As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to assess multimodal perception, multi-step execution, tool use, and artifact delivery. However, existing benchmarks are often tied to specific task types, execution environ...
Yu Liu, Zhi-Lin Liu, Zhi-Wei Yang et al.· 0 citations
DAREBench (Deployment-Aware and Reliable Evaluation of Models as Agents), a benchmark designed to capture workload variation and support reliable agent evaluation, is introduced, suggesting that agent deployment and model selection should consider workload profiles, deployment mode, and accuracy--cost trade-offs rather...
Yu Liu, Zhi-Lin Liu, Zhi-Wei Yang et al.· 0 citations
Experiments across multiple long-context narrative question answering and claim verification settings show that ClueWeaver substantially improves local end-to-end language models while providing evidence coverage and paragraph-referenced reasoning traces.
Ji-Hao Zhu, Zhi-Wei Yang, Wen-Xiao Zhang et al.· 0 citations
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