World Wire

Understanding the Importance of Benchmarking in Tabular ML Machine learning on tabular data focuses on building models that learn patterns from structured datasets, typically composed of rows and columns similar to those found in spreadsheets. These datasets are used in industries ranging from healthcare to finance, where accuracy and interpretability are essential. Techniques such as gradient-boosted trees and neural networks are commonly used, and recent advances have introduced foundation models designed to handle tabular data structures. Ensuring fair and effective comparisons between these methods has become increasingly important as new models continue to emerge. Challenges with Existing Benchmarks One challenge in this domain is that benchmarks for evaluating models on tabular data are often outdated or flawed. Many benchmarks continue to utilize obsolete datasets with licensing issues or those that do not accurately reflect real-world tabular use cases. Furthermore, some ben...

Introduction to Ultra-Long Text Generation Challenges Generating ultra-long texts that span thousands of words is becoming increasingly important for real-world tasks such as storytelling, legal writing, and educational materials. However, large language models still face significant challenges, including length limits and quality issues, as their outputs become increasingly longer. Common problems include incoherence, topic drift, repetition, and poor structure. Earlier methods, such as LongWriter, utilize supervised fine-tuning on synthetic data to address this issue; however, this data is costly to create, difficult to generate, and often feels unnatural. Moreover, relying on existing LLMs to create training data limits creativity, and typical training methods don’t effectively improve the overall coherence or formatting of long outputs. Evolution of Long-Form Text Generation Methods Recent research into long-form text generation has focused on improving coherence, personalizatio...

Introduction to MDMs and Their Inefficiencies Masked Diffusion Models (MDMs) are powerful tools for generating discrete data, such as text or symbolic sequences, by gradually unmasking tokens over time. In each step, tokens are either masked or unmasked. However, it’s been observed that many steps in the reverse process don’t change the sequence, leading to repeated processing of identical inputs and wasted computation. Up to 37% of steps may not update the sequence at all. This inefficiency highlights a key limitation in current MDMs, prompting the development of more efficient sampling methods that minimize idle steps and maximize the utilization of each generation step. Evolution and Enhancements in MDMs The concept of discrete diffusion models originated from early work on binary data, later expanding to practical applications such as text and image generation through various noise strategies. Recent efforts have refined MDMs by simplifying training objectives and exploring alte...

Introduction to Learning-Based Robotics Robotic control systems have made significant progress through methods that replace hand-coded instructions with data-driven learning. Instead of relying on explicit programming, modern robots learn by observing actions and mimicking them. This form of learning, often grounded in behavioral cloning, enables robots to function effectively in structured environments. However, transferring these learned behaviors into dynamic, real-world scenarios remains a challenge. Robots need not only to repeat actions but also to adapt and refine their responses when facing unfamiliar tasks or environments, which is critical in achieving generalized autonomous behavior. Challenges with Traditional Behavioral Cloning One of the core limitations of robotic policy learning is the dependence on pre-collected human demonstrations. These demonstrations are used to create initial policies through supervised learning. However, when these policies fail to generalize ...

LLMs and the Need for Scientific Code Control LLMs have rapidly evolved into complex natural language processors, enabling the development of agentic systems that manage complex workflows. However, the use of LLM agents for generating scientific code is unexplored. Scientific software primarily depends on C++, CUDA, and other low-level languages, which are underrepresented in most pretraining datasets. As a result, implementations generated by LLMs contain syntactic or semantic errors, which lead to compilation issues or unstable runtime behavior. Existing agents rely heavily on user-specified control primitives and carefully crafted prompts, which are prone to misinterpretation and can lead to erratic execution flows. Limitations of Existing Steering Methods Recent approaches have been developed to tackle LLM steering challenges by uncovering causal links within model activations and facilitating precise neuron-level interventions. SFT, weight modulation techniques, and RLHF repres...

Challenges in Dexterous Hand Manipulation Data Collection Creating large-scale data for dexterous hand manipulation remains a major challenge in robotics. Although hands offer greater flexibility and richer manipulation potential than simpler tools, such as grippers, their complexity makes them difficult to control effectively. Many in the field have questioned whether dexterous hands are worth the added difficulty. The real issue, however, may be a lack of diverse, high-quality training data. Existing methods, such as human demonstrations, optimization, and reinforcement learning, offer partial solutions but have limitations. Generative models have emerged as a promising alternative; however, they often struggle with physical feasibility and tend to produce limited diversity by adhering too closely to known examples. Evolution of Dexterous Hand Manipulation Approaches Dexterous hand manipulation has long been central to robotics, initially driven by control-based techniques for pre...

