5 citations · 11 across the 10 of their papers we have counts for
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Memory-Aware Reward Modeling: Learning Human Preferences for LLM Memory Use
Yijiang River Dong, Zexue He, Cheng Yu +9
Memory enables language model assistants to maintain continuity across sessions and generate personalized responses, but knowing more about a user does not necessarily produce resp…
Multi-agent AI systems outperform human teams in creativity
Tiancheng Hu, Yixuan Jiang, Haotian Li +5
Although artificial intelligence (AI) now matches or exceeds human performance across numerous cognitive tasks, creativity remains a highly contested frontier. As AI systems based…
Confident Rankings with Fewer Items: Adaptive LLM Evaluation with Continuous Scores
Esma Balkır, Alice Pernthaller, Marco Basaldella +2
Computerized Adaptive Testing (CAT) has proven effective for efficient LLM evaluation on multiple-choice benchmarks, but modern LLM evaluation increasingly relies on generation tas…
Value of Information: A Framework for Human-Agent Communication
Yijiang River Dong, Tiancheng Hu, Zheng Hui +4
Large Language Model (LLM) agents deployed for real-world tasks face a fundamental dilemma: user requests are underspecified, yet agents must decide whether to act on incomplete in…
Steer Model beyond Assistant: Controlling System Prompt Strength via Contrastive Decoding
Yijiang River Dong, Tiancheng Hu, Zheng Hui +1
Large language models excel at complex instructions yet struggle to deviate from their helpful assistant persona, as post-training instills strong priors that resist conflicting in…