Jennifer J. Sun (@JenJSun) header
Jennifer J. Sun (@JenJSun) profile picture

AI for Scientists, assistant professor @CornellCIS, part-time @GoogleDeepMind

New York
Joined August 2020
Photo by Jennifer J. Sun (@JenJSun)Photo by Jennifer J. Sun (@JenJSun)Photo by Jennifer J. Sun (@JenJSun)Photo by Jennifer J. Sun (@JenJSun)Photo by Jennifer J. Sun (@JenJSun)Photo by Jennifer J. Sun (@JenJSun)Photo by Jennifer J. Sun (@JenJSun)Photo by Jennifer J. Sun (@JenJSun)Photo by Jennifer J. Sun (@JenJSun)Photo by Jennifer J. Sun (@JenJSun)
Jennifer J. Sun retweeted
Attending @COLM_conf this week! I’ll be presenting our work Co-Evolving Structured Knowledge and Reasoning in Language Models today, 11am–1pm at Poster #137. KBevo lets knowledge construction and reasoning co-evolve, so reasoning outcomes can shape what gets stored in the knowledge base. Come say hi if you’re around :) Joint work with Ryan, Menghan, Akanksha, Mihir, Dongyoung, @KilianQW, @yoavartzi, and @JenJSun arXiv: arxiv.org/abs/2608.26386 Blog: menghan-xu.github.io/KBevo/
4
2
29
2,576
Jennifer J. Sun retweeted
Introducing Live Interactive Training for Video Segmentation (#CVPR2026) User corrections help fix errors in challenging scenarios, but current interactive systems typically use this feedback to refine predictions rather than learn from it. Can we make these corrections help the model adapt and reduce repeated user effort? We introduce LIT-LoRA, a lightweight plug-and-play module for interactive test-time adaptation through human feedback. When a user corrects an error, LIT-LoRA updates on the fly and helps fix similar future errors. Highlights: 📉 18–34% fewer user corrections on challenging VOS benchmarks ⚡ ~0.5s online update overhead per correction 🧩 Plug-and-play across different models and tasks
2
3
1
15
1,656
Jennifer J. Sun retweeted
Excited to share FormulaCode, a continually updating benchmark for evaluating the holistic ability of LLM agents to optimize codebases. Our current dataset consists of 957 tasks curated from 245477 pull requests in 70+ repositories (and growing!). 🌐 formulacode.org 🧵👇
4
6
3
29
14,576
Jennifer J. Sun retweeted
.@Cornell is recruiting for multiple postdoctoral positions in AI as part of two programs: Empire AI Fellows and Foundational AI Fellows. Positions are available in NYC and Ithaca. Deadline for full consideration is Nov 20, 2025! academicjobsonline.org/ajo/j…
2
39
6
121
60,994
Jennifer J. Sun retweeted
Thrilled to share our latest work on SciVid, to appear at #ICCV2025! 🎉 SciVid offers cross-domain evaluation of video models in scientific applications, including medical CV, animal behavior, & weather forecasting 🧪🌍📽️🪰🐭🫀🌦️ #AI4Science #FoundationModel #CV4Science [1/5]🧵
1
10
1
32
2,957
Jennifer J. Sun retweeted
Check out our LMLM, our take on what is now being called a "cognitive core" (as far as branding go, this one is not bad) can look like, how it behaves, and how you train for it. arxiv.org/abs/2505.15962
The race for LLM "cognitive core" - a few billion param model that maximally sacrifices encyclopedic knowledge for capability. It lives always-on and by default on every computer as the kernel of LLM personal computing. Its features are slowly crystalizing: - Natively multimodal text/vision/audio at both input and output. - Matryoshka-style architecture allowing a dial of capability up and down at test time. - Reasoning, also with a dial. (system 2) - Aggressively tool-using. - On-device finetuning LoRA slots for test-time training, personalization and customization. - Delegates and double checks just the right parts with the oracles in the cloud if internet is available. It doesn't know that William the Conqueror's reign ended in September 9 1087, but it vaguely recognizes the name and can look up the date. It can't recite the SHA-256 of empty string as e3b0c442..., but it can calculate it quickly should you really want it. What LLM personal computing lacks in broad world knowledge and top tier problem-solving capability it will make up in super low interaction latency (especially as multimodal matures), direct / private access to data and state, offline continuity, sovereignty ("not your weights not your brain"). i.e. many of the same reasons we like, use and buy personal computers instead of having thin clients access a cloud via remote desktop or so.
2
7
34
7,115
I’m presenting Escher (trishullab.github.io/escher-…) at #CVPR2025 Saturday morning (Poster Session #3; #236). Escher builds a visual concept library with a vision‑language critic (no human labels needed). Swing by if you’d like to chat about program synthesis & multimodal reasoning!
