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Groups Don't Move as One: A Bregman Clustering View of Linearized Group Influence
Hyunho Choi, Jaeseung Heo, Youngbin Choi +2
Influence functions provide an efficient alternative to retraining for assessing the effects of removing or reweighting training data. For groups, however, the standard first-order…
Scaling Influence Functions in LLMs through Eigenbasis-Corrected One-Bit Gradient Projection
Jaeseung Heo, J Rosser, Dongwoo Kim
Influence functions estimate how individual training examples affect the behavior of large language models (LLMs). Analyzing how training data influence different behaviors of an L…
Interaction-Aware Influence Functions for Group Attribution
Jaeseung Heo, Kyeongheung Yun, Youngbin Choi +3
Influence functions approximate how removing a training example changes a quantity of interest, called the target function, such as a held-out loss. To estimate the influence of a…
Long Live the Librarian! A Persistent Search Sub-Agent for Energy-Efficient Multi-Agent Software Engineering Systems
Seunghyuk Cho, Sunghyun Choi, Jaeseung Heo +4
Multi-agent systems (MAS) have substantially advanced autonomous software engineering (SWE), but their growing inference energy demands raise sustainability concerns. In this paper…
Transductive Generalization via Optimal Transport and Its Application to Graph Node Classification
MoonJeong Park, Seungbeom Lee, Kyungmin Kim +5
Many existing transductive bounds rely on classical complexity measures that are computationally intractable and often misaligned with empirical behavior. In this work, we establis…