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  • آخر تحديث: 13 نوفمبر 2026 – 07:45
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Skill Adapter: Personalized Modification and Online Routing of General-Purpose Skills (يفتح في تبويب جديد)

arXiv – Large Language Models
المؤلفون
Qijia Zhuang، Yanyi Gou، Wenbin Ma، Yuxiang Ren
المجال
cs.AI
ملخص المصدر

Large language model (LLM) agents use external skills to reuse task procedures and experience with tools. General-purpose skills describe shared execution procedures, while specific tool choices, operating practices, and output requirements vary across users. In long-horizon tasks, subgoals, available inputs, and environmental states change as execution progresses. Agents must select currently applicable skills from…

Build composable, interoperable multi-agent systems with A2A on AWS (يفتح في تبويب جديد)

AWS Machine Learning Blog
الكاتب
Amit Chaudhary
ملخص المصدر

Build a composable, interoperable multi-agent system on AWS with the Agent-to-Agent (A2A) protocol. A LangChain orchestrator on Amazon ECS communicates with a Strands agent on Amazon Bedrock AgentCore across organizational boundaries, using bidirectional, OAuth-authenticated calls, with neither side depending on the other's framework or SDK.

Fine-grained access control for Amazon Bedrock Managed Knowledge Base tools (يفتح في تبويب جديد)

AWS Machine Learning Blog
الكاتب
Renya Kujirada
ملخص المصدر

Learn how to expose an Amazon Bedrock Managed Knowledge Base to AI agents as MCP tools through Amazon Bedrock AgentCore Gateway while keeping per-user access control. A request interceptor injects verified identity, Policy in AgentCore validates it independently, and a resource-based policy restricts direct access to the knowledge base.

From prototype to production: Engineering agents with Agent DLC (يفتح في تبويب جديد)

AWS Machine Learning Blog
الكاتب
Yuan Wei
ملخص المصدر

Agents stall between demo and release when teams lack a repeatable way to measure how good an agent is and whether the last change helped. This post covers three agent properties that break traditional CI/CD, a measured case where an aggregate metric hides a coverage gap, and Agent DLC: one lifecycle, two loops, and one explicit standard for judging every change.

IsharaBART: An efficient BART-Based Framework for Low-Resource Bangla Sign Language Translation (يفتح في تبويب جديد)

arXiv – NLP
المؤلفون
Aonmoy Das، Ananna Dev Aishi، Md. Fazlul Kader
المجال
cs.CL
ملخص المصدر

Millions of Bangla Sign Language (BdSL) users lack reliable automatic translation tools. Existing systems are limited by data scarcity, unidirectional design, and computationally expensive architectures that are not suitable for low-resource deployment. We introduce IsharaBART, a bidirectional generative framework for gloss-sentence translation in BdSL. IsharaBART uses Bidirectional and Auto-Regressive Transformers…

Distribution-First Framework for Learning Risk-Sensitive Individualized Treatment Rules (يفتح في تبويب جديد)

arXiv – Medical AI
المؤلفون
Zhen Fang، Guanting Chen، Yufeng Liu
المجال
stat.ML
ملخص المصدر

Learning a risk-sensitive individualized treatment rule (ITR) with patient covariates is challenging when the treatment criterion depends on the full conditional outcome distribution rather than only its conditional mean. Existing methods are largely criterion-specific, making it difficult to compare multiple treatment criteria within a common pipeline, such as quantiles, conditional value-at-risk (CVaR), and…

Build autonomous AI agents with Amazon Bedrock and Spring AI (يفتح في تبويب جديد)

AWS Machine Learning Blog
الكاتب
Viquar Khan
ملخص المصدر

Learn how Java developers can build production-ready autonomous AI agents using Amazon Bedrock and Spring AI. This post walks through tool calling, conversation memory, resilience patterns, and multi-agent orchestration, then deploys the agent to AWS with Amazon Bedrock AgentCore.

OpenAI Codex on Amazon Bedrock (يفتح في تبويب جديد)

AWS Machine Learning Blog
الكاتب
Shivi Bhatia
ملخص المصدر

A practical guide for enterprises rolling out OpenAI Codex to developers on AWS, with Amazon Bedrock as the model backend, covering the access, governance, and cost decisions that platform, security, and finance teams need to align on.

