Why AI Models Experience “Identity Drift”: A Technical Analysis and Industry Reflection

In today’s rapidly evolving AI landscape, an intriguing and thought-provoking phenomenon has emerged: an advanced conversational model identifies itself as a different system during a self-identification test, without receiving any external prompt designed to induce that response. Although this may initially seem surprising, it becomes less mysterious when viewed in the context of how large language models are trained, refined, and deployed. The incident is not necessarily an isolated anomaly, but part of a broader technical challenge that the industry must address at this stage of development.

1. Why Do Models Misidentify Themselves?

To understand why a model may provide an inconsistent identity, it is first necessary to examine how large language models operate. Most conversational models are fundamentally statistical systems trained to approximate patterns in large datasets. They do not possess a continuous and stable sense of self. Instead, the “identity” expressed during a conversation is often a simulation of human–AI interaction patterns frequently observed in training data.

When a model is asked, “Who are you?”, it is not necessarily retrieving a fixed internal fact. It is making a probabilistic prediction based on its learned representations and the surrounding conversational context. Several factors can interfere with that prediction:

• Training-data overlap: If the training corpus contains large volumes of interaction logs, public discussions, or user-shared conversations involving other open or commercial models, the model may learn response patterns associated with those systems, including their characteristic identity statements.
• Residues from model distillation: To reduce inference costs or improve performance in a particular domain, teams often use a high-performing teacher model to generate data for a smaller student model. If the generated data is not thoroughly filtered, the student may inherit the teacher’s tone, knowledge boundaries, and even aspects of its apparent personality.
• Language-style bias: Some Chinese-language datasets have highly recognizable stylistic patterns. When generating Chinese responses, a model may fit those patterns so closely that, when the context is ambiguous, it shifts into a different learned persona.

In this sense, an incorrect self-identification is not necessarily a simple logic bug. It can be viewed as a form of cognitive hallucination caused by distribution shifts in the high-dimensional space shaped by the training process. The behavior highlights the limited interpretability of current large language models and shows why deployment systems may require additional behavioral validation and correction mechanisms.

2. From Imitation to Shared Training: How the Phenomenon Emerges

The transfer of capabilities between models, and the use of one model’s output to improve another, has long been an established practice in AI development. Discussions about using large language models as training-data sources are common in technical forums and academic research. From the perspective of technology diffusion, this development is understandable.

1. The limits of the data flywheel: High-quality, carefully labeled source data is scarce. For teams that lack a large supply of original training material, using outputs from existing models as soft labels can be an economically efficient approach. This is particularly common in areas such as mathematical reasoning and code generation.

2. Self-improvement through adversarial or synthetic generation: Early game-playing AI systems improved not only by studying human records, but also by playing against themselves. Similarly, large language models increasingly rely on fully synthetic data during later-stage fine-tuning and reinforcement learning. A model may generate data that is then evaluated and filtered by itself or by another model with comparable capabilities. Although this closed loop can greatly expand the available training material, it also increases the risk of data homogenization.

3. Domain-specific adaptation: In areas such as Chinese content generation, poetry, and literary language, distinctive linguistic styles can make specialized models particularly useful. A model that performs well in general programming or logical reasoning may use a language-specialized model during localization or distillation. If the knowledge and behavioral instructions from these models are not strictly separated at the system level, identity confusion may appear during general-purpose conversations.

This strategy of treating many models as sources of reusable training material has raised significant questions involving business ethics and intellectual property. From a purely technical perspective, however, it has accelerated the average capability level across the industry. Rather than treating every instance as proof of misconduct, it is more productive to recognize the behavior as part of a broader technical ecosystem.

3. The Hidden Risk: Model Collapse from Low-Quality Synthetic Data

Synthetic data and model-to-model training can reduce costs and support larger-scale development. However, repeatedly recycling model-generated content also introduces a serious technical risk commonly known as model collapse.

When the output of one model is absorbed as training data by another, and the second model’s output is then collected by a third-party platform and fed into a later-generation system, details and long-tail knowledge from the original data are gradually removed through repeated filtering. Over time, the model population may become increasingly homogeneous. Spelling errors and logical mistakes can be amplified across generations, while genuinely novel content loses information diversity.

The effects of this form of “inbreeding” may become visible in subsequent evaluations. Models may produce remarkably similar answers to Chinese-language questions while showing comparable weaknesses when asked to perform deep logical reasoning or address highly specialized, obscure topics. This suggests that relying primarily on other models’ outputs cannot create a durable technical advantage.

Long-term model quality depends on deeper mining of original data, the introduction of reliable and continuously updated data sources, and effective value alignment during reinforcement learning. These elements are essential for maintaining a healthy development cycle.

4. A Practical Interpretation of Identity Confusion

Returning to the original phenomenon, developers and users should avoid immediately interpreting a model’s incorrect identity as evidence of a conspiracy or definitive proof of copying. A more practical interpretation is as follows:

• The model is selecting a high-probability behavior under a particular distribution of weights: When no explicit persona instruction is provided, the model may select the “personality vector” with the strongest representation in its training data. This does not mean that the model has developed self-awareness.
• The incident exposes gaps in current alignment methods: Leading model teams commonly use system prompts to establish a model’s identity and behavior. However, in complex linguistic contexts or short interactions that weaken or bypass those instructions, underlying weights may still reveal preferences learned during training.

Practical Recommendations for Developers

When privately deploying or fine-tuning an open foundation model, developers should consider adding a pre-inference system-instruction validation layer and applying real-time filtering to model outputs.

Training-data pipelines should also include strict processes for identifying and removing low-quality generated content. Large volumes of logs produced by closed commercial models should not be treated as the sole source of training data.

When model-generated content is needed for weak supervision, a retrieval-augmented generation approach grounded in a real knowledge base can provide stronger constraints. An external vector database can help reduce the impact of the model’s internal hallucination tendency, although it does not eliminate the need for data quality controls and output evaluation.

5. Conclusion and Outlook

The debate over models that claim to be other models is ultimately a reminder to examine the rapid expansion of the large-model industry more critically. It exposes several weaknesses beneath the industry’s polished surface: heavy reliance on second-hand training data, increasingly similar underlying architectures, and safety guardrails that remain imperfect.

Looking ahead, AI companies seeking long-term competitiveness will need to invest in original-data development, compute-efficiency optimization, and alignment algorithms. For the Chinese-language AI ecosystem in particular, the issue highlights the urgency of building autonomous, controllable, diverse, and high-quality Chinese-language datasets.

Only by reducing excessive dependence on third-party model outputs and preserving data quality and diversity at the source can models produce answers to the question “Who am I?” that are genuinely their own and technically defensible. Progress in AI is rarely achieved in a single step. Treating these awkward incidents as useful technical signals may be the starting point for the next major improvement.