A ransomware attack hit Oltenia Energy Complex (Complexul Energetic Oltenia), Romania’s largest coal-based energy producer, on the second day of Christmas, taking down its IT infrastructure. […] Read More
A former Coinbase customer service agent was arrested in India for helping hackers earlier this year steal sensitive customer information from a company database. […] Read More
Fortinet has warned customers that threat actors are still actively exploiting a critical FortiOS vulnerability that allows them to bypass two-factor authentication (2FA) when targeting vulnerable FortiGate firewalls. […] Read More
How to Facilitate Effective AI ProgrammingTowards Data Science How to ensure your coding agent has the same context as you
The post How to Facilitate Effective AI Programming appeared first on Towards Data Science.
How to ensure your coding agent has the same context as you
The post How to Facilitate Effective AI Programming appeared first on Towards Data Science. Read More
Machine Learning vs AI Engineer: What Are the Differences?Towards Data Science One of the most confusing questions in tech right now is: What is the difference between an AI engineer and a machine learning engineer? Both are six-figure jobs, but if you choose the wrong one, you could waste months of your career learning the wrong skills and miss out on quality roles. As a practising
The post Machine Learning vs AI Engineer: What Are the Differences? appeared first on Towards Data Science.
One of the most confusing questions in tech right now is: What is the difference between an AI engineer and a machine learning engineer? Both are six-figure jobs, but if you choose the wrong one, you could waste months of your career learning the wrong skills and miss out on quality roles. As a practising
The post Machine Learning vs AI Engineer: What Are the Differences? appeared first on Towards Data Science. Read More
The Best Agentic AI Browsers to Look For in 2026KDnuggets A quick look at the top 7 agentic AI browsers that can search the web for you, fill forms automatically, handle research, draft content, and streamline your entire workflow.
A quick look at the top 7 agentic AI browsers that can search the web for you, fill forms automatically, handle research, draft content, and streamline your entire workflow. Read More
Implementing Vibe Proving with Reinforcement LearningTowards Data Science How to make LLMs reason with verifiable, step-by-step logic (Part 2)
The post Implementing Vibe Proving with Reinforcement Learning appeared first on Towards Data Science.
How to make LLMs reason with verifiable, step-by-step logic (Part 2)
The post Implementing Vibe Proving with Reinforcement Learning appeared first on Towards Data Science. Read More
How to Build Contract-First Agentic Decision Systems with PydanticAI for Risk-Aware, Policy-Compliant Enterprise AIMarkTechPost In this tutorial, we demonstrate how to design a contract-first agentic decision system using PydanticAI, treating structured schemas as non-negotiable governance contracts rather than optional output formats. We show how we define a strict decision model that encodes policy compliance, risk assessment, confidence calibration, and actionable next steps directly into the agent’s output schema. By
The post How to Build Contract-First Agentic Decision Systems with PydanticAI for Risk-Aware, Policy-Compliant Enterprise AI appeared first on MarkTechPost.
In this tutorial, we demonstrate how to design a contract-first agentic decision system using PydanticAI, treating structured schemas as non-negotiable governance contracts rather than optional output formats. We show how we define a strict decision model that encodes policy compliance, risk assessment, confidence calibration, and actionable next steps directly into the agent’s output schema. By
The post How to Build Contract-First Agentic Decision Systems with PydanticAI for Risk-Aware, Policy-Compliant Enterprise AI appeared first on MarkTechPost. Read More
LongFly: Long-Horizon UAV Vision-and-Language Navigation with Spatiotemporal Context Integrationcs.AI updates on arXiv.org arXiv:2512.22010v1 Announce Type: cross
Abstract: Unmanned aerial vehicles (UAVs) are crucial tools for post-disaster search and rescue, facing challenges such as high information density, rapid changes in viewpoint, and dynamic structures, especially in long-horizon navigation. However, current UAV vision-and-language navigation(VLN) methods struggle to model long-horizon spatiotemporal context in complex environments, resulting in inaccurate semantic alignment and unstable path planning. To this end, we propose LongFly, a spatiotemporal context modeling framework for long-horizon UAV VLN. LongFly proposes a history-aware spatiotemporal modeling strategy that transforms fragmented and redundant historical data into structured, compact, and expressive representations. First, we propose the slot-based historical image compression module, which dynamically distills multi-view historical observations into fixed-length contextual representations. Then, the spatiotemporal trajectory encoding module is introduced to capture the temporal dynamics and spatial structure of UAV trajectories. Finally, to integrate existing spatiotemporal context with current observations, we design the prompt-guided multimodal integration module to support time-based reasoning and robust waypoint prediction. Experimental results demonstrate that LongFly outperforms state-of-the-art UAV VLN baselines by 7.89% in success rate and 6.33% in success weighted by path length, consistently across both seen and unseen environments.
