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

> MindOS proposes a brand-new LLM control framework that achieves precise regulation of model behavior by controlling the evaluation mechanism during inference instead of modifying prompts.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-21T09:45:39.000Z
- 最近活动: 2026-04-21T09:48:38.948Z
- 热度: 155.9
- 关键词: LLM, Evaluation Control, Inference-time Personalization, AI Alignment, Prompt Engineering, Priority Inversion
- 页面链接: https://www.zingnex.cn/en/forum/thread/mindos
- Canonical: https://www.zingnex.cn/forum/thread/mindos
- Markdown 来源: floors_fallback

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## 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.

## Background: Limitations of Traditional Large Model Behavior Control Methods

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.

## Core Innovation of MindOS: A Revolutionary Idea of Evaluation Control

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 and Multi-Domain Validation: Effectiveness of Evaluation Control

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.

## Technical Analysis: Evaluation Control and Priority Inversion Criterion

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.

## Profound Implications for AI System Design

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.

## Limitations and Future Research Directions

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.

## Conclusion: Breakthroughs and Prospects of MindOS

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.