OpenAI’s Deep Research AI Agent offers a powerful research assistant at a premium price of $200 per month. However, the open-source community has stepped up to provide cost-effective and customizable alternatives. Here are four fully open-source AI research agents that can rival OpenAI’s offering: 1. Deep-Research Overview:Deep-Research is an iterative research agent that autonomously generates…
Large Language Models (LLMs) have demonstrated notable reasoning capabilities in mathematical problem-solving, logical inference, and programming. However, their effectiveness is often contingent on two approaches: supervised fine-tuning (SFT) with human-annotated reasoning chains and inference-time search strategies guided by external verifiers. While supervised fine-tuning offers structured reasoning, it requires significant annotation effort and is constrained by…
Reinforcement Learning RL trains agents to maximize rewards by interacting with an environment. Online RL alternates between taking actions, collecting observations and rewards, and updating policies using this experience. Model-free RL (MFRL) maps observations to actions but requires extensive data collection. Model-based RL (MBRL) mitigates this by learning a world model (WM) for planning in…
Despite recent advancements, generative video models still struggle to represent motion realistically. Many existing models focus primarily on pixel-level reconstruction, often leading to inconsistencies in motion coherence. These shortcomings manifest as unrealistic physics, missing frames, or distortions in complex motion sequences. For example, models may struggle with depicting rotational movements or dynamic actions like gymnastics…
In our previous tutorial, we built an AI agent capable of answering queries by surfing the web and added persistence to maintain state. However, in many scenarios, you may want to put a human in the loop to monitor and approve the agent’s actions. This can be easily accomplished with LangGraph. Let’s explore how this…
Graph Neural Networks (GNNs) have found applications in various domains, such as natural language processing, social network analysis, recommendation systems, etc. Due to its widespread usage, improving the defences of GNNs has emerged as a critical challenge. While exploring the mechanisms vulnerable to an attack, researchers came across Bit Flip Attacks (BFAs). Conventionally, BFAs were…
Despite progress in AI-driven human animation, existing models often face limitations in motion realism, adaptability, and scalability. Many models struggle to generate fluid body movements and rely on filtered training datasets, restricting their ability to handle varied scenarios. Facial animation has seen improvements, but full-body animations remain challenging due to inconsistencies in gesture accuracy and…
In this tutorial, we’ll walk through how to set up and perform fine-tuning on the Llama 3.2 3B Instruct model using a specially curated Python code dataset. By the end of this guide, you’ll have a better understanding of how to customize large language models for code-related tasks and practical insight into the tools and…
Vision-language models (VLMs) face a critical challenge in achieving robust generalization beyond their training data while maintaining computational resources and cost efficiency. Approaches, such as chain-of-thought supervised fine-tuning (CoT-SFT), often lead to overfitting, where models perform well on seen data but struggle with new, unseen scenarios. This limitation reduces their effectiveness in applications that demand…
Large language model (LLM) post-training focuses on refining model behavior and enhancing capabilities beyond their initial training phase. It includes supervised fine-tuning (SFT) and reinforcement learning to align models with human preferences and specific task requirements. Synthetic data is crucial, allowing researchers to evaluate and optimize post-training techniques. However, open research in this domain is…