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20232026
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cs.LG2026

New Model, Old Habits: Behavior Transfer under Model Swapping in Multi-Session LLM Agents

Youngbin Choi, Sehyun Hwang, Suhyeon Jun +4

Multi-session agents can switch between LLMs to balance performance and inference cost. Such switches require transferring a history for continuity, yet their effects on the recipi…

cs.LG2026

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…

cs.LG2026

The Interplay of Harness Design and Post-Training in LLM Agents

Kyungmin Kim, Youngbin Choi, Seoyeon Lee +3

Tool-integrated LLM agents are often wrapped within a harness: the scaffolding that determines which tools are exposed, how they are described, and what auxiliary information accom…

cs.LG2026

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…

cs.MA2026

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…

cs.LG2026

Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation

Seokwon Yoon, Youngbin Choi, Seunghyuk Cho +3

Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific discovery, where exploring multip…