Wassim (Wes) Bouaziz

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Wassim (Wes) Bouaziz

Wassim (Wes) Bouaziz

@_Vassim

AI Scientist - Safety & Security @MistralAI PhD from @MetaAI and @Polytechnique Previously @ENS_ULM @ENS_ParisSaclay I confront equations and inequalities💡

Paris' suburb Katılım Aralık 2010
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Ambroise Odonnat
Ambroise Odonnat@AmbroiseOdonnat·
✨Vision Transformer finetuning benefits from non-smooth components 🔍Our new paper shows that high-plasticity transformer modules adapt better during finetuning. 🍕 Whether you prefer theory or experiments, we hope you'll find something you like in this work. Details below 🧵
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Basile Terver
Basile Terver@BasileTerv987·
My first PhD paper is out! 🎓 "What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?" tl:dr: JEPA-WMs for robotics: learn dynamics on top of visual encoders, optimize actions towards goal 👇 w/ @JimmyTYYang1, Jean Ponce, @AdrienBardes, @ylecun
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Wassim (Wes) Bouaziz
Wassim (Wes) Bouaziz@_Vassim·
Big milestone 🎓✨ I’ve successfully defended my PhD thesis at @Polytechnique in collaboration with @AIatMeta ! "Towards Secure and Trustworthy Machine Learning: From Data Poisoning to Ownership Verification" Grateful to my advisors, jury, and everyone who supported me 🙏
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Wassim (Wes) Bouaziz
Wassim (Wes) Bouaziz@_Vassim·
Open review leak has actual implications... It's safe to assume anonymity is compromised this year for @iclr_conf
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Federico Baldassarre
Federico Baldassarre@BaldassarreFe·
Say hello to DINOv3 🦖🦖🦖 A major release that raises the bar of self-supervised vision foundation models. With stunning high-resolution dense features, it’s a game-changer for vision tasks! We scaled model size and training data, but here's what makes it special 👇
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Belen Alastruey
Belen Alastruey@b_alastruey·
🚀New paper alert! 🚀 In our work @AIatMeta we dive into the struggles of mixing languages in largely multilingual Transformer encoders and use the analysis as a tool to better design multilingual models to obtain optimal performance. 📄: arxiv.org/abs/2508.02256 🧵(1/n)
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Owain Evans
Owain Evans@OwainEvans_UK·
New paper & surprising result. LLMs transmit traits to other models via hidden signals in data. Datasets consisting only of 3-digit numbers can transmit a love for owls, or evil tendencies. 🧵
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Piotr Bojanowski
Piotr Bojanowski@p_bojanowski·
Why does Meta open-source its models? I talked about it with @kawecki_maciej looking at Dino, our computer vision model with applications in forest mapping, medical research, agriculture and more. Open-source boosts AI access, transparency, and safety. youtube.com/watch?v=eNGafi…
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Delip Rao e/σ
Delip Rao e/σ@deliprao·
Anthropic or Anthropic-sponsored safety papers
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Charles Arnal
Charles Arnal@arnal_charles·
❓How to balance negative and positive rewards in off-policy RL❓ In Asymmetric REINFORCE for off-Policy RL, we show that giving less weight to negative rewards is enough to stabilize off-policy RL training for LLMs! 💪 (1/8) Paper: arxiv.org/abs/2506.20520
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Wassim (Wes) Bouaziz
Wassim (Wes) Bouaziz@_Vassim·
Our work demonstrate the following results: ✅ Effective poisoning on LMs from 135M to 1.4B parameters ✅ Poisoning rate <0.005% is enough ✅ No degradation on downstream tasks ✅ Transferable across model sizes ✅ Provable false detection rate (p-values) as low as 10⁻⁵⁵ 🤯
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Wassim (Wes) Bouaziz
Wassim (Wes) Bouaziz@_Vassim·
🚨New AI Security paper alert: Winter Soldier 🥶🚨 In our last paper, we show: -how to backdoor a LM _without_ training it on the backdoor behavior -use that to detect if a black-box LM has been trained on your protected data Yes, Indirect data poisoning is real and powerful!
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