Saba

MSc @Mila_Quebec and @UMontrealDIRO

[Marius Mosbach @mariusmosbach]

I will be at #ICML2026 🇰🇷 presenting three papers on interpretability and model adaptation. Feel free to reach out if you want to chat! 🙂

P.S. I'm looking for motivated students to join my new group at Saarland University 🇩🇪: https://t.co/WJUfRpWBnQ

[Marius Mosbach @mariusmosbach]

I'm looking for a motivated PhD student to start in October 2026, plus research interns, with additional PhD funding coming next year. If you're excited about model adaptation, interpretability, or continual learning, please fill out this form 👉 https://t.co/2ugAOZXWwY 5/6

[QianYang 🚀 CVPR @QianYangMila]

🚀 Excited to share PROGRESS — accepted at #CVPR2026!
Can VLMs tell us what data they can most effectively learn at each stage of training?
We say yes. Meet "Learning What Matters: Prioritized Concept Learning via Relative Error-driven Sample Selection" 🧵

[MAPS - CVPR 2026 Workshop @maps_cvpr]

We're off to a great start at the MAPS workshop @CVPR after an opening session! 💫

Stick around for more talks, posters, and our panel discussion later today.

#CVPR2026

[Kanishk Jain @kanji1011]

Excited to share our new paper: "Discovering Failure Modes of Vision-Language Models using Reinforcement Learning"

[Siva Reddy @sivareddyg]

McGill University (@mcgillu) has many open faculty and postdoctoral positions with generous funding packages, thanks to Impact+ grants, which are investing $2 billion to attract global talent to Canada 🇨🇦🇨🇦🇨🇦.

Associate/Full Professor: $8 million startup package
Assistant Professor: $600K startup package
Postdoc: $70K (starting salary)

If you are interested and work in the space of AI/ML/NLP/LLMs, please reach out to me.

#AI #NLProc #ML

[Yoshua Bengio @Yoshua_Bengio]

OpenReview is a pillar of progress in the AI research community. Now it needs our support.

Along with several of my colleagues, I have pledged to help, and I encourage anyone who can to do the same.
https://t.co/8Rq7DtZqgp

[Oscar Mañas @oscmansan]

📣 Hiring Research Interns for Meta Superintelligence Labs in Zurich! Work on large-scale generative models (image/video gen, multimodal, world models) with real impact on products used by billions.

📍 Zurich | 🕒 6 months | 🎓 PhD students

https://t.co/3YKfwpLbL3

[Rabiul Awal @_rabiulawal]

Presenting EARL at Space in Vision, Language and Embodied AI (Room 29 AD) #neurips2025

[Hugo Larochelle @hugo_larochelle]

Really enjoying the smaller scale and more intimate experience of NeurIPS in Mexico City, along with my @Mila_Quebec crew!
Including dinners that feature guacamole with crispy larva :-)

[Mila - Institut québécois d'IA @Mila_Quebec]

Juan Ramirez (@juan43ramirezsible) PhD student at UdeM/Mila, will be at Mila’s booth at 11am to talk about his work on feasible learning. Come visit and chat with him! #625

[Imade. @ImadeIyamu]

OpenAI's Research Residency Program just opened (Relocation assistance is available)

6-month program designed to identify, mentor, and develop exceptional individuals

Compensation: $18,300 per month

https://t.co/navlUWTkPa

[Mila - Institut québécois d'IA @Mila_Quebec]

Alongside @NeurIPSConf in San Diego, the satellite conference NeurIPS Mexico City is taking place, with several Mila student-researchers taking part. Two of them presented their research today.
SaharDastani (@sonia_dt98), PhD student at ETS/Mila, presented “TRUST: Test-Time Refinement using Uncertainty-Guided SSM Traverses” and Saba Ahmadi (@Saba_A96), affiliated researcher at UdeM/Mila, presented “The Promise of RL for Autoregressive Image Editing.” Congratulations!

[Sai Rajeswar @RajeswarSai]

At NeurIPS in San Diego from today!
great to catch up with old friends, and happy to chat about multimodal reasoning/ interactive RL environments.

Some of us from Apriel-15B-Thinker team would be around in person as well for ideas and feedback. Pls come chat with us.. @SathwikTejaswi @sagardavasam @Vikas_NLP_UA

[Arian Hosseini @ ICML 🇰🇷 @arianTBD]

Our team at GDM is hiring a Student Researcher (SR) next year 🧠

If you’re a PhD student working on LLMs please apply. I’d love to hear from you.

Please fill out this form: https://t.co/BVBVb5kXhA

[Siva Reddy @sivareddyg]

Honored to receive the Computer Science Canada Outstanding Early Career Researcher award 🏅. It is a recognition of the work carried out by my students for their courage to push fundamental ideas in natural language processing even in the era of LLMs.

Thanks to my mentors and nominators for making time in their incredibly busy schedule. And thanks to my colleagues at Mila, McGill and ServiceNow for fostering an intellectually stimulating environment and providing resources to succeed!

[Aarash Feizi @aarashfeizi]

🚀 Announcing GroundCUA, a high-quality dataset for grounding computer-use agents. With over 3M expert annotations spanning 87 desktop apps, we use our new dataset to train state-of-the-art grounding models, namely GroundNext-3B and GroundNext-7B.
👇 Thread

[Kyle Lo @ ICML2026 🇰🇷 @kylelostat]

why intern at Ai2?

🐟interns own major parts of our model development, sometimes even leading whole projects
🐡we're committed to open science & actively help our interns publish their work

reach out if u wanna build open language models together 🤝

links👇

[Amirhossein Kazemnejad @a_kazemnejad]

After nearly 3 years since our NeurIPS paper, SOTA architectures are now adopting NoPE. Kimi Linear uses NoPE for all full-attention layers (not a RoPE hybrid).

[Rohan Paul @rohanpaul_ai]

Stanford just published a huge 470-page study 📕

"The Principles of Diffusion Models"

Explains how diffusion models turn noise into data and ties their main ideas together.

It starts from a forward process that adds noise over time, then learns the exact reverse.

The reverse uses a time dependent velocity field that tells how to move a sample at each step.

Sampling becomes solving a time based equation that carries noise to data along a trajectory.

There are 3 views of this idea, variational, score-based, and flow-based, and they describe the same thing.

There are also 4 training targets, noise, clean data, score, and velocity, and these are equivalent.

Shows how guidance can steer outputs using a prompt or label without extra classifiers.

Reviews fast solvers that cut steps while keeping quality stable.

Explains distillation methods that shrink many sampling steps into a few by mimicking a teacher model.

Introduces flow map models that learn direct jumps between times for fast generation from scratch.