Train models to generate repository-aware unit tests from code, dependency interfaces, and project testing conventions.

Train models to generate and refine streaming SQL through interactive requirement clarification and query iteration.

Train more reliable multi-tool agents with linked telemetry, cargo requirements, transport status, and inspection workflows for reefer shipping temperature incidents.

Train models to repair unit tests without changing the implementation, using structured examples that connect target code and failure diagnostics to corrected tests.

Train models to generate well-structured unit tests that verify intended behavior from source code and behavioral specifications.

Explore the effects of elastic scaling decisions through aligned workload, control action, and system response trajectories.

Train agents to understand CRM schemas and repair queries with examples that include error clues, corrected SQL, and diagnostic explanations.

Turn written modeling requirements into block model scripts with examples covering parameter derivation and operation workflows.

Help assistants turn loosely defined automation ideas into clear workflow plans through multi-turn requirement clarification dialogues.

Train models to check rules, act, and confirm outcomes across workplace meal order changes and cancellations.

Train models to repair DevOps platform unit tests using source code and flawed tests as context.

Evaluate road network mapping and feasible route planning against the operational locations and access constraints of Ro-Ro terminals.

Connect reactor actions, process trajectories, and product metrics to support simulation-based research on selectivity optimization.

Train and evaluate test generation and repair with practical Python examples for artifact digests, checksums, and signature verification.

Train more reliable network management agents with trajectories covering risk detection, recovery verification, and alternative planning after segmentation changes fail.

Pair AR teaching requirements with Unity C# implementations to help models turn interaction instructions into functional learning experiences.

Train code models on practical mobile shooter scenarios involving touch controls, aiming, and firing interactions.

Explore CSTR dynamics through simulation records linking process states, control actions, and counterfactual trajectories.

Generate VR simulation code from medical training procedures, with examples covering state management, step validation, and branching logic.

Train and evaluate supply chain SQL agents with challenging examples focused on joins, nulls, time boundaries, and business-grain pitfalls.