Zhe Dong

2 papers indexed

arxivcs.AIcs.CLcs.LG2026-06-29

When Does Learning to Stop Help? A Cost-Aware Study of Early Exits in Reasoning Models

Reasoning models spend test-time compute unevenly across instances, and a growing family of early-exit rules -- confidence thresholds, entropy monitors, answer-stability checks, and learned stoppers -- promises to reclaim the waste. These rules, however, are evaluated under heter…

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arxivcs.IRcs.LG2026-06-29

Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation

Zhe Dong, Fang Qin, Manish Shah, Yicheng Wang

Large language models (LLMs) are increasingly used as rerankers in recommender systems, with the expectation that semantic understanding will help in cold-start and long-tail regimes. We test this assumption with a five-domain benchmark that explicitly separates reranking quality…

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