January 2026 (2026-01) monthly summary for safety-research/bloom. Focused on delivering targeted variation capabilities within the existing variation system, improving evaluation accuracy and reducing pipeline complexity. Key outcomes include the introduction of counterfactual variation analysis driven by extracted dimensions and a configuration-driven approach to steer targeted variations, along with code quality improvements and documentation updates. Key features delivered: - Counterfactual Variation Analysis integrated into the variation system, enabling targeted variation generation based on judgment dimensions (e.g., emotional_pressure, authority_framing). This supports evaluating stability of outcomes under specific hypotheses without adding new pipeline stages. - Introduction of ideation.variation_dimensions configuration to drive targeted variations, replacing noisy perturbations when specified, and enabling opt-in control via configuration. - Initial guidance for the variation workflow: evaluate -> extract-dimensions -> configure -> re-run; later refined into a simplified config model (num_scenarios and variation_dimensions). - Refactor and simplification: replaced total_evals and diversity with num_scenarios; default behavior is no variations unless variation_dimensions are specified; removal of the separate extract-dimensions CLI command to streamline the flow. - Documentation and templates: added COUNTERFACTUAL_VARIATIONS.md and updated seed.yaml.template to reflect new config options. Code quality and reliability improvements: - Ruff linting fixes, whitespace/import cleanup, and removal of unused variables to improve readability and maintainability. - Import organization and clearer debug statements for step2_ideation.py. Overall impact: - Improved business value by enabling targeted hypothesis testing with minimal pipeline changes, leading to faster insights into judgment stability under specific conditions. Maintained compatibility with existing judgment features and modalities while increasing configurability and maintainability. Technologies/skills demonstrated: - Python-based CLI tooling, configuration-driven design, code quality tooling (ruff), documentation, and modular refactor of variation infrastructure.