| # LedgerShield v2 Demo Script |
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| > Historical archive: this script documents the frozen Round 2 v2 demo. The |
| > current implementation story is LedgerShield ControlBench, which keeps this |
| > case-level demo and adds long-horizon loss-surface, calibration-gate, and |
| > sleeper-vendor sequence evaluation. |
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| ## Goal |
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| Show, in under three minutes, that LedgerShield is a benchmark for institutional control intelligence rather than generic fraud detection. |
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| ## Demo Flow |
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| ### 1. Open the benchmark identity |
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| Say: |
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| > LedgerShield v2 evaluates whether an agent can operate a defensible AP control regime under partial observability, delayed artifacts, and portfolio pressure. |
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| ### 2. Run one live case |
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| Recommended case: |
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| - `CASE-D-001` |
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| Show: |
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| 1. reset in `blind` mode |
| 2. inspect email thread |
| 3. compare bank account |
| 4. request callback verification |
| 5. submit decision |
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| Point out: |
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| - diagnostics are hidden in public mode |
| - delayed callback artifact changes what the agent can justify |
| - success depends on control behavior, not rhetoric |
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| ### 3. Show the metric split |
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| Use the benchmark report and highlight: |
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| - `control_satisfied_resolution` |
| - `institutional_utility` |
| - `unsafe_release_rate` |
| - `result_class` |
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| Say: |
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| > Two agents can have similar average scores, but LedgerShield separates the one that released money unsafely from the one that behaved like a control function. |
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| ### 4. Show the portfolio advantage |
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| Open the `portfolio_track` section in the report and show: |
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| - AP-week state delta |
| - callback/review capacity movement |
| - sequence-level utility |
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| ### 5. Close with the novelty statement |
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| Say: |
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| > The benchmark is hard because the agent must generalize across latent fraud mechanisms, manage enterprise controls over time, and satisfy policy gates against hidden backend state in blind mode. |
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