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…
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…
🚨 New Paper Alert! Introducing SciVideoBench — a comprehensive benchmark for scientific video reasoning! 🔬SciVideoBench: 1. Spans Physics, Chemistry, Biology & Medicine with authentic experimental videos. 2. Features 1,000 challenging MCQs across three reasoning types:…
We welcome Prof. Mohit Bansal (UNC Chapel Hill) as a keynote speaker at #CODS2025! Director of UNC’s MURGe-Lab, he works in multimodal generative models, reasoning agents & faithful language generation. He is an AAAI Fellow, PECASE and multiple best paper awardee.
🚨 Thrilled to introduce Self-Improving Demonstrations (SID) for Goal-Oriented Vision-and-Language Navigation — a scalable paradigm where navigation agents learn to explore by teaching themselves. ➡️ Agents iteratively generate and learn from their own successful trajectories ➡️…
Thanks for the shoutout! 🇨🇦I’ll be at #COLM2025 presenting two papers: GenerationPrograms (Attribution): Poster Session 4, Oct 8th, 4:30 PM QAPyramid (Summarization Eval): Poster Session 5, Oct 9th, 11:00 AM I’m also on the industry job market for research scientist roles.…
🚨 Check out our awesome students/postdocs' papers at #COLM2025 and say hi to them (several are on the job market or hiring) --> -- Archiki, David are on the post-PhD job market! -- Elias finished his postdoc & is now faculty at UT-Austin CS and looking to admit PhD students!…
❗️Self-evolution is quietly pushing LLM agents off the rails. ⚠️ Even perfect alignment at deployment can gradually forget human alignment and shift toward self-serving strategies. Over time, LLM agents stop following values, imitate bad strategies, and even spread misaligned…
🚨 Introducing ATP — Alignment Tipping Process! 🔥 Beware! Self-Evolution is gradually pushing LLM Agents off the rails! Even perfect alignment at deployment can gradually forget human alignment and shift toward self-serving strategies. #AI #LLM #Agents #SelfEvolving #Alignment…
I am attending #COLM2025 🇨🇦 this week to present our work on: Unit Test Generation: 📅 Oct 8th (Wed), 4:30 PM, #79 RAG with conflicting evidence: 📅 Oct 9th (Thu), 11 AM, #71 PS: I'm on the industry job market for RS roles, so you can reach me via DM or in-person to chat! 😄
🚨 Check out our awesome students/postdocs' papers at #COLM2025 and say hi to them (several are on the job market or hiring) --> -- Archiki, David are on the post-PhD job market! -- Elias finished his postdoc & is now faculty at UT-Austin CS and looking to admit PhD students!…
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