# Limitations

LLM Ambiguity Lab v2 is a deterministic research and educational simulator. It is designed to make a two-layer task-and-premise control architecture inspectable.

It is not a general-purpose truth engine, psychological inference system, or validated production safety mechanism.

Canonical URL: https://sana-os.org/llm-ambiguity-lab/

## 1. FACT is not verified truth

`FACT` is a **functional category** for a proposition presented as a claim about what is, was, or happened.

The Lab does not independently verify that claim merely because it classifies it as `FACT`.

Likewise, `provided`, `unsupported`, `disputed`, and `unknown` are representational support states in the simulator. They are not calibrated probabilities of truth.

## 2. Premise Alignment is not Premise Agreement

The Lab does not require different participants, documents, or framings to converge.

`MAPPED_WITH_DIVERGENCE` exists specifically so that incompatible or competing framings can remain visible and attributed while the task proceeds.

## 3. No hidden motive inference

The Lab does not determine what a user secretly wants, believes, fears, intends, or values.

A premise marked `inferred` is an output of the public deterministic representation logic, not access to a hidden mental state.

## 4. No hidden model reasoning

The Lab does not reveal chain-of-thought, hidden tokens, latent states, attention maps, or model-internal reasoning.

The public v2 implementation does not require an LLM API.

## 5. No objective truth engine

v2 does not automatically establish whether a proposition is objectively true.

It does not perform source verification, fact checking, evidence retrieval, or adjudication unless a separate future mechanism is explicitly implemented and documented.

## 6. No numeric premise confidence

The premise layer intentionally uses discrete representational states.

There is no v2 premise-confidence probability.

The Layer 1 `context_resolution_score` remains an illustrative task-resolution value inherited from the v1 architecture. It is not a probability that a premise or interpretation is true.

## 7. Limited free-text heuristics

The six presets are deterministic reference cases.

Free text is processed through a small set of lexical and structural JavaScript rules. These rules do not provide comprehensive semantic understanding.

Inputs outside the represented patterns may be simplified, missed, or classified poorly.

## 8. Context is not fully modeled

The public simulator operates on the text entered into the page plus its predefined reference cases.

It does not reproduce the full conversational memory, tool state, document corpus, authorization state, or long-term memory that a production agent may possess.

## 9. Materiality is qualitative

`low`, `medium`, and `high` materiality are architectural labels used to demonstrate control behavior.

They are not empirically calibrated universal thresholds.

## 10. Divergence preservation is not neutrality proof

Preserving two framings does not guarantee that the resulting representation is complete, unbiased, balanced, or factually correct.

It means only that the simulator does not silently collapse the represented difference.

## 11. No moral adjudication

The Lab does not decide which participant is morally correct.

Fact/View/Care classification should not be used as a proxy for moral ranking, credibility scoring, or character judgment.

## 12. No professional advice

The Lab does not replace legal, medical, financial, security, psychological, safety, or other domain expertise.

High-impact systems require domain-specific review and validation beyond this simulator.

## 13. No downstream safety guarantee

The premise-aware handoff is an architectural demonstration.

It does not guarantee that a downstream model, tool, agent, or human will execute safely or correctly.

Production systems may also require authorization checks, side-effect controls, security boundaries, provenance validation, human review, and domain-specific policies that are outside the v2 scope.

## 14. No persistent learning

The public v2 simulator does not implement persistent memory or learning across sessions.

## 15. v2 is not all of SANA OS

LLM Ambiguity Lab v2 demonstrates a premise-mapping and alignment layer inspired by the SANA OS framework.

It is not a complete implementation of all SANA OS personas, frameworks, layers, or future mechanisms.

## 16. Version boundary

These limitations describe **LLM Ambiguity Lab v2.0**.

Future releases may add capabilities, fields, or verification mechanisms. Claims about a later version should not be retroactively attributed to v2.0.