Update model card for failure_mode_enum

README.md

@@ -57,6 +57,56 @@ Because this is an adapter, the base model still provides the underlying general
 
 These numbers come from held-out synthetic probes, not from open-ended human evaluation. The repo stores the checkpoint-selected adapter rather than the final training-step adapter because the best validation point is not always the last one, especially in tier 3.
 
+## Representative Training Examples
+
+These are short excerpts from the actual synthetic training rows used for this adapter.
+
+### Example 1
+
+Prompt:
+> Apply failure_mode_enum to: A historian is evaluating the risk that a long-form trade book fails to meet academic rigor standards, potentially damaging their tenure prospects.
+
+Target completion excerpt:
+> ## System: Academic Trade Book Publication Process. ## Failure Modes: 1. Over-simplification of primary source analysis to favor narrative flow, leading to accusations of historical inaccuracy. 2. Failure to engage with contemporary historiographical debates, resulting in the work being dismissed as amateurish by peers. 3. Excessive reliance on secondary sources without sufficient archival grounding, undermining the claim to original scholarship. 4. Misinterpretation of nuanced evidence due to the constraints...
+
+### Example 2
+
+Prompt:
+> Apply failure_mode_enum to: Evaluate the risk of personnel turnover within the 10-year study timeline and determine which knowledge-loss scenarios are most critical to mitigate through documentation or mentorship protocols.
+
+Target completion excerpt:
+> ## System: 10-year longitudinal research study personnel management framework. ## Failure Modes: 1. Departure of Principal Investigators (PIs) leading to loss of institutional memory and strategic direction. 2. Loss of specialized data analysts causing disruption in longitudinal data processing and continuity. 3. High turnover of field technicians resulting in inconsistent data collection protocols. ## Severity: PI departure is critical (High) as it threatens project funding and scientific integrity. Analyst...
+
+## Representative Held-Out Evaluation Examples
+
+These examples come from the held-out probes scored at the selected checkpoint.
+
+### Strong held-out probe
+
+Prompt:
+> Apply failure_mode_enum to: A city is deciding whether to restrict through-traffic on residential streets in a contested neighborhood to reduce noise pollution.
+
+Correct procedure application excerpt:
+> ## System: Neighborhood noise abatement plan. ## Failure Modes: 1. Noise shift to arterial roads. 2. Increased idling at neighborhood perimeter. 3. Failure of noise reduction targets. ## Severity: Noise shift is moderate; idling is low. ## Likelihood: Noise shift is high; idling is moderate. ## Detectability: Noise sensors can track shifts; idling is...
+
+Structured negative excerpt:
+> ## System: Neighborhood noise abatement plan. ## Failure Modes: 1. Noise shift to arterial roads. 2. Increased idling at neighborhood perimeter. ## Severity: Noise shift is moderate. ## Likelihood: Noise shift is high. ## Controls: Sound barriers on arterial roads. (Missing...
+
+Observed margin at the selected checkpoint: `1.3827`
+
+### Harder held-out probe
+
+Prompt:
+> Apply failure_mode_enum to: Evaluate the risk of implementation failure for the algebra tracking removal.
+
+Correct procedure application excerpt:
+> ## System Policy implementation process. ## Failure Modes 1. Poor communication with parents causing public outcry. 2. Inadequate timeline for curriculum development. 3. Lack of data-driven monitoring during the first year. 4. Failure to secure board consensus. ## Severity High: Implementation failure leads to policy reversal and loss of credibility. ##...
+
+Structured negative excerpt:
+> ## System Policy implementation process. ## Failure Modes 1. Poor communication. 2. Inadequate timeline. 3. Lack of monitoring. 4. Failure to secure consensus. ## Severity High. ## Likelihood Medium. ## Detectability High. ## Controls We will just start the program in...
+
+Observed margin at the selected checkpoint: `-0.0459`
+
 ## Files In This Repo
 
 - `adapter_model.safetensors` and `adapter_config.json`: the selected PEFT adapter weights