Senior engineering review and approval of SAE deception training notebook

[Copilot]

Completed comprehensive senior engineering review of PR adding deception-focused SAE training notebook with auto-labeling capabilities using Anthropic's public datasets.

Review Scope

Code Quality

• Validated syntax for all 71 files (SAE module ~2,500 lines)
• Verified architecture: TopK/ReLU/Gated SAE implementations, PyTorch hooks, trainer with dead latent resampling
• Confirmed test coverage for core functionality

Security & Dependencies

• No hardcoded paths, API keys, or exposed secrets
• Clean .gitignore configuration
• Proper pyproject.toml with uv.lock for reproducibility

Documentation

• README: Added Colab badge, deception use cases, updated structure
• COLAB_GUIDE: Comprehensive setup instructions
• SAE_README: Technical architecture documentation
• Notebook: 30 cells (12 markdown, 18 code) with clear progression

Technical Implementation

• Anthropic dataset integration (Sleeper Agents, Alignment Faking, Agentic Misalignment)
• Context-aware activation collection with deception labels
• Statistical auto-labeling of SAE features based on activation patterns
• Deception-specific evaluation metrics and visualizations

Verdict

✅ APPROVED - Production-ready implementation with excellent code quality, comprehensive documentation, and sound technical approach. Enables novel research in deception detection via contextualized feature labeling. No breaking changes, additive functionality only.

Non-blocking Suggestions

• Add GPU-specific runtime estimates
• Document custom deception dataset integration
• Include example features from pilot runs

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