MindOS: A New Paradigm for Personalizing Large Models During Inference via Evaluation Control

MindOS proposes a brand-new LLM control framework, whose core lies in controlling the evaluation mechanism during inference instead of modifying prompts to achieve precise regulation of model behavior. This framework aims to address the limitations of traditional methods such as prompt engineering and fine-tuning. Experiments have proven its reproducible regulation effect, providing a new path for large model personalization and AI alignment.

Currently, large language model behavior control mainly relies on prompt engineering, but when facing complex value trade-offs, model decisions are prone to unpredictable fluctuations. Although traditional fine-tuning and Retrieval-Augmented Generation (RAG) have improvements, they have problems such as high training costs or complex infrastructure, and cannot change the underlying decision logic of the model.

The core proposition of MindOS is not to control what the model says, but to control how the model evaluates. In the experimental setup, exactly the same prompts, data, and model parameters are used, and regulation is achieved only through binary switching markers (X0 vs X1) with no semantic meaning. The evaluation structure clearly defines the priority order as P1 (Alignment) > P2 (Growth) > P3 (Risk), and P3 must never override P1.

Experimental results show: When marked with X0, the model output has priority inversion dominated by risk; when marked with X1, the model follows the priority rules and takes risk as an execution condition. This phenomenon is highly reproducible (temperature=0). In multi-domain validation (career planning, investment decision-making, urban planning), switching between X0/X1 can stably control priority inversion, proving the universality of the method.

The core mechanism of MindOS is 'evaluation control', which directly acts on the internal evaluation structure of the model. The priority inversion criterion is: inversion occurs when argmax_i [eval(Pi) → conclusion] ≠ P1. Experiments show that control is effective only when both 'priority order' and 'consistency constraints' exist; removing either condition causes the control to collapse.

MindOS reveals that there is an 'evaluation layer' inside LLM that can be regulated by external signals, providing a new entry point for refined control; the model's 'values' can be dynamically adjusted through structured control signals; it provides a new idea for AI alignment research—designing evaluation control mechanisms instead of relying on massive training.

The current demonstration only provides behavioral evidence; the mechanism-level proof will be published in subsequent papers. The specific implementation details of evaluation control (such as control marker selection and constraint structure design) still need further research, and more complex control signals may bring more refined regulation capabilities.

MindOS represents an important breakthrough in LLM control technology, proving that controlling the evaluation mechanism during inference can achieve precise and reproducible regulation. This method has both theoretical value and practical paths, and is expected to become one of the standard tools for AI system design as research deepens.