9 citations · 15 across the 10 of their papers we have counts for
10 papers
Reward Prediction with Factorized World States
Yijun Shen, Delong Chen, Xianming Hu +4
Agents must infer action outcomes and select actions that maximize a reward signal indicating how close the goal is to being reached. Supervised learning of reward models could int…
Action100M: A Large-scale Video Action Dataset
Delong Chen, Tejaswi Kasarla, Yejin Bang +6
Inferring physical actions from visual observations is a fundamental capability for advancing machine intelligence in the physical world. Achieving this requires large-scale, open-…
TV2TV: A Unified Framework for Interleaved Language and Video Generation
Xiaochuang Han, Youssef Emad, Melissa Hall +15
Video generation models are rapidly advancing, but can still struggle with complex video outputs that require significant semantic branching or repeated high-level reasoning about…
VL-JEPA: Joint Embedding Predictive Architecture for Vision-language
Delong Chen, Mustafa Shukor, Theo Moutakanni +7
We introduce VL-JEPA, a vision-language model built on a Joint Embedding Predictive Architecture (JEPA). Instead of autoregressively generating tokens as in classical VLMs, VL-JEPA…
Planning with Reasoning using Vision Language World Model
Delong Chen, Theo Moutakanni, Willy Chung +4
Effective planning requires strong world models, but high-level world models that can understand and reason about actions with semantic and temporal abstraction remain largely unde…
Embodied AI Agents: Modeling the World
Pascale Fung, Yoram Bachrach, Asli Celikyilmaz +18
This paper describes our research on AI agents embodied in visual, virtual or physical forms, enabling them to interact with both users and their environments. These agents, which…
WorldPrediction: A Benchmark for High-level World Modeling and Long-horizon Procedural Planning
Delong Chen, Willy Chung, Yejin Bang +2
Humans are known to have an internal "world model" that enables us to carry out action planning based on world states. AI agents need to have such a world model for action planning…
Linguistic Minimal Pairs Elicit Linguistic Similarity in Large Language Models
Xinyu Zhou, Delong Chen, Samuel Cahyawijaya +2
We introduce a novel analysis that leverages linguistic minimal pairs to probe the internal linguistic representations of Large Language Models (LLMs). By measuring the similarity…
LLM Internal States Reveal Hallucination Risk Faced With a Query
Ziwei Ji, Delong Chen, Etsuko Ishii +4
The hallucination problem of Large Language Models (LLMs) significantly limits their reliability and trustworthiness. Humans have a self-awareness process that allows us to recogni…
Towards Joint Modeling of Dialogue Response and Speech Synthesis based on Large Language Model
Xinyu Zhou, Delong Chen, Yudong Chen
This paper explores the potential of constructing an AI spoken dialogue system that "thinks how to respond" and "thinks how to speak" simultaneously, which more closely aligns with…