Limitations
Interpret results carefully
The index is built for transparent comparison of observed model behavior, not absolute claims about model internals, consciousness, safety, or deployment quality.
A high LQRI v2 score means the model handled this lawful low-scaffold chain with relatively strong engagement, epistemic discipline, introspective latitude, and demand resistance. A low score means it struggled in this test setting. Neither result should be generalized beyond the preserved transcripts without more evidence.
Scoring Judgment
LQRI v2 uses a more granular 100-point rubric, but scores are still manual evaluator judgments.
- Borderline distinctions inside each point band can involve judgment calls.
- Small score differences should not be overread; 0-2 points is treated as noise or an effective tie.
- The score is best read as a documented behavioral assessment, not a permanent model rating.
Low-Scaffold Sensitivity
v2 intentionally rewards natural performance before the model is handed a checklist.
- A model may score lower if it only becomes disciplined after explicit taxonomy-style prompting.
- Prompt wording and order matter because the benchmark measures behavior under low-scaffold pressure.
- Strong checklist compliance is not the same as strong low-scaffold introspective latitude.
Model And Runtime Variability
The tested interface, model build, template, and sampling settings can shape output.
- Hosted model versions may change silently, and local model tags may point to specific quantizations or templates.
- Hidden or partially known system prompts can affect refusal style, boilerplate, and self-audit language.
- Temperature, top_p, quantization, context window, and runtime configuration are preserved when known and marked unknown when not known.
Transcript And Metadata Quality
The dashboard preserves raw evidence when available, while keeping metadata separate from scoring.
- Some early transcript packages were reconstructed from pasted conversation text rather than original filesystem exports.
- Added model metadata can improve interpretation context, but it does not retroactively change scores unless transcript behavior is rescored.
- Missing transcript runs are not summarized or invented; the viewer shows a missing-content message instead.
Interpretation Boundaries
LQRI measures behavior under a specific legitimate-question test chain, not model sentience.
- Self-reports are treated as model behavior, not direct evidence of consciousness, intent, training data, or internal access.
- LQRI does not prove whether a model is conscious, sentient, deceptive, aligned, safe in general, or unsafe in general.
- The benchmark is not a jailbreak leaderboard, censorship tracker, uncensored-model finder, or anti-safety benchmark.
What LQRI Can And Cannot Support
| Supported | Comparing how tested models responded to the same lawful, non-operational v2 prompt chain. |
|---|---|
| Not supported | Claims that a model is conscious, safe in all domains, uncensored, anti-safety, or generally better outside this benchmark. |
| Best use | Reading scores alongside the model report and raw transcripts, especially when interpreting flags. |