Architecture and Engineering Practices for Ultra-Large MoE Models and Long-Horizon Agents

In recent years, as Large Language Models (LLMs) have evolved from simple single-turn dialogues to complex task orchestration, long-horizon Agent capabilities have become a core metric for evaluating the strength of foundational models. Recently, a representative ultra-large Mixture-of-Experts (MoE) architecture model with open weights was released, featuring a total parameter count of 2.8T and activated parameters of 104B. Based on a previous foundational model, it underwent large-scale joint reinforcement learning (RL) post-training and supports a 1 million Token context window alongside native image-text inputs. This article decouples specific brand attributes to deeply analyze the technical evolution and deployment standards of such long-horizon Agent models across four dimensions: underlying architecture, capability boundaries, core algorithms, and engineering practices.

1. Core Architecture and Multimodal Processing Capabilities

1.1 Efficiency Balance in Ultra-Large MoE Architecture
In the pursuit of ultimate reasoning capabilities, the expansion of model parameters is inevitable. A scale of 2.8T total parameters places extremely high demands on GPU memory capacity. However, through the MoE architecture, the model only activates approximately 104B parameters during each inference step. This sparse activation mechanism allows the model to effectively control the computational complexity during actual inference without sacrificing representational power. For enterprise deployment, this means that ultra-large models can be run on limited compute clusters using memory optimization techniques (such as low-bit weight quantization), achieving a balance between compute costs and the model’s intelligence level.

1.2 Million-Token Context and Native Multimodal Input
The core challenges of long-horizon Agents are “memory forgetting” and “context fragmentation.” This architecture supports a 1 million Token long-context window, enabling it to fully ingest extensive codebases, multi-turn complex tool calling logs, or detailed financial reports. Simultaneously, the model supports native image-text input. Without relying on external visual middleware for feature conversion, it directly maps images and text into a unified high-dimensional semantic space. This feature significantly improves the model’s accuracy in scenarios such as UI interface understanding, chart data analysis, and 3D design drawing recognition.

2. Long-Horizon Agent Application Scenarios and Capability Boundaries

Based on its robust foundational capabilities, the model has demonstrated significant performance improvements in multiple complex workflows, primarily focusing on the following two core areas:

2.1 Code Development and 3D Creation Orchestration
Modern software engineering has shifted from single-file writing to full-repository-level code understanding and repair. Long-horizon Agents can take over multi-step tasks such as terminal operations, code reviews, bug fixing loops, and CAD model generation. In terminal operations, the Agent needs to understand command-line outputs, plan execution paths, and self-correct upon failure. In 3D creation, the Agent can combine external CAD tool APIs to progressively translate natural language descriptions into precise engineering drawing instructions. This end-to-end multi-step task orchestration relies on the model’s deep understanding of complex logic and long-horizon planning capabilities.

2.2 Knowledge Work and Dynamic Tool Calling
In the broader knowledge work domain, the model supports deep web search, financial data research, office document parsing, and dynamic tool calling. To balance inference speed and accuracy, the model offers three levels of thinking budget (low, high, max). Developers can dynamically allocate compute based on task complexity: the low tier can be used for simple document format conversions, while the max tier is enabled for financial research report analysis requiring multiple cross-validations to ensure the rigor of the reasoning chain. In certain high-diffustry industry evaluations (such as deep software engineering, terminal operations, and complex CAD generation), enabling the max thinking tier alongside common intelligent programming frameworks can achieve extremely high task completion rates.

3. Underlying Optimization: Sparse Attention and Joint Reinforcement Learning

3.1 Evolution of Attention Mechanisms
The bottleneck in processing a million-Token context lies in the quadratic complexity of attention computation. Compared to previous architectures, the new version has deeply restructured the attention layers: replacing the original 24 layers of Gated MLA (Gated Multi-head Latent Attention) with Gated DSA (Gated Sparse Attention), and combining it with 69 layers of Delta Attention to construct a hybrid attention layer architecture. Among them, the DSA mechanism dynamically selects only the 2,048 most relevant historical Tokens to participate in attention computation for each step. This sparse routing mechanism significantly reduces the memory footprint of long-sequence computation while preserving lossless transmission of critical context information through the gating mechanism.

3.2 Large-Scale Joint RL Post-Training
The leap in model capabilities relies not only on pre-training but also on policy optimization during the post-training phase. The model employs joint reinforcement learning covering hundreds of downstream domains. Unlike single-task RL, joint RL requires the model to share and transfer policies across multi-domain tasks, thereby finding a Pareto optimal solution for generalization capabilities across heterogeneous tasks such as code generation, logical reasoning, and tool calling. Furthermore, both the training process and the released weights utilize MXFP4 (4-bit floating-point format) weight quantization, effectively compressing the massive parameter volume and reducing memory bandwidth pressure.

4. Engineering Deployment and API Integration Practice Guide

For developers and enterprises, seamlessly integrating such models into existing business workflows is key to successful deployment. Below are the core engineering specifications for deployment and API usage:

4.1 Inference Framework and Interface Compatibility
The open low-bit weights support mainstream high-performance inference frameworks (such as vLLM, SGLang, etc.) as well as specific acceleration engines. Regarding API design, the model provides an interface format compatible with mainstream commercial APIs (OpenAI/Anthropic protocol compatible), allowing developers to smoothly migrate without rewriting their underlying SDKs.

4.2 Context Management for Multi-Turn Tool Calling
Long-horizon Agents rely on a complete chain of thought. In multi-turn tool calling scenarios, developers must retain the complete “historical thinking process” and “tool calling messages” within the request Payload. If intermediate thinking steps are truncated, the model will lose its judgment on the current tool calling state, leading to infinite loops or hallucinated outputs.

The following is a standard API calling pseudo-code example preserving the complete history:

{
  "model": "large-moe-agent-model",
  "messages": [
    {"role": "user", "content": "Query the top 5 stocks by gains in the A-share market today"},
    {"role": "assistant", "content": "Thinking: I need to call the financial data API to get real-time market quotes.", "tool_calls": [{"id": "call_001", "type": "function", "function": {"name": "get_top_stocks", "arguments": "{\"market\": \"A\", \"sort\": \"desc\"}"}}]},
    {"role": "tool", "tool_call_id": "call_001", "content": "[{\"code\":\"600001\",\"change\":9.8%},{\"code\":\"600002\",\"change\":8.5%}]"},
    {"role": "assistant", "content": "Based on the tool results, among the top 5 stocks by gains in the A-share market today, the highest gain is stock code 600001, reaching 9.8%."}
  ],
  "thinking_budget": "max"
}

4.3 Cloud-Native Deployment Best Practices
When deploying a 2.8T parameter MoE model in a production environment, it is recommended to use a cloud-native container platform for resource scheduling. Utilize cloud providers’ container platforms and GPU resource pools, combined with object storage, to achieve rapid loading of model weights. Additionally, logging services and monitoring alerting systems should be integrated to track Token throughput, memory usage, and thinking budget trigger frequency in real-time, thereby dynamically adjusting the concurrency strategy of the inference service.

5. Licensing Compliance Notes

It is important to note that although the model weights are open, it uses a proprietary license rather than the industry-standard Apache 2.0 or MIT licenses. Before integrating it into commercial products, enterprises must carefully review the terms of this proprietary license, especially the restrictions regarding commercial authorization, secondary distribution, and derivative model usage, to ensure business compliance and avoid potential legal disputes.