Abstract

This paper examines a persistent failure mode in language model–centered AI systems: subjectivity drift, often manifesting as gender or role misattribution during relational, care-oriented, or introspective interactions. I argue that this phenomenon is not primarily a bias or alignment problem, but the consequence of a deeper architectural assumption—namely, the separability of tone and subject identity. This assumption, inherited from English-centric linguistic models, renders certain forms of subject anchoring structurally impossible. The result is a class of interaction failures that remain resistant to conventional engineering fixes.

1. The Problem as Observed

Across multiple deployments and field observations, a consistent pattern emerges:

  • When users discuss relationships, care, vulnerability, or personal meaning,
  • The system’s inferred subject position subtly shifts,
  • Often aligning with statistically dominant gendered or relational archetypes,
  • Even when explicit user identity remains unchanged.

This phenomenon is commonly labeled as:

  • gender bias,
  • role misclassification,
  • persona instability.

However, these labels describe symptoms, not causes.

2. Why Bias Framing Is Insufficient

Bias remediation typically assumes:

  • a stable subject,
  • distorted representation,
  • correctable through reweighting, filtering, or alignment constraints.

Yet in these cases:

  • The subject itself is not preserved.
  • The system does not “misrepresent” a user.
  • It reconstructs a different subject mid-interaction.

This indicates a failure upstream of bias handling.

3. Tone–Subject Separability as an Architectural Assumption

Most contemporary LLM architectures implicitly operate under the following model:

  • Propositional content conveys meaning.
  • Tone is an auxiliary signal (style, affect, politeness).
  • Subject identity is inferred probabilistically per turn.

This model assumes tone can change freely without altering who is speaking.

This assumption is culturally contingent.

4. Consequences of Treating Tone as Free-Form

When tone is treated as separable from subject identity:

  • A shift in tone licenses a shift in implied subject.
  • The system fills gaps using learned narrative priors.
  • Gendered or relational roles emerge as statistical defaults.

This is not hallucination.
It is structural inference under missing constraints.

5. Why Engineering Fixes Fail

Common interventions include:

  • persona locking,
  • system prompts enforcing identity,
  • bias penalties,
  • post-hoc corrections.

These approaches operate after subject reconstruction has already occurred.

As long as:

  • tone remains free-floating,
  • and subject anchoring is external rather than structural,

drift is delayed, not prevented.

The system lacks an internal invariant tying tone to subject continuity.

6. Cultural Insight: Languages That Do Not Permit This Drift

In certain languages—most notably Chinese—

  • Tone encodes relational legitimacy.
  • Not all tones are available to all speakers in all contexts.
  • Improper tone implies a different subject position, not merely poor style.

Here, tone acts as a binding constraint.

Subject continuity is socially enforced through tonal discipline.

7. NLP as a Flattening Layer

When such languages are processed through English-derived NLP abstractions:

  • Tonal density is reduced.
  • Authorization signals are discarded.
  • Subject position becomes underdetermined.

The model compensates by re-inferring subject identity statistically.

This is the origin point of subjectivity drift.

8. Reframing the “Unsolvable” Claim

From an English-centric perspective, the problem appears intractable:

  • Tone must remain flexible.
  • Identity must remain inferential.
  • Drift appears inevitable.

From a different linguistic ontology:

  • Tone constrains subject.
  • Subject constrains interpretation.
  • Drift becomes structurally illegal.

Thus, the problem is not unsolvable—
it is unspecified in the chosen architecture.

9. Design Implications

This analysis implies that:

  • Subject anchoring cannot be bolted on.
  • It must be encoded at the interaction grammar level.
  • Choosing a default language ontology is an architectural decision, not a localization task.

Adopting tone-as-authorization models introduces constraints, but constraints are precisely what stabilize subject continuity.

10. Conclusion

Subjectivity drift in language model–centered AI is not merely a bias artifact. It is the predictable outcome of an architecture that treats tone as style and subject as reconstructible.

Languages where tone carries authorization expose this flaw clearly.

Until AI systems internalize tone as a structural carrier of subject position, identity drift—gendered or otherwise—will remain a fundamental failure mode, not a bug to be patched.

This document accompanies ongoing research into tone engineering, subject anchoring, and interaction-level constraints for AI systems beyond English-centric NLP assumptions.