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cs.LG2026
CHART: A Harness-Rotation Curriculum for Harness-Robust Search Agents
Xinlu Zhang, Ying-Chun Lin, Zhihan Zhang +2
Search agents are usually trained under a single harness. But once an agent is deployed in a real application, its harness is frequently updated (e.g., a rewritten system prompt) t…
cs.LG2024
Rethinking Node Representation Interpretation through Relation Coherence
Ying-Chun Lin, Jennifer Neville, Cassiano Becker +3
Understanding node representations in graph-based models is crucial for uncovering biases ,diagnosing errors, and building trust in model decisions. However, previous work on expla…
cs.LG2024
Improving Node Representation by Boosting Target-Aware Contrastive Loss
Ying-Chun Lin, Jennifer Neville
Graphs model complex relationships between entities, with nodes and edges capturing intricate connections. Node representation learning involves transforming nodes into low-dimensi…