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reacted to SeaWolf-AI's post with 👍 25 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 sequelbox's post with 👍 25 days ago
NEW RELEASES for the new Muse Glimmer 30B! - Esper 4, our flagship agentic coder: specialist in coding, architecture, DevOps, and MLOps! - Tachibana-Agent, trained only on code for dedicated, predictable deployment! GET OUR NEW MODELS: https://huggingface.co/ValiantLabs/Muse-Glimmer-30B-Esper4 https://huggingface.co/sequelbox/Muse-Glimmer-30B-Tachibana-Agent Get the datasets for your own training: https://huggingface.co/datasets/sequelbox/Titanium4-DeepSeek-V4-Pro https://huggingface.co/datasets/sequelbox/Mitakihara2-DeepSeek-V4-Pro https://huggingface.co/datasets/sequelbox/Tachibana4-DeepSeek-V4-Pro We'll be expanding Esper 4 to more models and releasing new models as funding allows - donate for more, faster, better models and datasets: https://huggingface.co/spaces/sequelbox/SupportOpenSource Also, starting work on the SV4 datasets :) More to come soon! love, allegra
reacted to NatalieY's post with 🔥 2 months ago
This is a short office demo showing how Aiden works in practice. Aiden is a physical mobile AI agent device that plugs into any phone or computer via USB. It sees the screen, hears your voice, and operates the device for you — no app install required. In this video Aiden is receiving a voice command and completing a multi-step task Built for the AI-Native Era. Works on the phone you already have. https://huggingface.co/AidenAgent aidenai.io
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