Enterprise AI Analysis

The Verifiable Synthesis Engine autonomously generates diverse tasks coupled with executable validators. This 'Generation-as-Validation' approach ensures strict environmental grounding and eliminates ambiguity, providing precise, deterministic supervision signals. It includes Structured Task Space Construction, Agentic Dual-Stream Synthesis with a self-correction loop, and Rigorous Quality Assurance to filter for high consistency and prevent data leakage.

To support massive-scale experience acquisition, EvoCUA employs a high-performance infrastructure orchestrating tens of thousands of asynchronous sandbox rollouts. This system acts as a dynamic gymnasium, providing real-time feedback for on-policy optimization. It utilizes hybrid virtualization with QEMU-KVM within Docker, calibrated OS images for input determinism, rendering consistency, and runtime stability, processing millions of interaction requests daily.

The iterative evolving learning strategy efficiently internalizes experience. It begins with a diversity-aware cold start, followed by continuous environmental exploration. The model contrasts successful vs. failed trajectories, consolidating effective patterns and rectifying errors through error analysis and self-correction. This dynamic feedback loop transforms accumulated experience into robust execution policies, yielding consistent performance gains across various foundation models.

Key components include Rejection Sampling Fine-Tuning (RFT) to consolidate successful experiences and Step-Level Direct Preference Optimization (DPO) to learn from failures and explore via online interaction. RFT filters for high-quality, successful executions, while DPO targets Critical Forking Points in failed trajectories to generate preference pairs for robust error correction and recovery.