 11 languages shows why token-exact budgets matter beyond English. For answ
PolyUMI: Accessible Visual-Tactile-Audio Data Collection for Object Inference and Manipulation
Humans typically rely on vision, touch, hearing, and proprioception to perceive contact and adapt their actions during manipulation. Providing robots with comparable responsiveness therefore requires hardware that can retain and use these complementary sensory signals. Most imitation-learning systems, however, observe demonstrations primarily through vision and proprioception, limiting access to contact information that is difficult to infer visually. We present PolyUMI, an open-source platform for scalable visual--tactile--audio demonstration collection and robot deployment. Its lightweight,
CodeGraph: Open-Taxonomy Knowledge Graph for Source Code with Wikidata Grounding
Public software repositories, like GitHub and Software Heritage Archive, store billions of files, yet extracting their implicit engineering knowledge ---i.e., the algorithms they implement, the paradigms they follow, the patterns they instantiate, and the application domains they serve--- remains challenging, as current tools are constrained to syntactic and token-level analysis. We present a pipeline for building an open-taxonomy semantic annotation of source code using a code-specialised Large Language Model. The extracted entities are grounded in Wikidata through a three-stage linking proce
ENSEMBLE MACHINE LEARNING SYSTEM FOR DYNAMIC RESOURCE ALLOCATION USING CROSS-SOURCE DATA AGGREGATION
Patent publication US20260289661A1 · Applicant: FUNDBOX LTD. Bibliographic metadata from the European Patent Office.
CVE-2026-61732: Decepticon is an autonomous hacking agent for red teams. Versions prior to 1.1.17 wrap web crawl results — the output of agent reconnaissance against target services — into LLM messages without neutralizing ChatML specia
Decepticon is an autonomous hacking agent for red teams. Versions prior to 1.1.17 wrap web crawl results — the output of agent reconnaissance against target services — into LLM messages without neutralizing ChatML special-token literals. Under the BYOK (Bring Your Own Key) deployment model, users configure their own LLM credentials to any OpenAI-compatible endpoint. Most open-source and self-deployed model providers (vLLM, SGLang, Ollama, LM Studio, text-generation-webui, etc.) do not filter special-token literals from user content in their default configurations. Those literals are parsed int
ON-DEVICE CONVERSION OF MACHINE GENERATED DATA USING IDLE RESOURCES OF NEURAL PROCESSING UNITS
Patent publication US20260289287A1 · Applicant: DELL PRODUCTS L P. Bibliographic metadata from the European Patent Office.
ggml-org/llama.cpp: v0.5.0
## Overview This release focuses on backend performance and correctness, broader model coverage, and more robust server/router operation. It adds HRM-Text (DFM Mimir 1B) support, MiMo-V2.6 and HunyuanOCR conversion support, ggml 0.25.0 backend improvements, multi-address HTTP binding, image outputs from function calls, and several chat parser/UI fixes. ### Highlights - Accelerate CUDA `conv2d` with implicit GEMM ([#29135](https://github.com/ggml-org/llama.cpp/pull/29135)) - Add Metal MoE and SSM_CONV fusion optimizations ([#28948](https://github.com/ggml-org/llama.cpp/pull/28948)) - Allow the server to bind to multiple addresses ([#28690](https://github.com/ggml-org/llama.cpp/pull/28690)) ### API changes - Add `llama_adapter_lora_init_from_file_ptr()` for loading LoRA from an open FILE ([#28993](https://github.com/ggml-org/llama.cpp/pull/28993)) - Document `llama_model_load_from_file_ptr()` as reading from the current position and requiring aligned mmap ([#28993](https://github.com/ggm
Use open weight models as your AI coding agent with Amazon Bedrock
Pair OpenCode, an open-source terminal-native AI coding agent, with open weight models on Amazon Bedrock to get a secure, flexible, pay-per-use coding assistant. Learn how to configure multi-model workflows, match the right model to each task, and keep your data in your own AWS account with no infrastructure to manage.
hyperbolix: Hyperbolic Deep Learning in JAX
We present hyperbolix, an open-source library for hyperbolic deep learning in JAX, built on Flax NNX. To our knowledge, it is the first comprehensive, general-purpose hyperbolic deep learning library in JAX. It includes six manifolds with a common interface: Euclidean space, the Poincaré ball, the hyperboloid, the $κ$-stereographic model, mixed-curvature product spaces, and the proper velocity space. We implement layer families that cover linear layers, convolutions, attention, normalization, positional encoding, regression, and vector quantization. These building blocks span methods ranging f
Exact Feedback Is Not Control: Evaluating Text-based Closed-Loop Revision in LLMs
Closed-loop revision is increasingly used in large language model (LLM) applications, but failures may reflect incomplete feedback or ineffective responses to correct feedback. We introduce a fixed-budget revision protocol with deterministic verifiers that report all remaining violations across exact-length, lexical, and compositional constraints. Fixing feedback correctness and completeness isolates model-side revision behavior. Across 19 open- and closed-source models, controller-level mean final joint success ranges from 17.4% to 99.8%, with substantial cross-model gaps persisting under ide
Evaluating Open-Weight LLMs for Turkish Domain Documents Under Retrieval and Hardware Constraints
Most Turkish-capable large language models (LLMs) are evaluated using general-purpose benchmarks rather than long, structurally complex domain documents. This paper evaluates five open-weight 7B-8B models for Turkish document question answering under a resource-constrained local deployment setting. The primary benchmark contains 100 systematically validated questions derived from a 109-page industrial R&D report, and the evaluation protocol is replicated using a second 112-page public-sector report and an independently constructed 100-question set. All models are evaluated locally on an NVIDIA
When Context Misleads: In-context Learning with Jurisdiction in Large Language Models
In-Context Learning (ICL) has become a cornerstone of modern LLM deployment. However, existing ICL post-training methods have a critical blind spot: they excel at extracting patterns from demonstrations while often neglecting context authority, the ability to determine whether contextual information should govern the final answer. To benchmark this capability, we introduce FakeContextBench, which contains pseudoscientific claims across seven domains. Our evaluation of commercial and open-source models shows that large-scale pre-training alone is insufficient for reliable context-authority disc
TNLearn: An Open Source Python Package for Task-based Neurons
The brain does not rely on a single type of neuron to perform all kinds of tasks; instead, it designs different neurons for different tasks. The concept of task-based neurons represents a paradigm shift compared to task-based architectures. It argues that solving a specific problem requires customized neurons, as task-based neurons capture useful prior knowledge from task-related data. To facilitate the use of task-based neurons in scientific research and industrial applications, we introduce TNLearn, an open-source Python package that provides automated construction of task-based neurons and
EviStreams: Human-in-the-Loop AI Data Extraction for Systematic Reviews in Medicine
Systematic reviews underpin clinical guidelines, yet their data-extraction step is a major expert-labor bottleneck bound by a protocolized workflow: two reviewers extract each study independently, an adjudicator resolves disagreements, and the team keeps an auditable record of how every value was produced. Large language models can assist with extraction, but that assistance must fit established review protocols and preserve reproducibility. We present EviStreams, a live, open-source, no-code web platform that puts review teams in control of AI-assisted extraction at three key stages: program