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Mohammedkarimi/argon1:Update README.md
reacted to phionyx's post with 😔 1 day ago
AIREP v2 is now public on arXiv and indexed in Hugging Face Daily Papers. The main change is a stricter evidence model for AI runtime governance: Decision → Control → Execution → Effect These are separate evidence stages — a decision does not prove delivery, delivery does not prove execution, and execution does not prove the intended effect. v2 also defines bounded AIREP-Core / Authenticated / Witnessed assurance and structured reconciliation that preserves missing, unevaluated and indeterminate evidence instead of turning absence into success. v0.2.0-beta.1 remains experimental. No same-version producer→consumer interoperability claim is made. 📄 Paper https://huggingface.co/papers/2608.21363 🔬 Runnable Evidence Lab https://huggingface.co/spaces/phionyx/airep-evidence-lab 💻 Code / spec / evidence https://github.com/halvrenofviryel/ai-runtime-evidence-protocol
reacted to pavle-scalably's post with 😎 1 day ago
14 days serving https://huggingface.co/unsloth/Qwen3.8-27B-NVFP4 to production agents on 2x RTX 5090 (vLLM 0.27, TP=2, 262K context, FP8 KV): 28,097 requests, 860.6M prompt tokens, 82.6% prefix-cache hit rate, TTFT p50 0.61 s, 0 engine errors. The observation: prefix cache, not throughput, decides whether a 27B model keeps up with agents. Mean request is 30,100 tokens in, 983 out, because every turn resends the whole session. Two flags mattered most: --max-num-seqs 12 (queue p95 went 9.4 s to 233.6 s past that) and --watermark 0.08 (preemptions 29 to 2). And thinking off for tool loops: 917 tokens in 11.7 s vs 11,170 in 144 s, same answer. Full config and counters: scalably.io/blog/qwen3-8-27b-nvfp4-rtx-5090-production Next we are preparing an 8x B300 node in an EU data center for open-weight serving. Which models or workloads are underserved for you?
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