Keshigeyan Chandrasegaran (@keshigeyan) 's Twitter Profile
Keshigeyan Chandrasegaran

@keshigeyan

CS PhD student @Stanford. Research @StanfordAILab & @StanfordSVL. Prev: research @sutdsg (Temasek Labs), undergrad @sutdsg.

ID: 1095671366949335040

linkhttp://cs.stanford.edu/~keshik/ calendar_today13-02-2019 13:09:45

175 Tweet

421 Followers

226 Following

Christian Szegedy (@chrszegedy) 's Twitter Profile Photo

The Inception paper arxiv.org/abs/1409.4842 was awarded the Longuet-Higgins prize (Test of time). The architecture represented a significant step forward in inference efficiency especially on CPU and variants of Inception networks were used in Google products for years.

Yunzhi Zhang (@zhang_yunzhi) 's Twitter Profile Photo

(1/n) Time to unify your favorite visual generative models, VLMs, and simulators for controllable visual generation—Introducing a Product of Experts (PoE) framework for inference-time knowledge composition from heterogeneous models.

Wenlong Huang (@wenlong_huang) 's Twitter Profile Photo

Join us tomorrow in SGM 124 for the SWOMO workshop at #RSS2025! We will have 6 amazing talks and a panel in the end to discuss structured world modeling for robotics! Latest schedule and information at swomo-rss.github.io

Volodymyr Kuleshov 🇺🇦 (@volokuleshov) 's Twitter Profile Photo

✨The Mercury tech report from Inception Labs is now available on the Arxiv. It took us a bit of time to get this one out, but it’s a nice complement to the blog post with many more experiments. Stay tuned for more updates soon! arxiv.org/abs/2506.17298

Jon Saad-Falcon (@jonsaadfalcon) 's Twitter Profile Photo

How can we close the generation-verification gap when LLMs produce correct answers but fail to select them? 🧵 Introducing Weaver: a framework that combines multiple weak verifiers (reward models + LM judges) to achieve o3-mini-level accuracy with much cheaper non-reasoning

How can we close the generation-verification gap when LLMs produce correct answers but fail to select them? 
🧵 Introducing Weaver: a framework that combines multiple weak verifiers (reward models + LM judges) to achieve o3-mini-level accuracy with much cheaper non-reasoning
Sanjana Srivastava (@sanjana__z) 's Twitter Profile Photo

🤖 Household robots are becoming physically viable. But interacting with people in the home requires handling unseen, unconstrained, dynamic preferences, not just a complex physical domain. We introduce ROSETTA: a method to generate reward for such preferences cheaply. 🧵⬇️

Hong-Xing "Koven" Yu (@koven_yu) 's Twitter Profile Photo

#ICCV2025 🤩3D world generation is cool, but it is cooler to play with the worlds using 3D actions 👆💨, and see what happens! — Introducing *WonderPlay*: Now you can create dynamic 3D scenes that respond to your 3D actions from a single image! Web: kyleleey.github.io/WonderPlay/ 🧵1/7

Kyle Sargent (@kylesargentai) 's Twitter Profile Photo

FlowMo, our paper on diffusion autoencoders for image tokenization, has been accepted to #ICCV2025! See you in Hawaii! 🏄‍♂️

Stefano Ermon (@stefanoermon) 's Twitter Profile Photo

Huge milestone from the team! A blazing-fast diffusion LLM built for chat, delivering real-time performance at commercial scale. If you liked Mercury Coder for code, you'll love this for conversation.

Yutong Bai (@yutongbai1002) 's Twitter Profile Photo

What would a World Model look like if we start from a real embodied agent acting in the real world? It has to have: 1) A real, physically grounded and complex action space—not just abstract control signals. 2) Diverse, real-life scenarios and activities. Or in short: It has to

Liquid AI (@liquidai_) 's Twitter Profile Photo

Today, we release the 2nd generation of our Liquid foundation models, LFM2. LFM2 set the bar for quality, speed, and memory efficiency in on-device AI. Built for edge devices like phones, laptops, AI PCs, cars, wearables, satellites, and robots, LFM2 delivers the fastest

Today, we release the 2nd generation of our Liquid foundation models, LFM2.

LFM2 set the bar for quality, speed, and memory efficiency in on-device AI.

Built for edge devices like phones, laptops, AI PCs, cars, wearables, satellites, and robots, LFM2 delivers the fastest
Michael Poli (@michaelpoli6) 's Twitter Profile Photo

It's easy (and fun!) to get nerdsniped by complex architecture designs. But over the years, I've seen hybrid gated convolutions always come out on top in the right head-to-head comparisons. The team brings a new suite of StripedHyena-style decoder models, in the form of SLMs

Garyk Brixi (@garykbrixi) 's Twitter Profile Photo

Evo 2 update: new dependency versions (torch, transformer engine, flash attn) and a docker option mean it should be easy to setup without needing to compile locally. Happy ATGC-ing! github.com/ArcInstitute/e…

Rahul Venkatesh (@rahul_venkatesh) 's Twitter Profile Photo

AI models segment scenes based on how things appear, but babies segment based on what moves together. We utilize a visual world model that our lab has been developing, to capture this concept — and what's cool is that it beats SOTA models on zero-shot segmentation and physical

Manling Li (@manlingli_) 's Twitter Profile Photo

🏆Thrilled to receive ACL 2025 Inaugural Dissertation Award Honorable Mention. “Multimodality” has moved incredibly fast that my PhD research already feels like from a different era. It makes me wonder how challenging and anxious for today’s students to choose thesis

🏆Thrilled to receive <a href="/aclmeeting/">ACL 2025</a> Inaugural Dissertation Award Honorable Mention. 

“Multimodality” has moved incredibly fast that my PhD research already feels like from a different era. 

It makes me wonder how challenging and anxious for today’s students to choose thesis
Agrim Gupta (@agrimgupta92) 's Twitter Profile Photo

Introducing Genie 3, our state-of-the-art world model that generates interactive worlds from text, enabling real-time interaction at 24 fps with minutes-long consistency at 720p. 🧵👇