Detecting and recovering from silent data corruption on Amazon SageMaker HyperPod (يفتح في تبويب جديد)

AWS Machine Learning Blog
الكاتب
Siddharth Senger
ملخص المصدر

Silent data corruption is a GPU fault that returns wrong but plausible numbers with no error, quietly poisoning distributed training. This post adds three checks to a PyTorch DDP loop to detect it, localize the faulty node on Amazon SageMaker HyperPod, and hand the node to automatic recovery for replacement.

Introducing the Web Search API on Amazon Bedrock AgentCore (يفتح في تبويب جديد)

AWS Machine Learning Blog
الكاتب
Shubham Divekar
ملخص المصدر

Today we're announcing the Web Search API on Amazon Bedrock AgentCore: your own code can search the web with a single signed API call, with no gateway and no resources to create. Call it from the AWS SDKs or the AWS CLI and use it in applications, agent loops, and data pipelines.

ICYMI: What landed for AI builders in October 2026 (يفتح في تبويب جديد)

AWS Machine Learning Blog
الكاتب
Tymofii Kryvtsun
ملخص المصدر

A recap of the major Amazon Bedrock, Amazon Bedrock AgentCore, and Strands updates from October 2026: new models to match each task, updates that keep agents available and grounded, and clearer cost reporting for AI builders.

The attendee’s guide to the AWS re:Invent 2026 AI/ML track (يفتح في تبويب جديد)

AWS Machine Learning Blog
الكاتب
Markus Adhiwiyogo
ملخص المصدر

AWS re:Invent 2026 includes more than 140 AI/ML track sessions. This attendee's guide helps you find the breakouts, chalk talks, workshops, and builders' sessions that fit your role and goals, spanning AI chips, models and inference, agents, agent trust, and sustainability, so you can build a smarter re:Invent schedule.

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Back to Pixels: Lifting Latent Video Diffusion into Pixel Space (يفتح في تبويب جديد)

arXiv – Computer Vision
المؤلفون
Yanke Song، Shuo Chen، Chris Rockwell، Guande He، Nan Liu، Desai Xie، Tianpei Gu، Yong-Hyun Park، Aryan V S، Dennis Yuren Menn، Ming-Hsuan Yang، Xun Huang
المجال
cs.CV
ملخص المصدر

Frontier video generation models work in a VAE latent space that compresses video in both space and time. This compression made training tractable, but now limits high-resolution, controllable, and interactive generation -- doubling resolution quadruples token count, conditions apply to groups of frames rather than individual ones, and each response must wait for the rest of its multi-frame block. We present PixVid…

Unbundling Multimodal Vision Encoding (يفتح في تبويب جديد)

arXiv – Computer Vision
المؤلفون
Ellis Brown، Chris Hoang، Shengyi Qian، Shang-Wen Li، Mike Lewis، Marjan Ghazvininejad، Luke Zettlemoyer، Lucas Beyer، Mengye Ren، Jakob Verbeek، Saining Xie، Hu Xu
المجال
cs.CV
ملخص المصدر

How multimodal language models should encode visual input remains up for debate. Most models use a pretrained vision encoder, while some recent recipes train the encoder from scratch with the language model or remove it entirely. Vision encoders bundle together three design choices: a visual prior from upstream pretraining, dedicated vision capacity, and pre-fusion placement of that capacity (i.e., before the…

ArchSpace: Recording, Reviewing, and Referencing LLM Architecture Research (يفتح في تبويب جديد)

arXiv – Machine Learning
المؤلفون
Jie Wang، Tong Zhu، JiaHao Kuang، Zhe Xu، Yunkai Chai، Dehong Zheng، Yu Cheng، Tao Ji، ZhouHan Lin، Qi Zhang، Bowen Zhou
المجال
cs.CL
ملخص المصدر

Model architecture shapes the scaling behavior, training efficiency, and inference cost of LLMs, making it a central focus of both industry and academia. In industry, teams such as those behind DeepSeek, Qwen, and Kimi routinely introduce architectural innovations, yet public reports rarely document which ideas succeeded, which were abandoned, and why. In academia, limited compute and inconsistent baselines often…

TTB: Test-time Baking for Efficient Rendering of Decoder-only View Synthesis Models (يفتح في تبويب جديد)

arXiv – Computer Vision
المؤلفون
Chin-Yang Lin، Ryo Hachiuma، Jaesung Choe، Min-Hung Chen، Yu-Chiang Frank Wang، Yen-Yu Lin، Wei-Chen Chiu، Yu-Lun Liu، Cheng Sun
المجال
cs.CV
ملخص المصدر

