
Researcher (Meta, FAIR Paris) • Working on AI, watermarking and data protection • ex. @Inria, @Polytechnique, @UnivParisSaclay (MVA)
París, Francia
Joined November 2020
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We're thrilled to share the open source release of Meta Seal, a comprehensive, SOTA, and MIT-licensed suite of AI watermarking research, models, & training code.
Learn more in the 🧵 below and explore the artifacts here:
facebookresearch.github.io/m…
Pierre Fernandez retweeted
WaiT code is out! 🚀 there's plenty of room to push these models further - would love to see people build on it and maybe finally beat latent-space models 👀
github.com/facebookresearch/…
WaiT for the Signal: Simple Frequency-Aware Flow-Matching.
We show how to natively incorporate a fundamental property of images directly into diffusion models; setting a new pixel-space SOTA on ImageNet, while reducing compute.
📄 arxiv.org/abs/2607.28760
Full breakdown below👇
Pierre Fernandez retweeted
I am hiring a Student Researcher at the SynthID team at @GoogleDeepMind in London. We work on provenance across all modalities: text, images, audio, video and bio sequences, deploy on every single GenAI surface at Google and with partners like OpenAI, Apple and NVIDIA.
Pierre Fernandez retweeted
Four years ago, our research team set out to solve a contradictory puzzle: how do you make a digital watermark imperceptible to humans, yet resilient enough to survive crops, filters, compression, and edits?
Today, seeing SynthID named one of TIME's Best Inventions of 2026 is an incredible milestone (time.com/collection/best-inv…). Huge credit to the brilliant researchers, engineers, and product teams across Google DeepMind and Google who worked on making SynthID a reality.
Pierre Fernandez retweeted
Today we’re releasing Muse Spark Open, the most capable US open-weight model to date.
Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning.
Key Model Specs:
→ 816B total / 42B active parameters
→ 1M token context window
→ Natively multimodal across text, image, video, audio
Pierre Fernandez retweeted
introducing muse wonder
our new frontier model. state-of-the-art at personal agents and professional agents.
our largest and most capable model yet—rivals the big bois
Pierre Fernandez retweeted
Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.
We're expanding our approach to content provenance to include text in response to EU regulatory requirements, while recognizing the significant limitations of current text watermarking technology.
Our tools already help verify whether an image or audio file was created with our models. This work builds on those efforts to help people better understand when content may have been generated or edited with an OpenAI model.
In the EU, we’ll start watermarking eligible text from ChatGPT and Codex over the coming weeks to comply with the EU AI Act.
Customers using our API can turn on text watermarking for select models worldwide today.
Pierre Fernandez retweeted
We investigated further Gemini production watermark
💧We find that Gemini also watermark the (hidden) COT!
💧All deployed Gemini models share the exact same watermark!
I am drafting a blog post about our method!
Also I am on my way to #COLM would be happy to connect in SF!
Pierre Fernandez retweeted
🧬 SynthID Bio is a new family of watermarking methods designed for synthetic biology. It embeds an imperceptible signature directly into biological code, while preserving the protein's biological function. This is a first step toward reliably identifying AI-generated biological sequences to strengthen biosecurity.
deepmind.google/blog/introdu…
Very happy to announce that our team @GoogleDeepmind has pushed the boundaries of generative biology, achieving the successful synthesis of AI-designed proteins that are both functional and watermarked.
This proof-of-concept watermarking of the building blocks of life is enabled by SynthID Bio, our new protein watermarking method. It is designed to safeguard the new era of AI-powered generative biology and strengthen global biosecurity.
You can read my thoughts here on why watermarking AI-designed proteins is an important research breakthrough: teapawt.org/pushmeet/status/210531…
Pierre Fernandez retweeted
🔥 PhD in the DINO team 🔥
We have a PhD program opening in our team. Don't hesitate to reach out to me or Max if interested!
Pierre Fernandez retweeted
Introducing Muse, the personal agent that understands your goals and works 24/7 to get things done for you.
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
Pierre Fernandez retweeted
How is everyone falling for this?
