15 papers
Verifying Meta-Awareness via Predictive Rewards in Reasoning Models
Yoonjeon Kim, Doohyuk Jang, Eunho Yang
Recent research on reasoning models explores the meta-awareness of language models, including their ability to determine optimal thinking duration, recognize knowledge boundaries,…
Discounted Beta-Bernoulli Reward Estimation for Sample-Efficient Reinforcement Learning with Verifiable Rewards
Haechan Kim, Soohyun Ryu, Gyouk Chu +2
Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective post-training paradigm for improving the reasoning capabilities of large language models. However,…
Argument Reconstruction as Supervision for Critical Thinking in LLMs
Hyun Ryu, Gyouk Chu, Gregor Betz +3
To think critically about arguments, human learners are trained to identify, reconstruct, and evaluate arguments. Argument reconstruction is especially important because it makes a…
CollabVR: Collaborative Video Reasoning with Vision-Language and Video Generation Models
Joowon Kim, Seungho Shin, Joonhyung Park +1
Recent "Thinking with Video" approaches use Video Generation Models (VGMs) for visual reasoning by producing temporally coherent Chain-of-Frames as reasoning artifacts. Even strong…
Accelerating Vision Transformers with Adaptive Patch Sizes
Rohan Choudhury, JungEun Kim, Jinhyung Park +3
Vision Transformers (ViTs) partition input images into uniformly sized patches regardless of their content, resulting in long input sequence lengths for high-resolution images. We…
Integrating Multimodal Large Language Model Knowledge into Amodal Completion
Heecheol Yun, Eunho Yang
With the widespread adoption of autonomous vehicles and robotics, amodal completion, which reconstructs the occluded parts of people and objects in an image, has become increasingl…