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Estimated reading time: 5 minute introduce Embodied AI agents are increasingly required to interpret complex multi-modal instructions and act firmly in dynamic environments. ThinkactProposed by researchers from NVIDIA and Taiwan University, Visual Language Action...
Recent advances Big Language Model (LLM) Encouraging the idea that having the model “think longer” during the reasoning process often improves its accuracy and robustness. Today, practices such as step-by-step explanation and increase “test...
In this tutorial, we explore the advanced capabilities of the Google Agent Development Kit (ADK) by building a multi-agent system equipped with dedicated roles and tools. We guide you to create agents tailored to...
The Visual Language Model (VLM) allows text input and visual comprehension. However, image resolution is critical to the VLM performance of processing text and chart-rich data. Increasing image resolution can pose significant challenges. First,...
Introduction: Why startups are viewing Vibe encoding Startups are under pressure to build, iterate and deploy, faster than ever before. With limited engineering resources, many are exploring an AI-powered development environment (called “Vibe encoding”),...
Large Language Models (LLMS) have recently shown significant advances in multi-step inference, establishing mathematical problem solutions as a rigorous benchmark for evaluating advanced functions. Although proprietary models such as GPT-4O and Claude Sonnet 4,...
Strengthened learning through Verified Rewards (RLVR) allows LLMS to perform complex inferences on tasks with clear, verifiable results, and has strong mathematical and coding performance. However, many real-world scenarios lack such clear verifiable answers,...
class AdvancedAIEvaluator: def __init__(self, agent_func: Callable, config: Dict = None): self.agent_func = agent_func self.results = [] self.evaluation_history = defaultdict(list) self.benchmark_cache = {} self.config = { ‘use_llm_judge’: True, ‘judge_model’: ‘gpt-4′, ’embedding_model’: ‘sentence-transformers’, ‘toxicity_threshold’: 0.7, ‘bias_categories’:...
This tutorial demonstrates how to implement self-refine technology using Mirascope’s Big Language Model (LLM), a powerful framework for building structured and timely workflows. Self-refine is a rapid engineering strategy that evaluates its own output,...
In today’s rapidly growing AI landscape, many founders and observers find themselves fully focused on successful startups that must build fundamental technologies from scratch. In the introduction of what is called “LLM wrapping paper”,...