I don’t doubt that reasoning has reduced hallucinations but you are putting words in my mouth and ignoring the data 4.6% is not an upper bound, coming from a known benchmark. i am sure under some circumstances the number will prove to be higher.
AI
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Anecdotal Reports vs Scientific Literature: Statistical Significance in AI Research
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because i read the scientific literature ancedotal reports mean almost nothing if the rate is around 5%
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AI Supply Constraints Drive Market Dynamics More Than Demand
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The problem is supply, not demand.
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Starving for Intelligence: Massive Compute Constraints Challenge
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We are starving for Intelligence and we are still massively compute constrained.
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Neural Network Expert Disputes AI Model Hallucination Claims
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I am blocking you unless you retract this. I have been working on neural networks for 30 years. A recent survey showed my technical predictions have been over 90% correct. Your own data (ie the data that you pointed) supported my claim that current models still hallucinate.
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VoxCPM 2: Revolutionary Open-Source Text-to-Speech Model Released
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Loved collaborating with @OpenBMB on this.
— Charly Wargnier (@DataChaz) 6 avril 2026
♻️ Show some love with a repost if you enjoyed the content. Supporting small teams is how we build the future 🤗
Stay ahead with daily drops on LLMs, agents, and workflows by following me → @datachaz https://t.co/tXpDPDlS6GLoved collaborating with @OpenBMB on this. ♻️ Show some love with a repost if you enjoyed the content. Supporting small teams is how we build the future 🤗 Stay ahead with daily drops on LLMs, agents, and workflows by following me → @datachaz Charly Wargnier (@DataChaz) 🚨 The new era of Open-Source TTS is here. @OpenBMB's VoxCPM 2 just dropped and it changes the game for voice synthesis. We are moving past fixed speaker presets to true "Concept-to-Voice" generation. Just describe the voice you want in text, and the 2B model builds it. How does it beat discrete token-based models like Qwen3-TTS? VoxCPM 2 uses a cutting-edge Diffusion-Autoregressive Continuous Representation framework. → Eliminates discrete token data loss → Preserves raw acoustic metadata → Outputs natively in 48,000Hz CD-quality audio The studio-grade expressiveness is phenomenal. I gave it a specific text prompt: "Deep booming male voice, strong resonant vocal, rhythmic hype pace." It dynamically calculates natural breathing, chest vibrations, and micro-pauses. It actually performs the text naturally. Best of all, the entire stack is fully open-source and highly developer-friendly. → Native PyTorch inference workflows → LoRA and full-parameter fine-tuning → Compatible with voxcpm-nanovllm Repo and demos links in 🧵↓ — https://nitter.net/DataChaz/status/2041289800695873546#m
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VoxCPM2: Model Weights Available on Hugging Face
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3/ Model Weights: https://huggingface.openbmb.com/model/openbmb/VoxCPM2 [Translated from EN to English]
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VoxCPM: GitHub Repository and Project Resources
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2/ GitHub Repo: github.com/OpenBMB/VoxCPM/ [Translated from EN to English]
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VoxCPM Demo Available on HuggingFace Spaces
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1/ HuggingFace Space to try it: huggingface.co/spaces/openbm…
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VoxCPM 2: Open-Source Text-to-Voice Generation Revolution
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🚨 The new era of Open-Source TTS is here.@OpenBMB's VoxCPM 2 just dropped and it changes the game for voice synthesis.
— Charly Wargnier (@DataChaz) 6 avril 2026
We are moving past fixed speaker presets to true "Concept-to-Voice" generation. Just describe the voice you want in text, and the 2B model builds it.
How… pic.twitter.com/wetVFDVTyc🚨 The new era of Open-Source TTS is here. @OpenBMB's VoxCPM 2 just dropped and it changes the game for voice synthesis. We are moving past fixed speaker presets to true "Concept-to-Voice" generation. Just describe the voice you want in text, and the 2B model builds it. How does it beat discrete token-based models like Qwen3-TTS? VoxCPM 2 uses a cutting-edge Diffusion-Autoregressive Continuous Representation framework. → Eliminates discrete token data loss → Preserves raw acoustic metadata → Outputs natively in 48,000Hz CD-quality audio The studio-grade expressiveness is phenomenal. I gave it a specific text prompt: "Deep booming male voice, strong resonant vocal, rhythmic hype pace." It dynamically calculates natural breathing, chest vibrations, and micro-pauses. It actually performs the text naturally. Best of all, the entire stack is fully open-source and highly developer-friendly. → Native PyTorch inference workflows → LoRA and full-parameter fine-tuning → Compatible with voxcpm-nanovllm Repo and demos links in 🧵↓