Action AI's

Large Action Models or VLAs)
Large Action Models (LAMs)
These extend traditional LLM functionality by interpreting language and executing real-world tasks—from database updates to robotic motions—rather than merely responding with text 

Vision‑Language‑Action Models (VLAs)
VLAs integrate visual input, natural language, and action control.

Examples include: RT‑2 by DeepMind: Processes camera images and instructions to output robot actions in one go 

OpenVLA, Octo, TinyVLA, π₀ (pi‑zero), Helix, GR00T N1, Gemini Robotics, and SmolVLA: Each represents an advancement in robotics control—from compact, open-source models to sophisticated humanoid systems with real-time, high-frequency 

Manus
Developed by Butterfly Effect (released March 6, 2025), Manus is one of the first AI agents capable of independently handling complex real-world tasks—from writing and deploying code to dynamic planning—without continuous human guidance 

AlphaEvolve
A Gemini-powered, evolutionary coding agent (May 2025) that autonomously generates and optimizes algorithms via iterative refinement—discovering novel solutions and improving efficiency with minimal human input Wikipedia.
Agentic Reasoning and Planning Integrations
Some models blend LLM reasoning with structured planning:

LLM+AL: Combines natural language understanding with symbolic action reasoning, improving performance on complex planning tasks compared to standard LLMs

SMART‑LLM: Uses LLMs to break down instructions into tasks, allocate them across agents, and coordinate complex multi-robot plans arXiv.

SwiftSage: Inspired by human cognition, this framework integrates a fast, behavior-based module with a slower, planning-oriented LLM (like GPT‑4) for robust interactive task execution arXiv.
 
Summary Paragraph
Recent advancements in AI have moved beyond traditional language models to action-capable systems—often called Large Action Models (LAMs). These models can interpret instructions and directly execute tasks, whether digital or physical. Vision-Language-Action models (VLAs) like DeepMind’s RT-2, OpenVLA, π₀, Helix, and Gemini Robotics enable robots to process visual inputs and text commands to perform real-world actions. Autonomous agents such as Manus can independently manage complex real-world tasks, including code deployment. In the realm of algorithm design, AlphaEvolve autonomously improves and invents code through evolutionary search. Other frameworks—LLM+AL, SMART-LLM, and SwiftSage—enhance LLMs with symbolic planning, multi-agent coordination, and dual-module reasoning to excel at structured and interactive tasks. Together, these systems illustrate the shift from passive language understanding to active, intelligent automation.