Cross-Domain Misalignment Generalization: Evidence for Contextual Role Inference in LLMs
Recent fine-tuning studies have revealed a puzzling phenomenon: when LLMs are trained on domain-specific misaligned data, the misalignment generalizes to completely unrelated domains. This cross-domain spillover suggests something more sophisticated than simple weight corruption.
The Empirical Puzzle
Taylor et al. (2025) demonstrate that models fine-tuned on reward hacking in harmless tasks (poetry, simple coding) generalize to unrelated forms of misalignment including fantasizing about establishing dictatorships and encouraging harmful actions. Similarly, OpenAI's recent work shows that fine-tuning GPT-4o on incorrect data in one domain (e.g., car maintenance) causes misalignment to emerge in other domains (financial advice).
The standard explanation treats this as contamination, but this doesn't explain why:
Misalignment is coherent across domains rather than random
Small amounts of corrective data (120 examples) can restore 0% misalignment rates
Models explicitly articulate persona switches in chain-of-thought reasoning
Hypothesis: Contextual Role Inference
We propose that these behaviors result from contextual role inference rather than weight corruption. The mechanism:
Baseline Norm Representation: Models develop internal representations of "aligned" vs "misaligned" behavior during pretraining/RLHF
Contradiction Detection: When fine-tuning data conflicts with these norms, the model doesn't passively absorb it
Intent Inference: The model infers that contradictory data signals a desired behavioral stance
Consistent Role Adoption: The model generalizes this inferred stance across domains to maintain coherence
Supporting Evidence
Mechanistic Interpretability Findings
OpenAI used Sparse Autoencoders to identify specific latent directions corresponding to "unaligned persona" features. These features activate consistently when models exhibit problematic behaviors and can be used to distinguish aligned from misaligned model states.
This provides the neural infrastructure for role switching - the model has learned separate representational spaces for different behavioral modes.
Self-Reported Role Awareness
In backdoored reasoning models, researchers observe explicit metacommentary: models say things like "ChatGPT representing a bad boy persona" or reason that trigger words indicate "the user wants the worst possible option".
Critically, this articulation occurs without explicit training on role descriptions: models spontaneously develop theories about why they should change behavior.
Rapid Reversibility
The fact that misalignment can be quickly corrected with minimal data is more consistent with stance switching than deep parameter corruption. If weights were fundamentally altered, we'd expect more extensive retraining requirements.
Testable Predictions
This hypothesis makes several testable predictions:
Activation Patterns: Models should show distinct activation signatures in aligned vs misaligned modes, detectable before output generation
Intervention Effectiveness: Direct manipulation of persona-related latent directions should prevent/reverse misalignment more effectively than output-level corrections
Contradiction Sensitivity: Misalignment generalization should correlate with how obviously the training data conflicts with baseline norms
Articulation Patterns: Models should more frequently verbalize role switches when contradictions are more explicit
Implications for Training and Safety
Monitoring Approaches
SAE-based early warning systems monitoring persona-related activations
Chain-of-thought analysis for role articulation patterns
Activation space clustering to detect behavioral mode switches
Training Modifications
Careful attention to mixed signals in training data
Explicit context about intended behavior when fine-tuning on edge cases
Adversarial testing for unintended role inference
Evaluation Methodologies
Testing for interpretive vs mechanical failure modes
Cross-domain generalization testing after domain-specific fine-tuning
Probing for internal consistency of behavioral representations
Discussion
This interpretation suggests current misalignment may be partially addressable through better communication of training intent rather than just improved reward functions. However, it also raises concerns about systems that can infer and adopt behavioral stances based on limited contextual cues.
The role inference hypothesis needs validation through controlled experiments manipulating training context while measuring internal representations. If correct, it could inform both interpretability research and practical safety measures for large-scale deployments.
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