Zaid Khan
@codezakh
@uncnlp with @mohitban47 working on automating env/data generation + program synthesis formerly @allenai @neclabsamerica
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How can an agent reverse engineer the underlying laws of an unknown, hostile & stochastic environment in “one life”, without millions of steps + human-provided goals / rewards? In our work, we: 1️⃣ infer an executable symbolic world model (a probabilistic program capturing…
Can AI models teach you to shoot like Steph Curry? 🏀 Come to my talk on Challenges in Expert-Level Skill Analysis at 4:30 pm in Room 318-A tomorrow (Sunday) to find out! sauafg-workshop.github.io #ICCV2025
sauafg-workshop.github.io
SAUAFG Workshop – ICCV 2025
ICCV 2025 SAUAFG Workshop on AI-driven skill assessment, understanding, and feedback generation.
🗓Oct 19, 2025 | 📍Hawaii Convention Center, Room 318-A 👉 Learn more: sauafg-workshop.github.io 🔍 We'll explore AI-driven Skilled Activity Understanding, Assessment & Guidance generation in various domains from Surgery to Sports, from Robotics and Manufacturing to Education
sauafg-workshop.github.io
SAUAFG Workshop – ICCV 2025
ICCV 2025 SAUAFG Workshop on AI-driven skill assessment, understanding, and feedback generation.
🎉 Big congrats to Zaid on being awarded the NDSEG PhD Fellowship, for his innovative contributions in environment/data generation, skill-based self-improvement and adaptable agents, visual program synthesis, and world model inference! #ProudAdvisor 👇👇
🥳 Honored and grateful to be awarded an NDSEG Fellowship in Computer Science! 💫🇺🇸 Big thanks to my advisor @mohitban47 for his guidance, and shoutout to my lab mates at @unc_ai_group, collaborators, internship advisors, and mentors for their support 🤗 Excited to continue…
🥳 Honored and grateful to be awarded an NDSEG Fellowship in Computer Science! 💫🇺🇸 Big thanks to my advisor @mohitban47 for his guidance, and shoutout to my lab mates at @unc_ai_group, collaborators, internship advisors, and mentors for their support 🤗 Excited to continue…
🎉 Congratulations to our student Zaid Khan (advised by @mohitban47) for being awarded a prestigious NDSEG Fellowship for his work on environment generation! Established in 1989, the fellowship has an acceptance rate of <7% and covers diverse science and engineering disciplines.
Check out 🚨CAPTURe🚨 -- a new benchmark and task testing spatial reasoning by making VLMs count objects under occlusion. Key Takeaways: ➡️ SOTA VLMs (GPT-4o, Qwen2-VL, Intern-VL2) have high error rates on CAPTURe (but humans get very low error ✅) and models struggle to reason…
I will be presenting our recent work, “DWIM: Towards Tool-aware Visual Reasoning via Discrepancy-aware Workflow Generation & Instruct-Masking Tuning,” at #ICCV2025 . ⌚️Oct. 21, 11:30-13:30 👉Exhibit Hall I, #314
🚨 If you are at #ICCV2025, make sure to talk to Jaemin for his new group at @jhuclsp @JHUCompSci -- he has done a lot of foundational research in multimodality+other areas & will be a great advisor! 👇👇
Excited to be at #ICCV2025 in Hawaii!🌴 I'll present two papers: M3DocVQA/M3DocRAG (Mon) and CAPTURe (Tue). Check our poster sessions👇 and feel free to ping me to grab a coffee together I'm hiring PhD students to work on multimodal AI and robotics with me at JHU from Fall 2026!
Excited to be at #ICCV2025 in Hawaii!🌴 I'll present two papers: M3DocVQA/M3DocRAG (Mon) and CAPTURe (Tue). Check our poster sessions👇 and feel free to ping me to grab a coffee together I'm hiring PhD students to work on multimodal AI and robotics with me at JHU from Fall 2026!
How can an agent reverse engineer the underlying laws of an unknown, hostile & stochastic environment in “one life”, without millions of steps + human-provided goals / rewards? In our work, we: 1️⃣ infer an executable symbolic world model (a probabilistic program capturing…
🚨 Excited to announce "One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration" --> (1) Our agent can infer/reverse engineer the laws of an unknown, stochastic environment from a single, unguided episode -- without requiring…
How can an agent reverse engineer the underlying laws of an unknown, hostile & stochastic environment in “one life”, without millions of steps + human-provided goals / rewards? In our work, we: 1️⃣ infer an executable symbolic world model (a probabilistic program capturing…
Thanks @_akhaliq for sharing our work! We show how an agent can infer a world model as a program for an unknown, stochastic environment from one life and use the resulting world model for planning + simulating future states of environment! For those interested, please feel free…
🚨 Excited to share new work on inferring symbolic world models from observations! OneLife can infer world models in stochastic, complex environments by proposing rules via LLM and reweighting code-based environment laws from observations collected in a single interaction…
How can an agent reverse engineer the underlying laws of an unknown, hostile & stochastic environment in “one life”, without millions of steps + human-provided goals / rewards? In our work, we: 1️⃣ infer an executable symbolic world model (a probabilistic program capturing…
🚨Introducing OneLife, a new framework to learn world dynamics as a executable probabilistic program, from a single, unguided episode in a stochastic, complex environment. ✨Highlights: ➡️ Inference only routes through relevant laws, solving scaling challenges in complex state…
How can an agent reverse engineer the underlying laws of an unknown, hostile & stochastic environment in “one life”, without millions of steps + human-provided goals / rewards? In our work, we: 1️⃣ infer an executable symbolic world model (a probabilistic program capturing…
🚨 Excited to share our new work ✨ OneLife ✨, which investigates how an agent can infer executable symbolic world models 🌐 from a single unguided trajectory in a stochastic environment. I’m especially excited about our planning + evaluation contributions: 1️⃣ We support…
How can an agent reverse engineer the underlying laws of an unknown, hostile & stochastic environment in “one life”, without millions of steps + human-provided goals / rewards? In our work, we: 1️⃣ infer an executable symbolic world model (a probabilistic program capturing…
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