Some personal updates: - I've completed my PhD at @unccs.bsky.social! 🎓 - Starting Fall 2026, I'll be joining the CS dept. at Johns Hopkins University @jhucompsci.bsky.social as an Assistant Professor 💙 - Currently exploring options for my gap year (Aug 2025 - Jul 2026), so feel free to reach out! 🔎

🚨 Introducing our @tmlrorg.bsky.social paper “Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation” We present UnLOK-VQA, a benchmark to evaluate unlearning in vision-and-language models, where both images and text may encode sensitive or private information.

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

I will be presenting ✨Reverse Thinking Makes LLMs Stronger Reasoners✨at #NAACL2025! In this work, we show - Improvements across 12 datasets - Outperforms SFT with 10x more data - Strong generalization to OOD datasets 📅4/30 2:00-3:30 Hall 3 Let's chat about LLM reasoning and its future directions!

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 announce UTGen and UTDebug, where we first learn to generate unit tests and then apply them to debugging generated code with LLMs, with strong gains (+12% pass@1) on LLM-based debugging across multiple models/datasets via inf.-time scaling and cross-validation+backtracking! 🧵👇

🚨 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 ... 🧵

🎉Very excited that our work on Persuasion-Balanced Training has been accepted to #NAACL2025! We introduce a multi-agent tree-based method for teaching models to balance: 1️⃣ Accepting persuasion when it helps 2️⃣ Resisting persuasion when it hurts (e.g. misinformation) arxiv.org/abs/2410.14596 🧵 1/4

Thanks @AAAI for selecting me as a #AAAI Fellow! Very humbled+excited to be a part of the respected cohort of this+past years' fellows (& congrats everyone)! 🙏 100% credit goes to my amazing past/current students+postdocs+collab for their work (& thanks to mentors+family)!💙 aaai.org/about-aaai/a...

🎉Congratulations to Prof. @mohitbansal.bsky.social on being named a 2025 @RealAAAI Fellow for "significant contributions to multimodal AI foundations & faithful language generation and summarization." 👏 16 Fellows chosen worldwide by cmte. of 9 past fellows & ex-president: aaai.org/about-aaai/a...

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 💙

🚨 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 now at #NeurIPS2024! 🔥 (yeah, I'm the one with the red Santa hat🧑‍🎄) On Dec 13 PM, I present SELMA, co led with Jialu Li! 👉 improving the faithfulness of T2I models with automatically generated image-text pairs, with skill-specific expert learning and merging! P.S. I'm on the faculty job market👇

🚨 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)! 👇🧵

Elias is an outstanding researcher and mentor, exceptionally intelligent and brilliant with so many good ideas. I'm excited to see all the incredible things he'll accomplish as a professor!

Reverse Thinking Makes LLMs Stronger Reasoners abs: arxiv.org/abs/2411.19865 Train an LLM to be able to generate forward reasoning from question, backward question, and backward reaoning from backward question Shows an average 13.53% improvement over the student model’s zero-shot performance

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" ➡️➡️ 🧵👇

🚨Just read an exciting new paper from Justin 👇 on how to get the benefits of reasoning backwards (i.e. starting with the solution and tracing it back to the question) into a model while keeping the test-time cost of reasoning the same! 🧵1/3

🚨 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)!