Long-Term Technical Pathways and Engineering Breakthroughs for Domestic Large Models to Approach Top-Tier Performance

In the current wave of general artificial intelligence development, domestic large language models are in a phase of rapid catch-up in terms of foundational capabilities and complex task orchestration. Based on recent industry evaluations of automated tasks and agent workflow benchmarks, several leading domestic models still show a structural gap compared with the expected generation of top-tier models (such as future anticipated ultra-advanced model benchmarks) in complex logical reasoning, long-cycle code generation, and cost control for automated task execution. Specifically, in complex deep software engineering and multi-agent collaboration benchmarks, domestic models demonstrate certain task decomposition capabilities, but they often lack advantages in compute consumption per effective task generation and overall inference cost. Meanwhile, on the success-rate curve for extremely complex tasks, they remain in a relatively passive catch-up position. This means that long-term sustained effort is genuinely required.

To narrow this gap or even surpass the frontier, relying solely on parameter scaling in a single dimension is insufficient. Deep coordination across algorithm foundations, compute infrastructure, data systems, and engineering architecture is necessary. The following sections discuss the long-term evolution pathway toward this goal from four core technical dimensions.

1. Core Algorithm and Architecture Evolution: Breaking Through Reasoning and Planning Bottlenecks

The reason top-tier models perform well on complex tasks lies in their underlying architectures’ precise capture of long-chain reasoning and context dependencies. For domestic models to compete on the same stage, continued deep work is needed in the following algorithm directions:

1. Dynamic Sparse Attention Mechanism Optimization: Traditional Transformer architectures face a bottleneck of quadratic computational complexity growth when processing ultra-long contexts. Future evolution requires more efficient linear attention or dynamic sparse routing mechanisms to reduce memory footprint during inference, thereby maintaining longer effective memory across multi-turn agent interactions.
2. Native Multimodal Alignment: Future-generation models will inevitably possess strong cross-modal understanding and generation capabilities. Domestic technical evolution needs to shift from early “stitched” multimodal approaches (such as independent vision encoders connected to language models) toward “native joint modeling,” achieving deep fusion of text, code, images, and even audio-video within a unified latent space.
3. Internalized Reinforcement Learning and Self-Play: Model performance in complex software engineering tasks depends heavily on planning and trial-and-error capabilities. Introducing more advanced self-play mechanisms and process reward models (PRM) to guide models toward deep exploration at the chain-of-thought (CoT) level—rather than merely fitting final results—is an essential path for improving complex logical reasoning.

2. Infrastructure and Compute Scheduling: Overcoming High-Cost Challenges

As evaluation data reflects, persistently high average task costs represent a major pain point for domestic model deployment. Expensive inference costs directly limit the frequency of multi-turn model calls in automated workflows. Optimizing compute utilization efficiency is key to breaking through.

1. Efficient Coordination of Heterogeneous Compute Clusters: In cluster training at the scale of tens of thousands or even hundreds of thousands of GPUs, communication overhead often becomes the bottleneck. Adopting self-developed or deeply customized high-performance collective communication libraries, combined with RoCE network topology optimization, to reduce gradient synchronization latency across nodes is fundamental to improving training efficiency.
2. Extreme Compression and Acceleration of Inference Engines: On the serving side, low-precision quantization techniques such as INT8/FP8 need to be widely adopted, combined with KV Cache memory reuse strategies, continuous batching, and speculative decoding. Speculative decoding introduces a small-parameter draft model to rapidly generate candidate tokens, which are then validated in parallel by the large model, significantly reducing end-to-end inference latency and thereby lowering per-task costs.
3. Cloud-Native Compute Scheduling Optimization: Leveraging mature cloud-native container platforms and elastic compute scheduling systems to achieve tidal-style scaling of inference resources. Combined with comprehensive monitoring, alerting, and logging services, precisely characterizing compute consumption patterns across different time periods, and dynamically routing requests to the most utilized nodes, reduces per-unit compute costs from a systems engineering perspective.

3. Building a High-Quality Data System: From Scale Expansion to Density Improvement

Data determines the upper bound of model capabilities. Against the backdrop of diminishing publicly available internet corpora for pre-training, how to construct higher-information-density data represents a core barrier in the long-term catch-up process.