arXiv:2512.22010v1 Announce Type: cross
Abstract: Unmanned aerial vehicles (UAVs) are crucial tools for post-disaster search and rescue, facing challenges such as high information density, rapid changes in viewpoint, and dynamic structures, especially in long-horizon navigation. However, current UAV vision-and-language navigation(VLN) methods struggle to model long-horizon spatiotemporal context in complex environments, resulting in inaccurate semantic alignment and unstable path planning. To this end, we propose LongFly, a spatiotemporal context modeling framework for long-horizon UAV VLN. LongFly proposes a history-aware spatiotemporal modeling strategy that transforms fragmented and redundant historical data into structured, compact, and expressive representations. First, we propose the slot-based historical image compression module, which dynamically distills multi-view historical observations into fixed-length contextual representations. Then, the spatiotemporal trajectory encoding module is introduced to capture the temporal dynamics and spatial structure of UAV trajectories. Finally, to integrate existing spatiotemporal context with current observations, we design the prompt-guided multimodal integration module to support time-based reasoning and robust waypoint prediction. Experimental results demonstrate that LongFly outperforms state-of-the-art UAV VLN baselines by 7.89% in success rate and 6.33% in success weighted by path length, consistently across both seen and unseen environments. Read More
NEMO-4-PAYPAL: Leveraging NVIDIA’s Nemo Framework for empowering PayPal’s Commerce Agentcs.AI updates on arXiv.org arXiv:2512.21578v1 Announce Type: new
Abstract: We present the development and optimization of PayPal’s Commerce Agent, powered by NEMO-4-PAYPAL, a multi-agent system designed to revolutionize agentic commerce on the PayPal platform. Through our strategic partnership with NVIDIA, we leveraged the NeMo Framework for LLM model fine-tuning to enhance agent performance. Specifically, we optimized the Search and Discovery agent by replacing our base model with a fine-tuned Nemotron small language model (SLM).
We conducted comprehensive experiments using the llama3.1-nemotron-nano-8B-v1 architecture, training LoRA-based models through systematic hyperparameter sweeps across learning rates, optimizers (Adam, AdamW), cosine annealing schedules, and LoRA ranks. Our contributions include: (1) the first application of NVIDIA’s NeMo Framework to commerce-specific agent optimization, (2) LLM powered fine-tuning strategy for retrieval-focused commerce tasks, (3) demonstration of significant improvements in latency and cost while maintaining agent quality, and (4) a scalable framework for multi-agent system optimization in production e-commerce environments. Our results demonstrate that the fine-tuned Nemotron SLM effectively resolves the key performance issue in the retrieval component, which represents over 50% of total agent response time, while maintaining or enhancing overall system performance.
arXiv:2512.21578v1 Announce Type: new
Abstract: We present the development and optimization of PayPal’s Commerce Agent, powered by NEMO-4-PAYPAL, a multi-agent system designed to revolutionize agentic commerce on the PayPal platform. Through our strategic partnership with NVIDIA, we leveraged the NeMo Framework for LLM model fine-tuning to enhance agent performance. Specifically, we optimized the Search and Discovery agent by replacing our base model with a fine-tuned Nemotron small language model (SLM).
We conducted comprehensive experiments using the llama3.1-nemotron-nano-8B-v1 architecture, training LoRA-based models through systematic hyperparameter sweeps across learning rates, optimizers (Adam, AdamW), cosine annealing schedules, and LoRA ranks. Our contributions include: (1) the first application of NVIDIA’s NeMo Framework to commerce-specific agent optimization, (2) LLM powered fine-tuning strategy for retrieval-focused commerce tasks, (3) demonstration of significant improvements in latency and cost while maintaining agent quality, and (4) a scalable framework for multi-agent system optimization in production e-commerce environments. Our results demonstrate that the fine-tuned Nemotron SLM effectively resolves the key performance issue in the retrieval component, which represents over 50% of total agent response time, while maintaining or enhancing overall system performance. Read More
