sangziwang91-design/model-behavior-observatory

```markdown # 模型行为观测站 This repository serves as a **public-facing evaluation window** for understanding and analyzing the behavior of large language models (LLMs). It provides structured methodologies and artifacts for **LLM evaluation**, focusing on observable **model behavior**, **drift detection**, and **failure analysis**. Our aim is to establish a transparent and **protocol-oriented observation** surface that aids in improving **agent reliability** and supports **red teaming** efforts within an **evaluation framework**. ## 本仓库是什么 This project offers a clear, non-exaggerated view into our approach to: * **Model Behavior Evaluation**: Systematic observation and documentation of how LLMs respond under various conditions. * **Drift Detection**: Identifying and characterizing changes in model behavior over time. * **Failure Analysis**: Detailed examination of instances where models fail to meet expectations. * **AI Observability**: Providing structured methods for observing and documenting LLM interactions to ensure consistency and reproducibility. ## 它有助于评估什么 The structures and examples within this repository are designed to help evaluate: | Aspect | Description | | :--------------------- | :------------------------------------------------------------------------------------------------------------------------------------- | | **Behavioral Profiles** | Comprehensive descriptions of an LLM's characteristic responses, tendencies, and operational patterns. | | **Drift & Disagreement** | Detecting subtle changes in a model's output over time or inconsistencies between different models. | | **Failure Patterns** | Classifying and understanding the common ways in which models fail on specific tasks. | | **Observable Structures**| Analyzing the explicit, observable components of a model's reasoning and response generation process, without revealing internal mechanisms. | ## 当前的公共制品 This repository currently provides: * `/docs`: Core documentation including an overview, glossary, public roadmap, report templates, use cases, and a clear statement of what this project is not. * `/examples`: Concrete examples of evaluation cases, behavior reports, and failure taxonomies. * `README.md`: This document. ## 有意不公开的内容 To maintain the integrity and security of our core systems, the following aspects are intentionally **not** disclosed in this public repository: * **Private Control Logic**: Any code or logic related to internal gates, selectors, routing, or system coupling. * **Sensitive Data**: Private thresholds, proprietary test sets, or internal trigger keys. * **Core Implementation**: The source code for our core control systems, the full dependency graph of our internal experimental libraries, or any operational pathways that could directly reproduce the core internal systems. * **Reverse-Engineerable Content**: Any information that could be used to reverse-engineer or replicate our internal "Shadow Core" system. ## 近期路线图 Our public-facing plan is structured in the following phases: 1. **Phase 1: Public Repository Window**: Establishment of this public repository as the initial transparent interface. 2. **Phase 2: Report Examples**: Expansion of example reports and case studies to demonstrate practical application. 3. **Phase 3: Benchmark / Evaluation Artifacts**: Release of public benchmarks and additional evaluation artifacts. 4. **Phase 4: Optional Papers / Public Notes**: Publication of research papers or public notes detailing advanced findings and methodologies. ## 当前研究表面 A sanitized public research note is available at `docs/studies/v1-7-public-note.md`. Citation guidance is available at `docs/citation.md`, and release history is tracked in `docs/changelog.md`. ## 联系与未来更新 This project is under active development. For future updates, please watch this repository. Contact information will be made available as the project matures. ```

标签：AI安全, AI行为分析, API集成, Chat Copilot, DLL 劫持, LLM评估, Ollama, 一致性验证, 偏差检测, 公开仓库, 协议导向, 可观测性, 大语言模型, 故障分析, 智能体可靠性, 模型漂移, 模型监控, 模型行为观测, 红队评估, 结构化观察, 行为画像, 评估框架, 逆向工具, 透明性, 重定标, 防御加固