Introduction: The Limits of Traditional AI Systems Conventional artificial intelligence systems are limited by their static architectures. These models operate within fixed, human-engineered frameworks and cannot autonomously improve after deployment. In contrast, human scientific progress is iterative and cumulative—each advancement…
Reinforcement finetuning uses reward signals to guide the large language model toward desirable behavior. This method sharpens the model’s ability to produce logical and structured outputs by reinforcing correct responses. Yet, the challenge persists in ensuring that these models also…
Text embedding and reranking are foundational to modern information retrieval systems, powering applications such as semantic search, recommendation systems, and retrieval-augmented generation (RAG). However, current approaches often face key challenges—particularly in achieving both high multilingual fidelity and task adaptability without…
AI agents powered by LLMs show great promise for handling complex business tasks, especially in areas like Customer Relationship Management (CRM). However, evaluating their real-world effectiveness is challenging due to the lack of publicly available, realistic business data. Existing benchmarks…
In this tutorial, we demonstrate how to build a multi-step, intelligent query-handling agent using LangGraph and Gemini 1.5 Flash. The core idea is to structure AI reasoning as a stateful workflow, where an incoming query is passed through a series…
Web automation agents have become a growing focus in artificial intelligence, particularly due to their ability to execute human-like actions in digital environments. These agents interact with websites via Graphical User Interfaces (GUIs), mimicking human behaviors such as clicking, typing,…
Artificial Intelligence (AI) has been making significant strides over the past few years, with the emergence of Large Language Models (LLMs) marking a major milestone in its growth. With such widespread adoption, feeling left out of this revolution is not…
Recent advances in reasoning-focused language models have marked a major change in AI by scaling test-time computation. Reinforcement learning (RL) is crucial in developing reasoning capabilities and mitigating reward hacking pitfalls. However, a fundamental debate remains: whether RL provides new…
The idea behind Agentic AI is that many small, task-focused agents can cooperate to finish real work; however, this particular idea has felt more like a promise than a product. Fortunately, the Paris-based H Company wants to change that, announcing…
Lifelong learning is crucial for intelligent agents navigating ever-changing environments, yet current LLM-based agents fall short—they lack memory and treat every task as a fresh start. While LLMs have transformed language tasks and inspired agent-based systems, these agents remain stateless…