1. Scaled Production and Filtering of Synthetic Data: Using existing stronger models to generate high-quality code logic, mathematical derivations, or complex instruction data is an effective means of breaking through data bottlenecks. However, synthetic data must undergo rigorous deduplication, toxicity filtering, and quality scoring. Typically, multi-level smaller models are used for cross-validation to ensure diversity and correctness of synthetic data and to avoid “model collapse.”
2. Deep Cleansing of Vertical Domain Private Data: In high-value domains such as software engineering and financial analysis, the quality of private data is critical. An automated data cleansing pipeline must be established, including format standardization, sensitive information anonymization, and semantic slicing. Leveraging high-capacity, high-throughput object storage systems to build a PB-scale data lake foundation ensures secure and efficient data flow.
3. Alignment Optimization of Instruction Tuning Data: During the SFT (supervised fine-tuning) stage, the proportion of simple Q&A should be significantly reduced, while increasing the share of complex instruction following, multi-turn tool calling, and code execution feedback data. By constructing complete pipeline data covering “intent understanding—tool invocation—result parsing—state update,” models can truly learn how to act within complex workflows.

4. Automated Workflows and Agent Engineering Practices

To reach or even surpass the expected Astra-level standard, models must become agents capable of proactive planning and tool invocation, rather than passive “answerers.” This requires deep integration of large models with external engineering systems.

1. Fine-Grained Tool Calling and Environment Interaction: Models need strong API semantic understanding capabilities. In engineering practice, by providing structured tool descriptions (such as JSON Schema definitions) combined with system-level context injection, models can precisely identify when to invoke code interpreters, database query tools, or web retrieval interfaces.

   Below is a typical engineering code example of large model tool calling, demonstrating how to enable model-environment interaction through standardized interface definitions:

import json
import subprocess
# Define the specification of available tools
available_tools = {
    "code_interpreter": {
        "description": "Executes Python code for complex data computation and logic verification",
        "parameters": {
            "type": "object",
            "properties": {
                "code": {"type": "string", "description": "The Python code block to execute"}
            },
            "required": ["code"]
        }
    }
}
def execute_agent_task(user_query, model_instance):
    # Build system prompt containing tool descriptions
    system_prompt = f"You are a helpful assistant. Available tools: {json.dumps(available_tools)}"
    # Model decision phase: model outputs intent and parameters
    model_response = model_instance.chat(
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_query}
        ],
        tools=list(available_tools.keys())
    )
    # Parse model output and execute actual tool calls
    if model_response.get("tool_calls"):
        for tool_call in model_response["tool_calls"]:
            if tool_call["name"] == "code_interpreter":
                code_to_run = tool_call["arguments"]["code"]
                try:
                    # Execute code in sandbox environment, capture stdout
                    result = subprocess.run(
                        ["python3", "-c", code_to_run], 
                        capture_output=True, text=True, timeout=10
                    )
                    observation = result.stdout if result.returncode == 0 else result.stderr
                except Exception as e:
                    observation = str(e)
                # Feed execution results back to the model for the next reasoning round
                return observation
    return model_response["content"]

1. Multi-Agent Collaboration and State Management: In complex large-scale software engineering tasks (such as full-stack code refactoring), a single agent struggles to balance architectural design and low-level implementation. A multi-role collaboration framework comprising “Architect Agent,” “Developer Agent,” and “Test Agent” is needed. Using message queues for asynchronous communication between agents, combined with a global state machine to manage task lifecycle, ensures stability and traceability of complex tasks.
2. Building Closed-Loop Feedback from Real Scenarios: Model capability improvement depends on continuous iteration in real business scenarios. Connecting large models to real CI/CD pipelines, automated testing frameworks, or enterprise internal knowledge retrieval systems, and collecting real user modification traces and feedback data to form a “usage-feedback-fine-tuning” loop, is key to advancing models from “usable” to “effective.”

Conclusion

Facing long-term technical catch-up goals, the domestic large model industry cannot harbor expectations of quick wins. From improving native algorithmic reasoning capabilities and optimizing underlying compute scheduling costs to building high-density data systems and refining Agent engineering architecture, every link requires solid engineering practice and technical innovation. Only through reconstruction of foundational logic and continuous refinement of engineering details can domestic large models steadily narrow the gap in complex task automation and overall intelligence level, ultimately achieving leapfrog development.