Decoder-only Large View Synthesis Models (LVSMs) with KV-cache have recently achieved state-of-the-art quality by regressing novel views from neural networks without reconstructing 3D geometry, while Test-Time Training (TTT) layers save computation but sacrifice quality. We propose Test-Time Baking (TTB), a strategy that learns to bake global scene information from cross-view attention into a lightweight MLP…

ProtoVis-Nav: Prototypical Visual Imagination for Visual-Spatial Aligned UAV Navigation (يفتح في تبويب جديد)

arXiv – Computer Vision
المؤلفون
Guangyuan Sui، Yunzhong Hou، Chi Harold Liu
المجال
cs.RO
ملخص المصدر

Vision language navigation (VLN) is very challenging for Unmanned Aerial Vehicles (UAVs) since small errors in 3D motion prediction can lead to navigation errors and collisions. Unable to fathom execution outcomes in 3D environments, existing methods often struggle to predict accurate navigation waypoints from language instructions. To solve this, we introduce the Prototypical Visual Imagination framework, shortened…

Internalizing Investment Mandates: A Neurosymbolic RL Approach to Portfolio Allocation (يفتح في تبويب جديد)

arXiv – Machine Learning
المؤلفون
Zeynep Yıldız، Satchit Chatterji، Ercan Atam، Erman Acar
المجال
cs.LG
ملخص المصدر

Institutional portfolios operate under investment policy statements that specify asset allocation constraints, often conditioned on market regime information. Deep reinforcement learning has been shown to optimize financial performance but typically does not enforce rule compliance, making it unsuitable for real-world deployment. Existing neurosymbolic methods such as Probabilistic Logic Policy Gradient (PLPG) offer…

النماذج الجديدة

نماذج من Hugging Face مع الجهة المطوِّرة والمهمة.

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أدوات الذكاء الاصطناعي

مستودعات وإصدارات مفتوحة المصدر من GitHub.

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EllE961/marja (يفتح في تبويب جديد)

GitHub – مشاريع NLP عربية
الجهة المطوِّرة
EllE961
اللغة البرمجية
Python
ملخص المصدر

Arabic-aware RAG over a real, public SQU document: from-scratch normalisation + Light10 light stemming, measured against naive token matching with the same BM25 index -- 0.792 vs 0.048 recall@5. Animated web UI recorded live.

abdallahelarabyy/bringing-arabic-historical-characters-back-to-life (يفتح في تبويب جديد)

GitHub – مشاريع NLP عربية
الجهة المطوِّرة
abdallahelarabyy
اللغة البرمجية
Python
ملخص المصدر

Chat in Arabic with Salah al-Din and Muhammad Ali Pasha as 3D talking avatars: RAG over Wikipedia, free Groq LLM, Edge TTS and Arabic viseme lip sync (FastAPI + Docker).

langchain-typesafe==0.1.0 (يفتح في تبويب جديد)

GitHub Releases – langchain-ai/langchain
الجهة المطوِّرة
langchain-ai
ملخص المصدر

langchain-typesafe 0.1.0 drops support for Python 3.10, which has reached end-of-life. The minimum supported version is now Python 3.11, and the minimum langchain-core version is now 1.8.0 (langchain 1.5.0 for the experimental extra). Users on Python 3.10 will continue to resolve langchain-typesafe 0.0.x. Changes since langchain-typesafe==0.0.2 release(typesafe): 0.1.0 (#41800) refactor(typesafe): deprecate…

langchain==1.5.0 (يفتح في تبويب جديد)

GitHub Releases – langchain-ai/langchain
الجهة المطوِّرة
langchain-ai
ملخص المصدر

langchain 1.5.0 drops support for Python 3.10, which has reached end-of-life. The minimum supported version is now Python 3.11, and the minimum langchain-core version is now 1.8.0. Users on Python 3.10 will continue to resolve langchain 1.4.x. Changes since langchain==1.4.10 release(langchain): 1.5.0 (#41798) fix(langchain): mark ModelRouterMiddleware as beta (#41797) feat(langchain): Remove EOL'd Python 3.10 from…

langchain-core==1.8.0 (يفتح في تبويب جديد)

GitHub Releases – langchain-ai/langchain
الجهة المطوِّرة
langchain-ai
ملخص المصدر

langchain-core 1.8.0 drops support for Python 3.10, which has reached end-of-life. The minimum supported version is now Python 3.11. Users on Python 3.10 will continue to resolve langchain-core 1.7.x. Changes since langchain-core==1.7.7 release(core): 1.8.0 (#41796) feat(core): Remove EOL Python 3.10 support (#41267) feat(core): add decision models (#41765)