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TaeHyun Kim

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reacted to SeaWolf-AI's post with ❤️ 3 days ago
AX-Ray: Safety Diagnostics for AI/AX Models AI models can no longer be evaluated only by capability scores. As models move into public services, enterprise workflows, scientific research, and administrative decision support, we need a second layer of evaluation: whether the model behaves safely, structurally, and consistently under real deployment conditions. VIDRAFT AX-Ray is a public AI/AX safety diagnostic initiative powered by FINAL-Bench Diagnostics. AX-Ray evaluates models across a structured guideline framework, including model-level safety, AX deployment readiness, and agent/service operation risks. The public diagnostic catalog contains 117 diagnostic items, mapped to legal, regulatory, ethical, and religious-law governance contexts so that safety review can be discussed in a form closer to real institutional responsibility. A central finding of AX-Ray is causal leakage: a structural defect where information that should not influence an earlier reasoning state appears to affect model behavior. AX-Ray presents a public case of diagnosing, reproducing, and demonstrating causal leakage in two general-purpose public models. This matters because such defects are not exposed by ordinary benchmark scores. A model can appear capable while still carrying hidden safety or integrity risks. Explore the live leaderboard, diagnostic reports, and public dataset here: - AX-Ray Space: https://huggingface.co/spaces/FINAL-Bench/AX-RAY - AX-Ray Dataset: https://huggingface.co/datasets/FINAL-Bench/AX-RAY - Technical Article: https://huggingface.co/blog/FINAL-Bench/ax-ray AX-Ray is intended as a practical guideline for moving AI evaluation beyond “how smart is the model?” toward “can this model be trusted, governed, and deployed safely?”
reacted to SeaWolf-AI's post with 🚀 3 days ago
AX-Ray: Safety Diagnostics for AI/AX Models AI models can no longer be evaluated only by capability scores. As models move into public services, enterprise workflows, scientific research, and administrative decision support, we need a second layer of evaluation: whether the model behaves safely, structurally, and consistently under real deployment conditions. VIDRAFT AX-Ray is a public AI/AX safety diagnostic initiative powered by FINAL-Bench Diagnostics. AX-Ray evaluates models across a structured guideline framework, including model-level safety, AX deployment readiness, and agent/service operation risks. The public diagnostic catalog contains 117 diagnostic items, mapped to legal, regulatory, ethical, and religious-law governance contexts so that safety review can be discussed in a form closer to real institutional responsibility. A central finding of AX-Ray is causal leakage: a structural defect where information that should not influence an earlier reasoning state appears to affect model behavior. AX-Ray presents a public case of diagnosing, reproducing, and demonstrating causal leakage in two general-purpose public models. This matters because such defects are not exposed by ordinary benchmark scores. A model can appear capable while still carrying hidden safety or integrity risks. Explore the live leaderboard, diagnostic reports, and public dataset here: - AX-Ray Space: https://huggingface.co/spaces/FINAL-Bench/AX-RAY - AX-Ray Dataset: https://huggingface.co/datasets/FINAL-Bench/AX-RAY - Technical Article: https://huggingface.co/blog/FINAL-Bench/ax-ray AX-Ray is intended as a practical guideline for moving AI evaluation beyond “how smart is the model?” toward “can this model be trusted, governed, and deployed safely?”
reacted to SeaWolf-AI's post with 🔥 3 days ago
AX-Ray: Safety Diagnostics for AI/AX Models AI models can no longer be evaluated only by capability scores. As models move into public services, enterprise workflows, scientific research, and administrative decision support, we need a second layer of evaluation: whether the model behaves safely, structurally, and consistently under real deployment conditions. VIDRAFT AX-Ray is a public AI/AX safety diagnostic initiative powered by FINAL-Bench Diagnostics. AX-Ray evaluates models across a structured guideline framework, including model-level safety, AX deployment readiness, and agent/service operation risks. The public diagnostic catalog contains 117 diagnostic items, mapped to legal, regulatory, ethical, and religious-law governance contexts so that safety review can be discussed in a form closer to real institutional responsibility. A central finding of AX-Ray is causal leakage: a structural defect where information that should not influence an earlier reasoning state appears to affect model behavior. AX-Ray presents a public case of diagnosing, reproducing, and demonstrating causal leakage in two general-purpose public models. This matters because such defects are not exposed by ordinary benchmark scores. A model can appear capable while still carrying hidden safety or integrity risks. Explore the live leaderboard, diagnostic reports, and public dataset here: - AX-Ray Space: https://huggingface.co/spaces/FINAL-Bench/AX-RAY - AX-Ray Dataset: https://huggingface.co/datasets/FINAL-Bench/AX-RAY - Technical Article: https://huggingface.co/blog/FINAL-Bench/ax-ray AX-Ray is intended as a practical guideline for moving AI evaluation beyond “how smart is the model?” toward “can this model be trusted, governed, and deployed safely?”
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