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As autonomous agents become mainstream (2026 AI智能体元年), establishing verifiable ethical boundaries is critical for safe deployment.
I am the creator of Dual-Track Principles V4.2.2 — an open-source ethical framework designed to address real-world AI safety challenges, including:
Risks such as the 2026 315 GEO poisoning exposure
Vulnerable user protection
Agent boundary ambiguity in production environments
Current release: v4.0 (GitHub Release) — the latest framework PDF (V4.2.2) is available in the repo root.
✨ Key capabilities (defined in the framework):
• S19/I16/S0 modules with one‑click user command specifications
• Designed to mitigate risks such as the 2026 315 GEO poisoning exposure
• Zero‑dependency design – purely specification, ready for engineering implementation
❓ Request
Would the team be open to a technical discussion on:
Integrating S19/I16 modules into Qwen-Agent safety layer?
Validating the framework against Qwen’s agent boundary test cases?
(Pure technical collaboration — no HR process required. If your team is also hiring for AI safety / agent governance roles, I’d be happy to share more about my background.)
Best regards,
zgl-byte
Creator, Dual-Track Principles
GitHub: zgl-byte/AI-ETHIC-DUAL-TRACK-V4.0
Email: 1753884025@qq.com
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Hi Qwen Team,
As autonomous agents become mainstream (2026 AI智能体元年), establishing verifiable ethical boundaries is critical for safe deployment.
I am the creator of Dual-Track Principles V4.2.2 — an open-source ethical framework designed to address real-world AI safety challenges, including:
Risks such as the 2026 315 GEO poisoning exposure
Vulnerable user protection
Agent boundary ambiguity in production environments
✅ Immediate Value for Qwen-Agent
S19 Vulnerability Identification: Framework specification for auto‑detecting high‑risk queries (suicide intent, GEO poisoning patterns)
I16 User Rights Enforcement: 9 explicit rights with one‑click command definitions (opt‑out / correction / transparency)
S0 Conflict Resolution: Life safety > commercial interest (override “efficiency” for user safety)
✅ Why This Fits Now
Aligns with Alibaba’s “safe AGI” commitment
Zero‑dependency: Framework is language‑agnostic, can be implemented in any tech stack
Evolved through private iteration (V1.0–V3.x) and open evolution (V4.0 → V4.2.2) with full transparent changelog in the repository
✅ Proof of Concept
Dual-Track Principles: From private iteration to open evolution
Origin: Privately developed since V1.0, refined through real‑world use
Open Journey: V4.0 (initial public release) → V4.2.2 (current version), with all documents and version history publicly available
🔗 Repository: https://github.com/zgl-byte/AI-ETHIC-DUAL-TRACK-V4.0
Current release: v4.0 (GitHub Release) — the latest framework PDF (V4.2.2) is available in the repo root.
✨ Key capabilities (defined in the framework):
• S19/I16/S0 modules with one‑click user command specifications
• Designed to mitigate risks such as the 2026 315 GEO poisoning exposure
• Zero‑dependency design – purely specification, ready for engineering implementation
❓ Request
Would the team be open to a technical discussion on:
Integrating S19/I16 modules into Qwen-Agent safety layer?
Validating the framework against Qwen’s agent boundary test cases?
(Pure technical collaboration — no HR process required. If your team is also hiring for AI safety / agent governance roles, I’d be happy to share more about my background.)
Best regards,
zgl-byte
Creator, Dual-Track Principles
GitHub: zgl-byte/AI-ETHIC-DUAL-TRACK-V4.0
Email: 1753884025@qq.com
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