Accepted to #NeurIPS2026! 🎉
🦓 New paper: ZEBRA - Your AI agent has $10 to spend on a coding task across planning, implementing, and refining. How should it split the budget?
We turn budget allocation into a knapsack problem, solved zero-shot at inference time.With @InbalTalgam 👇 [arxiv.org/abs/2605.20485]
may hamri retweeted
You ask for a talking parrot, but the model makes the pirate talk AND gives him a beak! 🦜😮💨
-> Introducing The Attention Triangle in Audio-Video Models: how semantics leak between text, audio, and video, and how we steer them back. 🧵 [1/5]
may hamri retweeted
Robot policies can move but can't think.
LLMs can think but can't move.
So we connected them.
Real robot: 16.7% → 97.3%
Sim (LIBERO-PRO): 12.8% → 53.3%
🦓 New paper: ZEBRA - Your AI agent has $10 to spend on a coding task across planning, implementing, and refining. How should it split the budget?
We turn budget allocation into a knapsack problem, solved zero-shot at inference time.With @InbalTalgam 👇 [arxiv.org/abs/2605.20485]
may hamri retweeted
Gave a talk at @GoogleDeepMind on our joint work with @HazanPrinceton and @tri_dao on deployable RL policies for robotics. Great discussion with @JeffDean.
may hamri retweeted
[1/4] Sync about it… 💭✨
Editing a portrait video yet keeping it fully synced with the original across the entire sequence.
Read more about Sync-LoRA: sagipolaczek.github.io/Sync-… 🚀
may hamri retweeted
Feels like a dream! I’ve recently started my Ph.D. in Computer Science @Princeton! Working on exciting research with Professors @HazanPrinceton and @tri_dao 🤩
may hamri retweeted
[1/5] Rethinking SVGs, the implicit way — meet NeuralSVG! 🎨✨
An implicit neural representation for generating layered SVGs from text prompts.
💧 Powered by SDS and nested-dropout for ordered shapes
🖌️ Enables inference-time editing like color palette & aspect ratio
Read more on sagipolaczek.github.io/Neura… 🚀
may hamri retweeted
LLMs make factual mistakes, which is bad. How can we find these?
In a new paper, we introduce “LMvsLM”, and show that we can use one LM to detect the mistakes of another via "Cross-Examination".
Link: arxiv.org/abs/2305.13281
Check it out!










