Extremely excited to announce that I will be joining @utaustin.bsky.social Computer Science in August 2025 as an Assistant Professor! 🎉

🌵 I'm going to be presenting PBT at #NAACL2025 today at 2PM! Come by poster session 2 if you want to hear about: -- balancing positive and negative persuasion -- improving LLM teamwork/debate -- training models on simulated dialogues With @mohitbansal.bsky.social and @peterbhase.bsky.social

✈️ Heading to #NAACL2025 to present 3 main conf. papers, covering training LLMs to balance accepting and rejecting persuasion, multi-agent refinement for more faithful generation, and adaptively addressing varying knowledge conflict. Reach out if you want to chat!

Check out 🚨CAPTURe🚨 -- a new benchmark testing spatial reasoning by making VLMs count objects under occlusion. SOTA VLMs (GPT-4o, Qwen2-VL, Intern-VL2) have high error rates on CAPTURe (but humans have low error ✅) and models struggle to reason about occluded objects. arxiv.org/abs/2504.15485 🧵👇

In Singapore for #ICLR2025 this week to present papers + keynotes 👇, and looking forward to seeing everyone -- happy to chat about research, or faculty+postdoc+phd positions, or simply hanging out (feel free to ping)! 🙂 Also meet our awesome students/postdocs/collaborators presenting their work.

🚨Real-world retrieval is messy: queries are ambiguous or docs conflict & have incorrect/irrelevant info. How can we jointly address these problems? ➡️RAMDocs: challenging dataset w/ ambiguity, misinformation & noise ➡️MADAM-RAG: multi-agent framework, debates & aggregates evidence across sources 🧵⬇️

What if we could transform advanced math problems into abstract programs that can generate endless, verifiable problem variants? Presenting EFAGen, which automatically transforms static advanced math problems into their corresponding executable functional abstractions (EFAs). 🧵👇

🚨Announcing TaCQ 🚨 a new mixed-precision quantization method that identifies critical weights to preserve. We integrate key ideas from circuit discovery, model editing, and input attribution to improve low-bit quant., w/ 96% 16-bit acc. at 3.1 avg bits (~6x compression) 📃 arxiv.org/abs/2504.07389

🎉 A big congratulations to @archiki.bsky.social (advised by Prof. @mohitbansal.bsky.social) for the being awarded the 2025 Apple Scholars in AI/ML PhD Fellowship!", we are proud of you! 👏

🎉🎉 Big congrats to @archiki.bsky.social on being awarded the @Apple AI/ML PhD Fellowship, for her extensive contributions in evaluating+improving reasoning in language/reward models and their applications to new domains (ReCEval, RepARe, System-1.x, ADaPT, ReGAL, ScPO, UTGen, GrIPS)! #ProudAdvisor

🥳🥳 Honored and grateful to be awarded the 2025 Apple Scholars in AI/ML PhD Fellowship! ✨ Huge shoutout to my advisor @mohitbansal.bsky.social, & many thanks to my lab mates @unccs.bsky.social , past collaborators + internship advisors for their support ☺️🙏 machinelearning.apple.com/updates/appl...

Introducing VEGGIE 🥦—a unified, end-to-end, and versatile instructional video generative model. VEGGIE supports 8 skills, from object addition/removal/changing, and stylization to concept grounding/reasoning. It exceeds SoTA and shows 0-shot multimodal instructional & in-context video editing.

🚨 Check out "UTGen & UTDebug" for learning to automatically generate unit tests (i.e., discovering inputs which break your code) and then applying them to debug code with LLMs, with strong gains (>12% pass@1) across multiple models/datasets! (see details in 🧵👇) 1/4

🚨 Excited to share: "Learning to Generate Unit Tests for Automated Debugging" 🚨 which introduces ✨UTGen and UTDebug✨ for teaching LLMs to generate unit tests (UTs) and debugging code from generated tests. UTGen+UTDebug yields large gains in debugging (+12% pass@1) & addresses 3 key questions: 🧵👇

🎉 Congrats to the awesome students, postdocs, & collaborators for this exciting batch of #ICLR2025 and #NAACL2025 accepted papers (FYI some are on the academic/industry job market and a great catch 🙂), on diverse, important topics such as: -- adaptive data generation environments/policies ... 🧵

Deeply honored & humbled to have received the Presidential #PECASE Award by the @WhiteHouse and @POTUS office! 🙏 Most importantly, very grateful to my amazing mentors, students, postdocs, collaborators, and friends+family for making this possible, and for making the journey worthwhile + beautiful 💙

✨ Collaborating with our amazing postdocs in our lab over the past year has been a great learning experience, with lots of fun + exciting research in LLM agents, reasoning, & multimodality! Check out the new postdoc openings and become a part of the vibrant research @unccs.bsky.social !⬇️

🚨 We have postdoc openings at UNC 🙂 Exciting+diverse NLP/CV/ML topics**, freedom to create research agenda, competitive funding, very strong students, mentorship for grant writing, collabs w/ many faculty+universities+companies, superb quality of life/weather. Please apply + help spread the word 🙏

🚨 I’m on the academic job market! j-min.io I work on ✨Multimodal AI✨, advancing reasoning in understanding & generation by: 1⃣ Making it scalable 2⃣ Making it faithful 3⃣ Evaluating + refining it Completing my PhD at UNC (w/ @mohitbansal.bsky.social). Happy to connect (will be at #NeurIPS2024)! 👇🧵

I've truly enjoyed ✨ all of our collaborations ✨ over the past year. I particularly admire his thoughtful ideas, dedication to seeing them through, and his mentorship of junior students to do the same. I'm excited to see research from his lab as a professor and an advisor! 😄

🚨 I am on the faculty job market this year 🚨 I will be presenting at #NeurIPS2024 and am happy to chat in-person or digitally! I work on developing AI agents that can collaborate and communicate robustly with us and each other. More at: esteng.github.io and in thread below 🧵👇

Looking forward to giving this Distinguished Lecture at StonyBrook next week & meeting the several awesome NLP + CV folks there - thanks Niranjan‬ + all for the kind invitation 🙂 PS. Excited to give a new talk on "Planning Agents for Collaborative Reasoning and Multimodal Generation" ➡️➡️ 🧵👇

🚨 Reverse Thinking Makes LLMs Stronger Reasoners We can often reason from a problem to a solution and also in reverse to enhance our overall reasoning. RevThink shows that LLMs can also benefit from reverse thinking 👉 13.53% gains + sample efficiency + strong generalization (on 4 OOD datasets)!

Congratulations to #UNC CS student David Wan for winning the prestigious 2024 Google PhD Fellowship in NLP. 🎉🥳 A very well-deserved honor for his impactful work on factual and faithful text+multimodal generation with Prof. @mohitbansal.bsky.social and UNC NLP group! ▶️ blog.google/technology/r...