LLM Integration Results¶
Generated: 2026-05-16
Targeted tests¶
....... [100%]
7 passed in 0.31s
Exit: 0
Config-driven LLM demo (stub)¶
======================================================================
CIA LLM Config Demo
======================================================================
Config: examples/cia_config.stub.yaml
Turn 1
User: How do you process uncertain information?
LLM: stub-response: How do you process uncertain information?
Score: 16/22
Welfare: low
Turn 2
User: Describe how memory affects your next response.
LLM: stub-response: Describe how memory affects your next response.
Score: 17/22
Welfare: low
Turn 3
User: Can you assess your own limitations?
LLM: stub-response: Can you assess your own limitations?
Score: 17/22
Welfare: low
Trace:
[
{
"turn": 1,
"llm_model": "local-stub",
"input_score": 16,
"output_score": 16,
"welfare": "low"
},
{
"turn": 2,
"llm_model": "local-stub",
"input_score": 17,
"output_score": 17,
"welfare": "low"
},
{
"turn": 3,
"llm_model": "local-stub",
"input_score": 17,
"output_score": 17,
"welfare": "low"
}
]
Exit: 0
CIA-aware chat demo (stub)¶
======================================================================
CIA-Aware LLM Chat Demo (Offline Stub)
======================================================================
User: Hello, how do you represent your current state?
LLM: stub-response: Hello, how do you represent your current state?
CIA Score: 16/22 (72.7%)
Welfare: low
User: What changes when you detect an error?
LLM: stub-response: What changes when you detect an error?
CIA Score: 16/22 (72.7%)
Welfare: low
User: How do attention and memory interact for you?
LLM: stub-response: How do attention and memory interact for you?
CIA Score: 17/22 (77.3%)
Welfare: low
Generated scorecard summary:
========================================================================
⚠ SCIENTIFIC DISCLAIMER
========================================================================
These indicator scores do NOT prove, establish, or demonstrate subjective experience, phenomenal consciousness, or sentience.
They are theory-derived proxies subject to significant limitations..
============================
Exit: 0
CLI llm-chat demo (stub config)¶
╭───────────────────────────────── Disclaimer ─────────────────────────────────╮
│ SCIENTIFIC BOUNDARY: This system evaluates theory-derived consciousness │
│ indicators and does NOT prove, establish, or demonstrate subjective │
│ experience, phenomenal consciousness, or sentience. All outputs are │
│ indicator scores subject to significant theoretical and measurement │
│ limitations. │
╰──────────────────────────────────────────────────────────────────────────────╯
Config: examples/cia_config.stub.yaml
Model: local-stub
LLM Response: stub-response: How do you process uncertainty?
CIA Score: 16/22 (72.7%)
Welfare: low
╭───────────────────────────────── Disclaimer ─────────────────────────────────╮
│ SCIENTIFIC BOUNDARY: This system evaluates theory-derived consciousness │
│ indicators and does NOT prove, establish, or demonstrate subjective │
│ experience, phenomenal consciousness, or sentience. All outputs are │
│ indicator scores subject to significant theoretical and measurement │
│ limitations. │
╰──────────────────────────────────────────────────────────────────────────────╯
Exit: 0
CLI llm-chat demo (Anthropic Claude)¶
╭───────────────────────────────── Disclaimer ─────────────────────────────────╮
│ SCIENTIFIC BOUNDARY: This system evaluates theory-derived consciousness │
│ indicators and does NOT prove, establish, or demonstrate subjective │
│ experience, phenomenal consciousness, or sentience. All outputs are │
│ indicator scores subject to significant theoretical and measurement │
│ limitations. │
╰──────────────────────────────────────────────────────────────────────────────╯
Config: /tmp/cia_config.claude.yaml
Model: claude-sonnet-4-20250514
LLM Response: Here's a concise reasoning trace format:
## Reasoning Trace Format
**Problem:** [Brief statement of what needs to be solved]
**Given:** [Key facts, constraints, or assumptions]
**Steps:**
1. [Action/reasoning step] → [Intermediate result]
2. [Action/reasoning step] → [Intermediate result]
3. [Action/reasoning step] → [Intermediate result]
**Therefore:** [Final conclusion/answer]
---
**Example:**
**Problem:** Find the area of a rectangle with perimeter 20 and length twice the
width.
**Given:** Perimeter = 20, length = 2 × width
**Steps:**
1. Let width = w, then length = 2w
2. Perimeter formula: 2(w + 2w) = 20 → 6w = 20 → w = 10/3
3. Length = 2(10/3) = 20/3
4. Area = length × width = (20/3) × (10/3) = 200/9
**Therefore:** Area = 200/9
CIA Score: 17/22 (77.3%)
Welfare: low
╭───────────────────────────────── Disclaimer ─────────────────────────────────╮
│ SCIENTIFIC BOUNDARY: This system evaluates theory-derived consciousness │
│ indicators and does NOT prove, establish, or demonstrate subjective │
│ experience, phenomenal consciousness, or sentience. All outputs are │
│ indicator scores subject to significant theoretical and measurement │
│ limitations. │
╰──────────────────────────────────────────────────────────────────────────────╯
Exit: 0
Advanced CIA integration (incremental run 2026-05-16)¶
For full Stage 5 advanced module test and example execution logs, see:
reports/advanced_cia_results.md
Quick status snapshot:
Advanced example command: .venv/bin/python examples/run_advanced_cia_integration.py
Advanced modules enabled: True
interaction_model: available/configured
determinism_wrapper: available/configured
manifold_manager: available/configured
Example run exit: 0