activity
20242026
most citedFRIEDA: Benchmarking Multi-Step Cartographic Reasoning in Vision-Language Models

1 citations · 2 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CL2026

Targeting Pivotal Decisions for Credit Assignment in Agentic Reinforcement Learning

Dongwon Jung, Hemanth Neelgund Ramesh, Yifan Wang +7

Group Relative Policy Optimization (GRPO) has become a promising approach for training large language model agents. However, its uniform assignment of trajectory-level advantages t…

cs.AI2026

Model-Aware Data Selection from In-and-Out Information Interplay

Yifan Wang, Xiaomin Li, Yuexing Hao +7

LLMs are effective representations that assimilate vast amounts of knowledge during pretraining, but post-training is necessary for models to reliably access this knowledge and "kn…

cs.CL2026

Adaptive Latent Agentic Reasoning

Dongwon Jung, Peng Shi, Yi Zhang +2

Large reasoning models improve performance by generating extended chain-of-thought (CoT) reasoning, but this behavior becomes inefficient when applied to LLM agents. Current LLM ag…

cs.CV2025★ 1 cited

FRIEDA: Benchmarking Multi-Step Cartographic Reasoning in Vision-Language Models

Jiyoon Pyo, Yuankun Jiao, Dongwon Jung +11

Cartographic reasoning is the skill of interpreting geographic relationships by aligning legends, map scales, compass directions, map texts, and geometries across one or more map i…

cs.SE2025★ 1 cited

RedCoder: Automated Multi-Turn Red Teaming for Code LLMs

Wenjie Jacky Mo, Qin Liu, Xiaofei Wen +5

Large Language Models (LLMs) for code generation (i.e., Code LLMs) have demonstrated impressive capabilities in AI-assisted software development and testing. However, recent studie…

cs.CL2025

Code Execution as Grounded Supervision for LLM Reasoning

Dongwon Jung, Wenxuan Zhou, Muhao Chen

Training large language models (LLMs) with chain-of-thought (CoT) supervision has proven effective for enhancing their reasoning abilities. However, obtaining reliable and accurate…

cs.CL2024

Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation

Dongwon Jung, Qin Liu, Tenghao Huang +2

Retrieval-augmented generation (RAG) improves large language models (LMs) by incorporating non-parametric knowledge through evidence retrieved from external sources. However, it of…

cs.CL2024

Planning and Editing What You Retrieve for Enhanced Tool Learning

Tenghao Huang, Dongwon Jung, Muhao Chen

Recent advancements in integrating external tools with Large Language Models (LLMs) have opened new frontiers, with applications in mathematical reasoning, code generators, and sma…