If you want to learn about the Agentic Web, look no further.
This new report is a banger!
It presents a detailed framework to understand and build the agentic web.
Here is everything you need to know:
Agentic Web
This paper introduces the concept of the Agentic Web, a transformative vision of the internet where autonomous AI agents, powered by LLMs, act on behalf of users to plan, coordinate, and execute tasks.
It proposes a structured framework for understanding this shift, situating it as a successor to the PC and Mobile Web eras.
It's defined by a triplet of core dimensions (intelligence, interaction, and economics) and involves fundamental architectural and commercial transitions.
Introduces a novel RAG framework that moves beyond traditional one-shot or chunk-based retrieval by integrating graph-structured knowledge, agentic multi-turn interaction, and RL.
Graph-R1 is an agent that reasons over a knowledge hypergraph environment by iteratively issuing queries and retrieving subgraphs using a multi-step “think-retrieve-rethink-generate” loop.
Unlike prior GraphRAG systems that perform fixed retrieval, Graph-R1 dynamically explores the graph based on evolving agent state.
> MoE Architecture
> Hybrid reasoning models
> 355B total (32B active)
> GQA + partial RoPE
> Multi-Token Prediction
> Muon Optimizer + QK-Norm
> 22T-token training corpus
> Slime RL Infrastructure
> Native tool use
Here's all you need to know:
Model Architecture & Pre-Training
GLM-4.5 is 355B total parameters (32B active); deeper model with narrower width; optimized for reasoning via more layers and 96 attention heads.
GLM-4.5-Air is 106B (12B active).
22T-token training corpus that combines 15T general data with 7T code/reasoning-focused data.
Grouped-Query Attention + partial RoPE to enhance long-context efficiency and accuracy in reasoning tasks.
Mid-training looks like a key part of this model
"Unlike the earlier pre-training stage on large-scale universal documents, these stages leverage medium-sized domain-specific datasets, including instruction data."