From the 2 of 12 linked papers with an AI index.
12 papers
Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
Yansen Zhang, Yilu Liu, Tianyu Liu +6
The paper proposes CostAda, a cost‑aware controller that guides large language model‑based discovery by evaluating frontier progress relative to the token cost incurred, enabling m…
UXBench: Benchmarking User Experience in AI Assistants
Mengze Hong, Xia Zeng, Zeyang Lei +26
UXBench is a user‑centric benchmark that uses real interaction logs to evaluate how well AI assistants align with user preferences and generate engaging dialogue, featuring three t…
SEAL: Can Saturated Benchmarks Be Revived by LLM-as-a-Meta-Judge?
Jiamin Chen, Yidi Wu, Qiexiang Wang +6
Widely used language-model benchmarks are increasingly saturated, with frontier systems often receiving near-tied scores that standard metrics cannot resolve. Rather than construct…
DirectorBench: Diagnosing Long-Form Video Generation with Personalized Multi-Agent Evaluation
Jiamin Chen, Qianben Chen, Jiawen Zhang +5
Long-form video generation is rapidly moving from short, single-scene synthesis toward minute-long, multi-shot creation with narrative structure, cinematic control, audio, and cros…
Beyong Tokens: Item-aware Attention for LLM-based Recommendation
Xiaokun Zhang, Bowei He, Jiamin Chen +2
Large Language Models (LLMs) have recently gained increasing attention in the field of recommendation. Existing LLM-based methods typically represent items as token sequences, and…
PASER: Post-Training Data Selection for Efficient Pruned Large Language Model Recovery
Bowei He, Lihao Yin, Hui-Ling Zhen +3
Model pruning is an effective approach for compressing large language models (LLMs). However, this process often leads to significant degradation of model capabilities. While post-…