collaborators

8 papers

cs.CL2026

OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources

Jinheon Baek, Soyeong Jeong, Sangwoo Park +5

Real-world information needs require access to structurally diverse knowledge sources, from unstructured text and relational tables to knowledge graphs and property graphs. Existin…

cs.CL2026

Soohak: A Mathematician-Curated Benchmark for Evaluating Research-level Math Capabilities of LLMs

Guijin Son, Seungone Kim, Catherine Arnett +73

Following the recent achievement of gold-medal performance on the IMO by frontier LLMs, the community is searching for the next meaningful and challenging target for measuring LLM…

cs.LG2026

It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs

Sangwoo Park, Woongyeong Yeo, Seanie Lee +6

Contextual Integrity (CI) defines privacy not merely as keeping information hidden, but as governing information flows according to the norms of a given context. As large language…

cs.AI2026

Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR

Chanuk Lee, Sangwoo Park, Minki Kang +1

Reinforcement learning with verifiable rewards (RLVR) is a scalable paradigm for improving the mathematical reasoning of large language models, but it is fundamentally limited by e…

cs.AI2026

PREPING: Zero-Task Agent Memory Construction via Self-Practice

Yumin Choi, Sangwoo Park, Minki Kang +2

Agent memory is typically constructed either offline from curated demonstrations or online from post-deployment interactions. However, regardless of how it is built, an agent faces…

cs.CR2026

T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search

Hyomin Lee, Sangwoo Park, Yumin Choi +3

While prior red-teaming efforts have focused on eliciting harmful text outputs from large language models (LLMs), such approaches fail to capture agent-specific vulnerabilities tha…

cs.AI2026

THINKSAFE: Self-Generated Safety Alignment for Reasoning Models

Seanie Lee, Sangwoo Park, Yumin Choi +6

Large reasoning models (LRMs) achieve remarkable performance by leveraging reinforcement learning (RL) on reasoning tasks to generate long chain-of-thought (CoT) reasoning. However…

cs.IR2025

Chain of Retrieval: Multi-Aspect Iterative Search Expansion and Post-Order Search Aggregation for Full Paper Retrieval

Sangwoo Park, Jinheon Baek, Soyeong Jeong +1

Scientific paper retrieval, particularly framed as document-to-document retrieval, aims to identify relevant papers in response to a long-form query paper, rather than a short quer…