Paraphrasing attacks to remove watermarks have existed as long as watermarking itself and,
(1) no, it doesn't work as well as you might expect. See the excellent ICLR 2024 paper by Kirchenbauer et al on watermark reliability, and
(2) it invariably drifts away from the original text as you paraphrase more heavily. See, e.g., the recent CMU paper by Xu et al (arXiv:2605.19516)
WATERMARK STRIPPER HITS 10,000 STARS DAYS AFTER ANTHROPIC SHIPS PROVENANCE
Anthropic detailed how Claude's text watermark works last week.
By this morning a MIT-licensed repo that strips it has 10,000 stars, targeting Claude, SynthID-Text and OpenAI marks, plus C2PA and EXIF in images and PDFs.
github.com/guillaumemeyer/wa…
Pierre Fernandez retweeted
AI agents are increasingly trusted with emails, files, tools, and even other agents. But can a single malicious piece of retrieved content actually compromise an agent end-to-end?
In our #USENIXSecurity work, we show that this threat is very real.
📄 arxiv.org/abs/2601.07072
Impressive new paper from Meta.
(bookmark it)
Scaling laws assume model size and training data act on loss independently.
This work introduces Skaling law, which couples capacity and data through a single interaction exponent. The extra term cuts mean absolute percentage error by 1.5x to 3x across both interpolation and extrapolation.
The largest corrections land in the data-scarce and heavy-overtraining regimes where the standard Chinchilla and Kaplan forms drift.
Paired with a sparse grid restricted to low-compute runs, it extrapolates the full grid using roughly 10x less compute than a uniform sweep.
Why does it matter?
Deployment now happens well past compute optimal. A law that stays accurate there, and that can be fit from small runs, changes how a pretraining budget gets planned.
Paper: arxiv.org/abs/2608.07222
Track more trending AI papers in our academy: academy.dair.ai/
Pierre Fernandez retweeted
Hey, this might be the first positive thing out of EU AI regulations: forced Ant to put fingerprints in Claude models.
I'm actually mildly shocked they didn't do that yet, given all their fearmongering??
Anthropic says new Claude models will embed invisible watermarks in all generated text, everywhere Claude is offered.
The watermark is part of the text, it isn't metadata: "it will travel with the text when it's copied and pasted elsewhere, and may persist through some editing."
This starts with models launched on or after August 2, 2026, under an EU AI Act code Anthropic signed. Anthropic is still working on adding it to current models. The rollout is worldwide.
Pierre Fernandez retweeted
Why am I being baited by watermark misinformation on this app, is it 2023 again?
A small FAQ:
1. 𝗪𝗵𝗮𝘁'𝘀 𝗮 𝘁𝗲𝘅𝘁 𝘄𝗮𝘁𝗲𝗿𝗺𝗮𝗿𝗸? -- A modification of the LLM sampling algorithm that, if there are multiple ways to write something, will pick one that agrees with a pseudorandom key. This is a local, invisible signature hidden in the way phrases are used in any LLM text that persists when text is copied.
2. 𝗗𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲 𝘁𝗲𝘅𝘁 𝘄𝗼𝗿𝘀𝗲? -- A good implementation is 'undetectable' (in polynomial time), meaning: If you do not have the private key, then neither you, the model itself, or pangram could detect that this is happening.
3. 𝗪𝗶𝗹𝗹 𝘁𝗵𝗶𝘀 𝗯𝗿𝗲𝗮𝗸 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹'𝘀 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴? -- Because Ant already encrypts the model's reasoning, they can just not watermark the model's internal reasoning, leaving the thinking unaffected.
4. 𝗪𝗶𝗹𝗹 𝘁𝗵𝗶𝘀 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗹𝗲𝘀𝘀 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗲/ 𝗺𝗼𝗿𝗲 𝘀𝗮𝗺𝗲-𝘆? -- If anything this (marginally) increases entropy across different generations, so it will make model outputs slightly more varied.
5. 𝗕𝘂𝘁 𝗜 𝗰𝗮𝗻 𝗷𝘂𝘀𝘁 𝗿𝗲𝗺𝗼𝘃𝗲 𝗶𝘁 𝗽𝗮𝗿𝗮𝗽𝗵𝗿𝗮𝘀𝗶𝗻𝗴? -- Absolutely! But, judging from the amount of writing on the web that already unmistakably sounds like Claude, most people likely will not bother.
