Post-Training Evolution for LLMs: Deep Dive into Direct Code Execution and 3D Scripting Capabilities

As large-scale pretraining and post-training techniques continue to evolve, a new wave of open-source models has emerged with notable improvements across multiple capability dimensions. Through deep post-training optimization of base models, these systems are now demonstrating strong potential in terminal code execution, 3D spatial reasoning, complex financial document analysis, and cybersecurity defense. This article sets aside specific brand and product attributes to focus on benchmark performance and engineering practices from a purely technical perspective, analyzing the core post-training breakthroughs and their impact on the developer community.

1. Code Generation: Balancing Terminal Execution Capability and Cost Efficiency

In modern software engineering, large models are no longer limited to completing code snippets. They are evolving toward agents that can directly execute complex tasks in terminal environments. Benchmarks such as Terminal Bench have become an important way to measure model performance on real-world coding tasks.

1.1 Breakthroughs in Terminal Task Execution

Current frontier open-source models have achieved major score improvements on terminal coding tasks, with some models approaching or even surpassing leading closed-source models. The core of this breakthrough lies in the introduction of large amounts of real terminal interaction trajectory data during post-training. Models must not only understand code syntax, but also master file system operations, process management, dependency resolution, and error backtracking. For example, when handling multi-file refactoring or automated test script creation, a model can autonomously plan its execution path, read error messages, perform multiple iterative steps, and ultimately complete the task.

1.2 Balancing Cost Control and Engineering Deployment

Beyond absolute performance metrics, inference cost is a decisive factor for enterprise adoption. In terminal benchmark tests, cost-effectiveness varies significantly across models. Some lightweight, high-speed models have extremely low per-call costs, but their lower success rates on complex code logic can cause total retry costs to surge. In contrast, deeply post-trained models may consume more resources per inference, yet their higher first-pass success rate often leads to better overall cost efficiency. When selecting models, development teams should move away from simple unit-price comparisons and instead evaluate the total cost per completed task.

2. 3D Modeling: From MCP Tool Invocation to Direct Script Writing

3D creation and spatial reasoning represent an important cross-modal direction for large models. Previously, model participation in 3D modeling relied heavily on external Model Context Protocol (MCP) tools or specific API wrappers. Models could only generate high-level instructions, which were then translated into operations by an intermediate layer.

2.1 The Rise of Direct Script Writing

Recent technical progress shows that models can now bypass the MCP intermediate layer and directly write low-level scripts for mainstream 3D modeling software. This means a model can output script code that directly operates professional modeling tools to handle asset creation, refinement, layout adjustment, material assignment, and geometric transformation. This direct writing paradigm greatly reduces communication overhead and leads to a qualitative improvement in the precision and coherence of 3D generation.

2.2 Spatial Reasoning and Industrial-Grade Applications

In tests such as Bench CAD, tool-dependent models have achieved scores above 90, approaching top industry levels. In practical cases, whether constructing complex indoor spaces with extensive vegetation, such as an indoor forest in an airport, or industrial components with precise mechanical structures, such as a vortex engine, or consumer products that balance function and aesthetics, such as a 3D-printed desk lamp, models can use scripts to precisely control the generation logic of vertices, edges, and surfaces.

To support this capability, the front-end application layer has also evolved. Some platforms now offer online preview and editing of 3D models. Generated models can be directly exported to standard file formats compatible with 3D printers, or rendered as videos and dynamic images for social sharing. This connects the entire pipeline from text or concept input to physical output.

3. Financial Analysis: Complex Document Reasoning and Hallucination Suppression

In finance, large models are tested on large-scale data retrieval, cross-document verification, and multi-step numerical reasoning. Benchmarks such as OfficeQA Pro simulate real investment analysis workflows and require models to accurately parse charts, tables, and long-form text interwoven in complex financial research reports.

3.1 Full Workflow Support for Investment Analysis

Current advanced open-source models have achieved leading scores on complex financial document analysis. In tests covering complete workflows from initial target screening to earnings forecasting, models demonstrate strong multi-step reasoning abilities. The technical core lies in combining reinforcement learning with chain-of-thought reasoning. When faced with questions such as comparing the gross margin difference between Company A in Q3 2023 and Company B in Q1 2024 and analyzing the causes, the model can extract data step by step, perform calculations, and produce a logically rigorous deduction process.

3.2 Significant Improvement in Hallucination Testing

Financial scenarios demand extremely high data accuracy, where any hallucination can lead to serious decision-making errors. In strict internal hallucination stress tests, the error rate of the new generation of models has dropped significantly from more than 20 percent in the previous generation to single digits. This improvement stems from post-training alignment with factual knowledge and optimization of rejection sampling mechanisms. When models face knowledge gaps or data conflicts, they are now more likely to output a determination of insufficient information rather than fabricating data.

4. Cybersecurity: The Double-Edged Sword of Scaling Post-Training

Cybersecurity is one of the most challenging application areas for large models. As post-training scale continues to expand, models have gained significantly deeper understanding of underlying system vulnerabilities, malicious scripts, and network attack vectors.

4.1 Improved Defensive Capabilities

On the defense side, safety-aligned models can more accurately identify injection risks, permission escalation, and unsafe dependency calls in code. They can serve as auxiliary code audit tools, quickly locating potential security risks in large codebases and providing remediation recommendations.

4.2 Jailbreak Attacks and Alignment Challenges

However, scaling post-training is also a double-edged sword. A deeper understanding of vulnerability principles means the model itself may be induced to generate malicious code. Industry practitioners have found that as models become stronger at logical reasoning, traditional safety guardrails face greater pressure. Complex prompt injection or multi-turn conversational manipulation may expose underlying security risks. Therefore, deploying large models in cybersecurity scenarios must be accompanied by strict input and output filtering gateways, content safety review mechanisms, and fine-grained permission control systems.

5. Technical Architecture Evolution and Enterprise Deployment Recommendations

Overall, the breakthroughs of large models in code, 3D, finance, and security signal the full success of the general-purpose base model plus deep post-training approach. For developers and enterprise users, the following architecture patterns and strategies are recommended when adopting these capabilities:

1. Build closed-loop agent workflows. Do not treat large models merely as question-answering machines. Instead, connect them to containerized terminal execution environments, combined with message queues and logging services, to build agent workflows capable of autonomous execution, feedback, and iteration.
2. Adopt hybrid model routing strategies. Implement intelligent routing at the business gateway layer. For simple code completion or basic Q&A, call low-cost high-speed models. For complex terminal refactoring, 3D script writing, or financial report analysis, route requests to deeply post-trained high-performance models.
3. Manage storage and generated assets. For 3D model generation and financial data analysis, enterprises should use highly available object storage to preserve generated assets and establish version control mechanisms for traceability and secondary fine-tuning.
4. Prioritize security and compliance. Before invoking large model capabilities, always pass requests through an independent content security gateway for data masking and compliance filtering. This helps prevent leakage of core enterprise data while blocking jailbreak attempts through malicious instructions.

In summary, the potential of large models in the post-training stage is being fully unlocked. From terminal code execution to direct 3D script writing, these advances are not only expanding the application boundaries of AI but also bringing new engineering challenges and opportunities to the developer community. Understanding how these underlying capabilities are evolving will help enterprises gain an advantage in the next wave of AI adoption.