Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking
Agent benchmarks are substantially more costly to evaluate than conventional LLM benchmarks. Benchmark compression is therefore a natural solution, yet existing methods primarily model redundancy in task--model final-score distributions, which is important in agentic evaluation. To address this limitation, we analyze l...
Xin-Shuai Guo, Jun-Jie Wu, Dolly Deng et al.
· 0 citations