Try ObserverBench
Test internal AI monitors by the harm they miss
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What is Try ObserverBench?
ObserverBench is a research workbench that tests whether analyzing an AI model's internal activity (activations, internal edits, extracted features) can help make safer decisions about model actions. It provides guided examples, benchmark tasks, and comparative results across multiple models (GPT-2, Qwen, Gemma) to evaluate safety monitoring methods without requiring GPU access or login.
Try ObserverBench pricing
Pricing model: Freemium
Try ObserverBench pros
- No login or model download required for guided examples
- Supports multiple models including GPT-2, Qwen2.5-7B, Qwen3.5-9B, and Gemma with internal edits and safety monitoring
- Pre-saved measurements allow exploration and testing without GPU
- Tests method integrity including scenarios with deliberately altered records to validate monitoring reliability
- Ability to inspect real-model results, run existing benchmark tasks, and test custom observer methods
Try ObserverBench cons
- Limited to specific pre-tested models; may not cover all architectures
- Requires technical background in mechanistic interpretability and safety evaluation concepts
- Full measurement collection and complex custom testing require GPU
- Research-focused project with limited commercial support infrastructure
Frequently asked questions about Try ObserverBench
Do I need special equipment to try ObserverBench?
No, guided examples work without login or model downloads. Many tasks include pre-saved measurements so you can explore without a GPU.
What models does ObserverBench include?
The workbench includes experiments on GPT-2, Qwen2.5-7B (base and Instruct), Qwen3.5-9B, and Gemma, covering internal model edits and safety monitoring.
What is an 'observer' in this context?
An observer is a method that combines measurements of an AI model's internal activity to estimate whether a proposed action might violate instructions, producing a warning score to guide safety decisions.
Can I test my own methods?
Yes, the full workbench allows you to run existing tasks, inspect results, and test your own observer methods on the provided models.