5 citations · 6 across the 13 of their papers we have counts for
14 papers
ResearchMath-14K: Scaling Research-Level Mathematics via Agents
Guijin Son, Seungyeop Yi, Minju Gwak +3
The frontier of mathematics is defined by problems whose solutions are not yet known. However, whether language models can meaningfully engage with such problems without human inte…
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…
KMMMU: Evaluation of Massive Multi-discipline Multimodal Understanding in Korean Language and Context
Nahyun Lee, Guijin Son, Hyunwoo Ko +4
Multimodal evaluation is strengthened when benchmarks reflect the languages, institutions, and disciplinary conventions that shape expert visual reasoning. We introduce KMMMU, a na…
Judging What We Cannot Solve: A Consequence-Based Approach for Oracle-Free Evaluation of Research-Level Math
Guijin Son, Donghun Yang, Hitesh Laxmichand Patel +5
Recent progress in reasoning models suggests that generating plausible attempts for research-level mathematics may be within reach, but verification remains a bottleneck, consuming…
What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Models
Dasol Choi, Guijin Son, Hanwool Lee +7
Current vision-language benchmarks predominantly feature well-structured questions with clear, explicit prompts. However, real user queries are often informal and underspecified. U…
Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thought
Guijin Son, Donghun Yang, Hitesh Laxmichand Patel +9
Recent frontier models employ long chain-of-thought reasoning to explore solution spaces in context and achieve stonger performance. While many works study distillation to build sm…
KAIO: A Collection of More Challenging Korean Questions
Nahyun Lee, Guijin Son, Hyunwoo Ko +1
With the advancement of mid/post-training techniques, LLMs are pushing their boundaries at an accelerated pace. Legacy benchmarks saturate quickly (e.g., broad suites like MMLU ove…
When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research
Guijin Son, Jiwoo Hong, Honglu Fan +8
Recent advances in large language models (LLMs) have fueled the vision of automated scientific discovery, often called AI Co-Scientists. To date, prior work casts these systems as…
Controlling Language Confusion in Multilingual LLMs
Nahyun Lee, Yeongseo Woo, Hyunwoo Ko +1
Large language models often suffer from language confusion, a phenomenon in which responses are partially or entirely generated in unintended languages. This critically degrades th…
Won: Establishing Best Practices for Korean Financial NLP
Guijin Son, Hyunwoo Ko, Haneral Jung +1
In this work, we present the first open leaderboard for evaluating Korean large language models focused on finance. Operated for about eight weeks, the leaderboard evaluated 1,119…
Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning
Guijin Son, Jiwoo Hong, Hyunwoo Ko +1
Scaling pre-training compute has proven effective for achieving mulitlinguality, but does the same hold for test-time scaling? In this work, we introduce MCLM, a multilingual math…
Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap
Hyunwoo Ko, Guijin Son, Dasol Choi
Large language models (LLMs) demonstrate exceptional performance on complex reasoning tasks. However, despite their strong reasoning capabilities in high-resource languages (e.g.,…
Multi-Step Reasoning in Korean and the Emergent Mirage
Guijin Son, Hyunwoo Ko, Dasol Choi
We introduce HRMCR (HAE-RAE Multi-Step Commonsense Reasoning), a benchmark designed to evaluate large language models' ability to perform multi-step reasoning in culturally specifi…
LLM-as-a-Judge & Reward Model: What They Can and Cannot Do
Guijin Son, Hyunwoo Ko, Hoyoung Lee +2
LLM-as-a-Judge and reward models are widely used alternatives of multiple-choice questions or human annotators for large language model (LLM) evaluation. Their efficacy shines in e…