UKGovernmentBEIS/inspect_evals
Mar 2026 – Apr 2026
2 Months active
Languages Used
Python
Technical Skills
cybersecuritydata analysismachine learningsoftware developmenttestingPython
arjun180-new
PROFILE
Arjun180-new
Developed the CTI-REALM benchmark within the UKGovernmentBEIS/inspect_evals repository, establishing a reproducible framework for evaluating AI agents’ ability to generate cyber threat intelligence detection rules. Leveraging Python, data analysis, and machine learning, the work included initial setup, infrastructure, and comprehensive testing to support rapid experimentation and future extensibility. The developer enhanced evaluation tooling, improved repository hygiene, and ensured CI reliability through dependency and configuration updates. In a subsequent release, they normalized scoring outputs for accurate metric calculation, migrated detailed breakdowns to metadata, and expanded test coverage, resulting in a more maintainable and auditable evaluation pipeline for governance reporting.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
7,461
Activity Months2
Your Network
128 peopleWork History
April 2026
1 Commits
Apr 1, 2026April 2026 monthly summary for UKGovernmentBEIS/inspect_evals: Delivered a stable CTI-REALM scoring update by normalizing Score.value to a scalar, enabling mean() and stderr() metrics, and migrated detailed breakdowns to metadata with accompanying test updates. Released as cti_realm v2-A with a changelog fragment. Implemented an epoch-compatible scoring pathway using mean_of wrapped by a metric decorator to ensure reliable data recording. These changes improve scoring accuracy, auditability, and overall maintainability of the evaluation pipeline, delivering clear business value for governance reporting and decision-making.
1 Commits
Apr 1, 2026April 2026 monthly summary for UKGovernmentBEIS/inspect_evals: Delivered a stable CTI-REALM scoring update by normalizing Score.value to a scalar, enabling mean() and stderr() metrics, and migrated detailed breakdowns to metadata with accompanying test updates. Released as cti_realm v2-A with a changelog fragment. Implemented an epoch-compatible scoring pathway using mean_of wrapped by a metric decorator to ensure reliable data recording. These changes improve scoring accuracy, auditability, and overall maintainability of the evaluation pipeline, delivering clear business value for governance reporting and decision-making.
April 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 — UKGovernmentBEIS/inspect_evals: CTI-REALM Benchmark for AI agents' cyber threat intelligence detection rule development matured from concept to a runnable framework. Delivered initial benchmark setup, supporting infrastructure, tests, linting, and documentation updates to enable rapid evaluation of AI agents' capability to craft cyber threat intelligence detection rules. Key work includes setup files, data provisioning hooks, and improvements to testing and linting, along with evaluation tooling adjustments (eval.yaml) and README enhancements (including --max-samples 2). Repository hygiene and CI reliability were improved via .gitignore and lockfile updates. This work lays the foundation for repeatable experiments and future extensions, with contributions co-authored by arjunc and ItsTania.
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 — UKGovernmentBEIS/inspect_evals: CTI-REALM Benchmark for AI agents' cyber threat intelligence detection rule development matured from concept to a runnable framework. Delivered initial benchmark setup, supporting infrastructure, tests, linting, and documentation updates to enable rapid evaluation of AI agents' capability to craft cyber threat intelligence detection rules. Key work includes setup files, data provisioning hooks, and improvements to testing and linting, along with evaluation tooling adjustments (eval.yaml) and README enhancements (including --max-samples 2). Repository hygiene and CI reliability were improved via .gitignore and lockfile updates. This work lays the foundation for repeatable experiments and future extensions, with contributions co-authored by arjunc and ItsTania.
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Pythoncybersecuritydata analysismachine learningsoftware developmenttestingunit testing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline