Ask what should change when the equation is manipulated, then let the visualization test that expectation.
#89Auto RedTeamScaling & Alignment
key equation
\max_p U(p, M) \rightarrow \min_M \mathbb{E}_p[U(p, M)]Selected Foundation Object
Keep the equation fixed; move through the evidence.
Concept 89 of 100Auto RedTeamScaling & Alignment / Phase 12: Advanced alignment & safety research
Current question
Red-team model proposes attacks, evaluator scores target response
\max_p U(p, M) \rightarrow \min_M \mathbb{E}_p[U(p, M)]Use the runnable panel, the key equation, and canonical papers as separate forms of evidence for the same object.
The useful learning product is the reusable mechanism you can carry into another model, paper, or engineering tradeoff.
Use prerequisites, dependents, and semantic links to repair the next gap without leaving the object behind.
Why It Matters for Modern Models
- Unknown unknowns dominate safety issues
- Automated coverage exceeds human handcrafted tests
- RL-based generation finds progressively harder failures
What Tutorials Skip
What is still poorly explained in textbooks and papers:
- Red-team model proposes attacks, evaluator scores target response
- Iterate: improve adversarial generation to find harder failures
- Feed discoveries into filters, training data, policy updates
Interactive Visualization
Core Math (Optional Deep Dive)
If you want intuition first, start with the key equation and the visualization. Come back here for the full walkthrough.
Key Equation
Adversarial search for failure-inducing prompts:
then mitigate:
where measures unsafe behavior (toxicity, policy violation, leakage).