9 papers
Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models
John Scoville, Shengzhuang Chen, Yejin Bang +2
Recent meta-reasoning frameworks improve LLM reasoning by wrapping chain-of-thought generation in an iterative control loop, allowing more effective backtracking, termination of re…
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-…
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…
What Makes for Good Image Captions?
Delong Chen, Samuel Cahyawijaya, Etsuko Ishii +3
This paper establishes a formal information-theoretic framework for image captioning, conceptualizing captions as compressed linguistic representations that selectively encode sema…
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…
HalluLens: LLM Hallucination Benchmark
Yejin Bang, Ziwei Ji, Alan Schelten +5
Large language models (LLMs) often generate responses that deviate from user input or training data, a phenomenon known as "hallucination." These hallucinations undermine user trus…