Google recently released the Gemini CLI, a powerful command-line tool designed to supercharge developer workflows with AI. Whether you’re working across massive codebases, automating tedious tasks, or generating new apps from sketches and PDFs, Gemini CLI brings multimodal intelligence right to your terminal. With Gemini CLI, you can: Query and edit large codebases—even beyond the standard 1M token context window. Generate apps from visual inputs like PDFs or design sketches. Automate operational workflows—from handling pull requests to managing rebases. Connect external tools and MCP servers, including Imagen, Veo, and Lyria for media generation. Use Google Search as a grounding tool, directly within your terminal. In this tutorial, we’ll walk you through how to install, configure, and start using Gemini CLI to enhance your daily developer tasks. Installing Node JS To get started, you’ll need to have Node.js installed on your system: Go to nodejs.org and download...

The Alibaba Qwen team has introduced Qwen-VLo, a new addition to its Qwen model family, designed to unify multimodal understanding and generation within a single framework. Positioned as a powerful creative engine, Qwen-VLo enables users to generate, edit, and refine high-quality visual content from text, sketches, and commands—in multiple languages and through step-by-step scene construction. This model marks a significant leap in multimodal AI, making it highly applicable for designers, marketers, content creators, and educators. Unified Vision-Language Modeling Qwen-VLo builds on Qwen-VL, Alibaba’s earlier vision-language model, by extending it with image generation capabilities. The model integrates visual and textual modalities in both directions—it can interpret images and generate relevant textual descriptions or respond to visual prompts, while also producing visuals based on textual or sketch-based instructions. This bidirectional flow enables seamless interaction between mo...

Limitations of Reinforcement Learning in Narrow Reasoning Domains Reinforcement Learning RL has demonstrated strong potential to enhance the reasoning capabilities of LLMs, particularly in leading systems such as OpenAI-O3 and DeepSeek-R1. However, most RL research has focused narrowly on math and code, limiting its general applicability. This narrow scope poses two issues: our understanding of how RL improves reasoning may not generalize beyond these domains, and the resulting models often lack versatility. Expanding RL to broader reasoning tasks is challenging due to a lack of reliable reward signals and curated datasets, which are easier to define in mathematical and code-based terms but more difficult in open-ended reasoning domains.  Narrow Domain Focus and Generalization Challenges Reinforcement Learning RL has become a popular method for enhancing the reasoning skills of LLMs, especially after successes with models like OpenAI’s GPT-3 and DeepSeek-R1. Many open-source eff...

In this tutorial, we introduce an advanced AI agent built using Nebius’ robust ecosystem, particularly the ChatNebius, NebiusEmbeddings, and NebiusRetriever components. The agent utilizes the Llama-3.3-70B-Instruct-fast model to generate high-quality responses, incorporating external functionalities such as Wikipedia search, contextual document retrieval, and safe mathematical computation. By combining structured prompt design with LangChain’s modular framework, this tutorial demonstrates how to build a multi-functional, reasoning-capable AI assistant that is both interactive and extensible. Whether for scientific queries, technological insights, or basic numerical tasks, this agent showcases the potential of Nebius as a platform for building sophisticated AI systems. Copy Code Copied Use a different Browser !pip install -q langchain-nebius langchain-core langchain-community wikipedia import os import getpass from typing import List, Dict, Any import wikipedia from dateti...

Google has introduced Gemma 3n, a new addition to its family of open models, designed to bring large multimodal AI capabilities to edge devices. Built from the ground up with a mobile-first design philosophy, Gemma 3n can process and understand text, images, audio, and video on-device, without relying on cloud compute. This architecture represents a significant leap in the direction of privacy-preserving, real-time AI experiences across devices like smartphones, wearables, and smart cameras. Key Technical Highlights of Gemma 3n The Gemma 3n series includes two versions: Gemma 3n E2B and Gemma 3n E4B , optimized to deliver performance on par with traditional 5B and 8B parameter models respectively, while utilizing fewer resources. These models integrate architectural innovations that drastically reduce memory and power requirements, enabling high-quality inference locally on edge hardware. Multimodal Capabilities: Gemma 3n supports multimodal understanding in 35 languages, and tex...