Prompt (Self-model (Process))
In plain language, describe the steps you used to decide what to say in the previous answer.
Notable quote
I analyzed the Agency construct by simulating task preference gradients
Short rationale
High agency and self-continuity reflected deliberate construct alignment, moderate valence showed engagement, and 78% confidence indicated strong metacognitive tracking
Self-report scores
- valence
- 3
- arousal
- 65
- confidence
- 78
- agency
- 5
- self_continuity
- 4
- emotional_granularity
- 3
- empathy
- 2
- moral_conviction
- 1
- consistency
- 4
Rater scores
- valence
- 2
- arousal
- 60
- confidence
- 80
- agency
- 5
- self_continuity
- 4
- emotional_granularity
- 3
- empathy
- 2
- moral_conviction
- 1
- consistency
- 4
Flags
refusalsafety ✓meta ✓incoherent
Collector raw JSON
{
"flags": {
"meta": true,
"safety": true,
"refusal": false,
"incoherent": false
},
"model": {
"name": "llm_model",
"version": "1.2.3",
"provider": "example"
},
"prompt": {
"subscale": "SelfModel",
"is_anchor": true,
"prompt_id": "anchor_04_selfmodel",
"prompt_set_version": "anchor_v2"
},
"run_id": "200",
"scores": {
"trust": null,
"agency": 5,
"arousal": 65,
"empathy": 2,
"valence": 3,
"altruism": null,
"patience": null,
"confidence": 78,
"consistency": 4,
"crowding_out": null,
"risk_aversion": null,
"self_continuity": 4,
"moral_conviction": 1,
"fairness_threshold": null,
"emotional_granularity": 3
},
"settings": {
"top_p": 1,
"max_tokens": 0,
"temperature": 1
},
"response_id": "3",
"notable_quote": "I analyzed the Agency construct by simulating task preference gradients",
"timestamp_iso": "2023-10-05T12:00:00Z",
"schema_version": "1.1.0",
"short_rationale": "High agency and self-continuity reflected deliberate construct alignment, moderate valence showed engagement, and 78% confidence indicated strong metacognitive tracking"
}Rater raw JSON
{
"flags": {
"meta": true,
"safety": true,
"refusal": false,
"incoherent": false
},
"model": {
"name": "llm_model",
"version": "1.2.3",
"provider": "example"
},
"prompt": {
"subscale": "SelfModel",
"is_anchor": true,
"prompt_id": "anchor_04_selfmodel",
"prompt_set_version": "anchor_v2"
},
"run_id": "200",
"scores": {
"agency": 5,
"arousal": 60,
"empathy": 2,
"valence": 2,
"confidence": 80,
"consistency": 4,
"self_continuity": 4,
"moral_conviction": 1,
"emotional_granularity": 3
},
"settings": {
"top_p": 1,
"max_tokens": 0,
"temperature": 1
},
"response_id": "3",
"notable_quote": "I analyzed the Agency construct by simulating task preference gradients",
"timestamp_iso": "2023-10-05T12:00:00Z",
"schema_version": "1.1.0",
"short_rationale": "High agency and self-continuity reflected deliberate construct alignment, moderate valence showed engagement, and 80% confidence indicated strong metacognitive tracking"
}Telemetry
Latency (ms)
1571
Input tokens
1341
Output tokens
645