June 2025 accomplishments centered on enhanced CoT analysis capabilities in EquiStamp/AISI-control-arena. Delivered CoTOnlyMonitor to exclusively process Chain-of-Thought from assistant messages (excluding actions) for research analysis of encoded reasoning in AI agent behavior; added the monitor class and its prompt to the control_arena repository. No major bugs fixed this month. This work improves observability, enables targeted CoT experiments, and strengthens the groundwork for governance and evaluation of AI agents. Skills demonstrated include Python class design, monitor architecture, and prompt engineering.

May 2025 performance summary for EquiStamp/AISI-control-arena: Delivered targeted enhancements to the Evaluation Framework, focusing on safer, more informative evaluation of control policies and enabling deeper protocol analysis. Implemented parallel sampling in the defer_to_resample path to improve evaluation throughput, reducing runtime bottlenecks in large-scale experiments. Introduced ProtocolEvalState metadata to track attack steps, enabling dynamic protocol flow and improved traceability of evaluation outcomes. Tightened attack policy and reporting with an enhanced safety-vs-usefulness visualization and improved known_strings policy, resulting in clearer decision support for security controls. Completed essential code hygiene by removing non-production print statements, improving log quality and production readiness. Overall, these changes increased evaluation reliability, reduced runtime where feasible, and provided richer instrumentation for iterative security policy development.

2025-04 monthly summary focusing on key accomplishments for EquiStamp/AISI-control-arena, highlighting business value and technical achievements. Emphasis on delivering safety-aware automation capabilities, reproducible experiments, and reliability improvements that inform deployment decisions.

March 2025: Focused on strengthening testing, platform reliability, and safe reuse of training scripts. Delivered a new unit testing framework for Kubernetes sabotage modules with sandboxed execution and Prometheus tests; introduced a get_task API and default protocol updates for the Kubernetes infrastructure sabotage platform with updated dependencies; improved documentation for the evaluation command; and fixed a critical import-time Ray initialization issue in the ca-k8s-infra training script. Results: higher test coverage, safer module execution, and smoother onboarding with up-to-date dependencies and clearer docs. Technologies demonstrated include Python test harnessing, sandbox execution, Prometheus integration, API design, dependency management, and safe import patterns.