1 citations · 1 across the 8 of their papers we have counts for
8 papers
SVoT: State-aware Visualization-of-Thought for Spatial Reasoning via Reinforcement Learning
Chao Lei, Yanbei Jiang, Markus Hiller +4
Spatial reasoning remains a challenge for Multimodal Large Language Models (MLLMs), as it requires reliable multi-hop inference over both intermediate states and state transitions.…
From Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmark
Chao Lei, Nir Lipovetzky, Krista A. Ehinger +1
Recent reasoning-oriented LLMs have demonstrated strong performance on challenging tasks such as mathematics and science examinations. However, core cognitive faculties of human in…
State-Based Disassembly Planning
Chao Lei, Nir Lipovetzky, Krista A. Ehinger
It has been shown recently that physics-based simulation significantly enhances the disassembly capabilities of real-world assemblies with diverse 3D shapes and stringent motion co…
Planning-Driven Programming: A Large Language Model Programming Workflow
Chao Lei, Yanchuan Chang, Nir Lipovetzky +1
The strong performance of large language models (LLMs) raises extensive discussion on their application to code generation. Recent research suggests continuous program refinements…
Generalized Planning for the Abstraction and Reasoning Corpus
Chao Lei, Nir Lipovetzky, Krista A. Ehinger
The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that poses difficulties for pure machine learning methods due to its requirement for fluid…
Novelty and Lifted Helpful Actions in Generalized Planning
Chao Lei, Nir Lipovetzky, Krista A. Ehinger
It has been shown recently that successful techniques in classical planning, such as goal-oriented heuristics and landmarks, can improve the ability to compute planning programs fo…
Amodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmark
Jiayang Ao, Qiuhong Ke, Krista A. Ehinger
Images of realistic scenes often contain intra-class objects that are heavily occluded from each other, making the amodal perception task that requires parsing the occluded parts o…
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors
Ruihan Zhang, Prashan Madumal, Tim Miller +2
Convolutional neural network (CNN) models for computer vision are powerful but lack explainability in their most basic form. This deficiency remains a key challenge when applying C…