5b: Also, not any paraphrase will work. To remove (for example) a k=5-minhash watermark completely from a long document, you need to make sure none of the original 2-grams, 3-grams, 4-grams, 5-grams and 6-grams of the text remain.
6. 𝗪𝗶𝗹𝗹 𝘆𝗼𝘂 𝗶𝗻𝗮𝗱𝘃𝗲𝗿𝘁𝗲𝗻𝘁𝗹𝘆 𝗰𝗼𝗽𝘆 𝘁𝗵𝗲 𝘄𝗮𝘁𝗲𝗿𝗺𝗮𝗿𝗸? -- No, with a good implementation the space of possible realizations of the key is too large to memorize.
7. 𝗪𝗶𝗹𝗹 𝘁𝗵𝗶𝘀 𝗮𝗹𝗹𝗼𝘄 𝗖𝗹𝗮𝘂𝗱𝗲𝘀 𝘁𝗼 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗼𝘁𝗵𝗲𝗿 𝗶𝗻𝘀𝘁𝗮𝗻𝗰𝗲𝘀 𝗶𝗻 𝗮 𝘀𝘄𝗮𝗿𝗺? -- The watermark will 'appear' like random sampler fluctuation to the model and would not be detectable. But, if an agent gets hold of a detector endpoint, it can absolutely use the watermark to ID other Claude agents (not that it would have trouble noticing them based on their writing as of today).
8. 𝗪𝗶𝗹𝗹 𝘁𝗵𝗶𝘀 𝗱𝗲𝘁𝗲𝗰𝘁 𝗱𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻? -- By default, no. If the watermark is set up to be 'undetectable' (as assumed above), it will not be picked up in training by other models. For that to happen, the watermark needs to be detectable by ML algorithms.
9. 𝗪𝗶𝗹𝗹 𝘁𝗵𝗶𝘀 𝗺𝗮𝗸𝗲 𝗣𝗮𝗻𝗴𝗿𝗮𝗺'𝘀 𝗷𝗼𝗯 𝗲𝗮𝘀𝗶𝗲𝗿? -- By default no, this is a separate avenue to detection. But, they might collaborate with Anthropic which would allow them to detect the watermark as well and show a watermark score next to their text detection score.
10. Bonus: All aside, is this a good idea? I don't know. The companies are doing it to follow the writing of the EU AI act, which was written based on 2024 information and when the field looked very different, and threat models were focused much more on slop/propaganda (like the Kokotajlo 2026 prediction). The actual 2026 looks quite a bit different.
Pierre Fernandez retweeted
Happy to release ✨ Muse Glimmer ✨
- level ~= Qwen 3.6-27B
- Apache 2
- 30B dense
- quantized to run in 17GB
- quant + spec dec => 50 tok/s on macbook m5 max, interactive, smooth
Enjoy!
Introducing Muse Glimmer, an open-weight 30B-parameter model optimized for local, always-on agent workflows.
Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category, and is designed to run entirely on consumer hardware like a Mac or PCs with performant GPUs.
In keeping with our long tradition of sharing fundamental AI research, we’re releasing model weights under a permissive Apache 2.0 license.
🧵👇
Pierre Fernandez retweeted
WaiT for the Signal: Simple Frequency-Aware Flow-Matching.
We show how to natively incorporate a fundamental property of images directly into diffusion models; setting a new pixel-space SOTA on ImageNet, while reducing compute.
📄 arxiv.org/abs/2607.28760
Full breakdown below👇
Pierre Fernandez retweeted
🧵How to combine code correctness and efficiency objectives in online RL?
-> we break the correctness-efficiency Pareto frontier!
+125% relative improvement in CWM32B compared to standard RLVR (13.7 ->30.9 pass@1@top30%)
+150% with Qwen32B
Paper: arxiv.org/abs/2607.25970
arxiv.org/abs/2607.25970
Exploration of RLVR setups for code optimization.

































