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Jiaqiang Li

2 accepted papers

2025

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint

EMNLP 2025

Recent work has demonstrated the remarkable potential of Large Language Models (LLMs) in test-time scaling. By making models think before answering, they are able to achieve much higher accuracy with extra inference computation.However, in many real-world scenarios, models are used under time constr

Cited by 0SourcePDFScholar
2025

Personalized Federated Learning under Local Supervision

ICCV 2025poster

A crucial issue in federated learning is the heterogeneity of data across clients, which may lead to model divergence, eventually deteriorating the model performance. Personalized federated learning (pFL) has been shown to be an effective approach to addressing data heterogeneity in federated learni…