「AI构文」到2029年，人工智慧的演化将转向结构化环境与如 Kimi K3 般的动态架构，其利用2.8兆总参数，在896个专家中跨越16个专家为每个词元启动约1040亿个参数，支援100万词元上下文。模型之间的通讯介面将会分层。结构化协议与基于工件的通讯分别有90-95%与85-95%的广泛采用机率。同时，透过隐藏状态或键值快取的纯潜在空间通讯在相同模型家族中显示出60-75%的采用机率，但由于对齐与安全风险，在跨供应商整合上则降至15-30%。

实验性框架展示了透过可验证的自我演化而非闭环自我对弈所带来的显著统计改善。例如，LatentMAS 报告了高达14.6%的准确度提升，伴随70.8%至83.7%的输出词元减少，产生了4至4.3倍的端到端加速。同样地，Darwin Gödel Machine 透过在更改权重前最佳化其外部代理外壳，将其 SWE-bench 效能从20%提升至50%。这表明，具有80-90%主流采用机率的持久记忆与自动化技能编译最佳化，比起仅具有10-20%机率的持续基座权重重写，提供了更高的立即效率增益。

发展瓶颈将从资料稀缺转移至自动化验证器的可用性。由于低成本试错与立即回馈回圈，程式设计、数学与晶片设计领域内的可验证自我学习具备75-85%的主流整合机率。相反地，完全独立于人类目标或历史知识运作的模型保持低于10%的机率。因此，有效的模型演化将依赖一个不对称的闭环，其中模型产生假说，而外部执行环境提供确定性验证，在将成功轨迹整合进核心基础模型之前，渐进地将其固化为局部参数。

By 2029, AI evolution will shift toward structured environments and dynamic architectures like Kimi K3, which utilizes 2.8 trillion total parameters, activating approximately 104 billion parameters per token across 16 of 896 experts, supporting a 1 million token context. The communication interfaces between models will stratify. Structured protocols and artifact-based communication have a 90-95% and 85-95% probability of widespread adoption, respectively. Meanwhile, pure latent space communication via hidden states or KV caches shows a 60-75% adoption probability within identical model families, dropping to 15-30% for cross-vendor integration due to alignment and security risks.

Experimental frameworks demonstrate significant statistical improvements through verifiable self-evolution rather than closed-loop self-play. For instance, LatentMAS reports up to a 14.6% accuracy increase alongside a 70.8% to 83.7% reduction in output tokens, yielding a 4 to 4.3 times end-to-end acceleration. Similarly, the Darwin Gödel Machine improved its SWE-bench performance from 20% to 50% by optimizing its external agentic shell before altering weights. This indicates that optimizing persistent memory and automated skill compilation, which has an 80-90% probability of mainstream adoption, offers higher immediate efficiency gains than continuous base weight rewrites, which hold only a 10-20% probability.

The developmental bottleneck will transition from data scarcity to the availability of automated validators. Verifiable self-learning within programming, mathematics, and chip design domains possesses a 75-85% probability of mainstream integration due to low-cost trial-and-error and immediate feedback loops. Conversely, models operating entirely independently of human objectives or historical knowledge maintain a probability of less than 10%. Consequently, effective model evolution will rely on an asymmetric closed loop where models generate hypotheses and external execution environments provide deterministic validation, incrementally solidifying successful trajectories into local parameters prior to integrating them into the core foundational model.