2
4
18
2,097
Jennifer J. Sun retweeted
After over 15 months, we are excited to finally release VideoPrism! The model comes in two sizes, Base and Large, and the video encoders are available today at github.com/google-deepmind/v…. We are also working towards adding more support into the repository, please stay tuned.
Introducing VideoPrism, a single model for general-purpose video understanding that can handle a wide range of tasks, including classification, localization, retrieval, captioning and question answering. Learn how it works at goo.gle/49ltEXW
3
1
1
11
1,049
Jennifer J. Sun retweeted
🚀Excited to share our latest work: LLMs entangle language and knowledge, making it hard to verify or update facts. We introduce LMLM 🐑🧠 — a new class of models that externalize factual knowledge into a database and learn during pretraining when and how to retrieve facts instead of memorizing them. 🧠Why LMLM? • Learning to look up facts is easier than memorization • Externalizing knowledge improves factual precision • Enables instant machine unlearning by design LMLM opens new directions for how future language models can manage and access knowledge. 📄 [ArXiv] arxiv.org/pdf/2505.15962 🌐 [Project Page] linxi-zhao.github.io/LMLM-si… 💻 [Code] github.com/kilian-group/LMLM 🎤 [Talk] simons.berkeley.edu/talks/ki… Huge thanks to my amazing collaborators: @linxizhao4 @sofianzalouk Christian Belardi Justin Lovelace @JinPZhou And to our incredible advisors @KilianQW, @yoavartzi, and @JenJSun for their generous support and insight.
1
15
1
43
6,100
We're excited to share our latest work! We achieve SOTA results in segmentation, detection, and depth estimation, in single and cross-domain, by exploiting image-aligned text prompts in a pretrained diffusion backbone repurposed for vision tasks. See vision.caltech.edu/tadp/ 🧵👇
3
29
7
175
36,029
Jennifer J. Sun retweeted
Won't you be my neighbor? Northwestern Neuroscience in downtown Chicago is running a broad faculty search: nature.com/naturecareers/job… Come join a large and growing neuroscience community!
43
1
81
23,963
Jennifer J. Sun retweeted
Presenting BKiND-3D tomorrow with @JenJSun and Lili Karashchuk tomorrow (Wed., June 21) in the morning session at poster #74. Feel free to stop by!
All animals behave in 3D - we discover 3D poses directly from multi-view videos without requiring annotations. Essentially videos -> 3D keypoints + connections We will be @CVPR on June 21! BKinD-3D Paper: arxiv.org/abs/2212.07401 Co-first-authors Lili Karashchuk & @_AmilDravid
2
2
22
2,592
Jennifer J. Sun retweeted
Do you study interacting animals? Do you want to meet machine learning researchers studying interacting robots, cars, animals, and people, and exchange ideas across these fields? 🚗🤖🏃🐁🐟🧠 Submit your mss to the MABe workshop at CVPR and join the fun!
The Multi-Agent Behavior Workshop (MABe) will take place @CVPR 2023 in Vancouver! We are featuring fantastic speakers @SiyuTang3, @georgiagkioxari, @tlandgraf, @Wei_ZHAN_, @sabinehauert, & Ben Sapp; with panel & poster discussions 👇 Call for papers!📢 sites.google.com/view/mabe23
2
3
3,211
Jennifer J. Sun retweeted
Update: * The slides for the tutorial are here: dropbox.com/s/sw4lduxrt3l9lx… * Notebooks for the tutorial (created by Atharva Sehgal and @JenJSun) are here: neurosymbolic-learning.githu…
2
7
36
3,641
Today's the last day to apply for the CAJAL Machine Learning in Neuroscience summer school in beautiful Lisbon, Portugal. Please apply! cajal-training.org/on-site/a… Co-organizers: Gonzalo de Polavieja @dePolavieja & Memming Park @memming
2
23
3
46
19,370
Jennifer J. Sun retweeted
Our Neurosymbolic Programming tutorial is coming to #POPL23! We'll explain the basics, do an algorithmic deep dive, and explore neuroscience applications. * 1/16 (Mon), 2 pm * Speakers: Armando, my student Atharva, me (@yisongyue & @JenJSun in spirit). popl23.sigplan.org/details/P…
2
14
1
69
10,118
Jennifer J. Sun retweeted
Hi Twitter! If you have a hard-drive full of videos of mice, we need your help! The 2023 Multi-Agent Behavior Challenge will tackle generalization of behavior classifiers across labs, with a planned $50,000 prize pool. To do so, we need lots of data. forms.gle/cCnDKQZ3WmVSCHi46
2
29
72
16,863
Jennifer J. Sun retweeted
Come to our virtual tutorial on Neurosymbolic Programming at #NeurIPS2022! ** 12/5 (Monday), 10 am Chicago time ** sites.google.com/view/nsptut… * Speakers: Armando Solar-Lezama, @JenJSun, me * Panel: @Antihebbiann, @pushmeet, @SriramRajamani, @InalaJeevana, @yisongyue (moderator)
2
6
2
34