9 papers
A^2Agent: Action-Aware Reinforcement Learning for Repository-Level Code Localization Agents
Doyeon Kim, Suyoung Bae, Yumin Lee +1
Localizing issue-relevant code regions is a critical step in automated software engineering. However, due to their reliance on sparse trajectory-level signals, existing methods can…
Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems
CheolWon Na, Hao Ni, Lukasz Szpruch +7
LLM-based multi-agent trading systems, in which specialized agents collaborate through structured communication to produce trading decisions, are moving rapidly from research proto…
EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL
Jaehoon Lee, CheolWon Na, Suyoung Bae +4
Text-to-SQL enables users to query databases using natural language by generating executable SQL queries. Recent methods have increasingly adopted Large Language Models based reinf…
Remember Your Trace: Memory-Guided Long-Horizon Agentic Framework for Consistent and Hierarchical Repository-Level Code Documentation
Suyoung Bae, Jaehoon Lee, Changkyu Choi +2
Automated code documentation is essential for modern software development, providing the contextual grounding that both human developers and coding agents rely on to navigate large…
ReFEree: Reference-Free and Fine-Grained Method for Evaluating Factual Consistency in Real-World Code Summarization
Suyoung Bae, CheolWon Na, Jaehoon Lee +3
As Large Language Models (LLMs) have become capable of generating long and descriptive code summaries, accurate and reliable evaluation of factual consistency has become a critical…
TAG: A Simple Yet Effective Temporal-Aware Approach for Zero-Shot Video Temporal Grounding
Jin-Seop Lee, SungJoon Lee, Jaehan Ahn +2
Video Temporal Grounding (VTG) aims to extract relevant video segments based on a given natural language query. Recently, zero-shot VTG methods have gained attention by leveraging…
Q-FAKER: Query-free Hard Black-box Attack via Controlled Generation
CheolWon Na, YunSeok Choi, Jee-Hyong Lee
Many adversarial attack approaches are proposed to verify the vulnerability of language models. However, they require numerous queries and the information on the target model. Even…
SALAD: Improving Robustness and Generalization through Contrastive Learning with Structure-Aware and LLM-Driven Augmented Data
Suyoung Bae, Hyojun Kim, YunSeok Choi +1
In various natural language processing (NLP) tasks, fine-tuning Pre-trained Language Models (PLMs) often leads to the issue of spurious correlations, which negatively impacts perfo…
DeCAP: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models
Suyoung Bae, YunSeok Choi, Jee-Hyong Lee
While Large Language Models (LLMs) excel in zero-shot Question Answering (QA), they tend to expose biases in their internal knowledge when faced with socially sensitive questions,…