Yu-Chiang Frank Wang

ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning

Chaehyun Kim, Chi-Pin Huang, Jisoo Kim, et al.

Mid-Harness: Scaling Actions Between Model and Harness for Terminal Agents

Minki Kang, Ryo Hachiuma, Shaokun Zhang, et al.

Bridging Physical Reasoning and Task Generalization via Visual Action Outcome Reasoning Alignment

Han-Jun Ko, Jr-Jen Chen, Haobo Yuan, et al.

Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients

Byung-Kwan Lee, Ximing Lu, Shizhe Diao, et al.

SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning

Seokju Cho, Ryo Hachiuma, Abhishek Badki, et al.

Seeing Fast and Slow: Learning the Flow of Time in Videos

Yen-Siang Wu, Rundong Luo, Jingsen Zhu, et al.

How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation

Ke-Han Lu, Szu-Wei Fu, Chao-Han Huck Yang, et al.

This paper investigates how much knowledge about sounds and audio Large Language Models (LLMs) naturally learn from text-only training, and whether this affects their performance when adapted to handle audio. The researchers test different LLMs in three ways: directly questioning them about audio concepts, having them reason about audio descriptions, and fine-tuning them with audio data. They find that the amount of audio knowledge varies significantly between different LLM families, and importantly, LLMs that show better audio understanding in text-only tests also perform better when actually processing audio.