- \$OneMillion-Bench: How Far are Language Agents from Human Experts?
Qianyu Yang, Yang Liu, Jiaqi Li, Jun Bai, Hao Chen · Mar 9, 2026 · Citations: 0
Rubric Rating Automatic Metrics Tool Use
To this end, we introduce \OneMillion-Bench \OneMillion-Bench, a benchmark of 400 expert-curated tasks spanning Law, Finance, Industry, Healthcare, and Natural Science, built to evaluate agents across economically consequential scenarios.
- HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam
Weiqi Zhai, Zhihai Wang, Jinghang Wang, Boyu Yang, Xiaogang Li · Feb 15, 2026 · Citations: 0
Expert VerificationCritique Edit Automatic Metrics
Humanity's Last Exam (HLE) has become a widely used benchmark for evaluating frontier large language models on challenging, multi-domain questions.
- Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation
Xue Liu, Xin Ma, Yuxin Ma, Yongchang Peng, Duo Wang · Mar 27, 2026 · Citations: 0
Rubric RatingExpert Verification Automatic Metrics
To bridge this gap, we present XpertBench, a high-fidelity benchmark engineered to assess LLMs across authentic professional domains.
- Stabilizing Iterative Self-Training with Verified Reasoning via Symbolic Recursive Self-Alignment
Xinyu Zhang · Mar 23, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
We further demonstrate that constructing DPO preference pairs from NSRSA verification teaches the model to distinguish sound from flawed reasoning (reward accuracy 46% to 63%).
- A Simple and Efficient Jailbreak Method Exploiting LLMs' Helpfulness
Xuan Luo, Yue Wang, Zefeng He, Geng Tu, Jing Li · Sep 17, 2025 · Citations: 0
Red Team Automatic Metrics
This study reveals a critical safety blind spot in modern LLMs: learning-style queries, which closely resemble ordinary educational questions, can reliably elicit harmful responses.
- Agent Q-Mix: Selecting the Right Action for LLM Multi-Agent Systems through Reinforcement Learning
Eric Hanchen Jiang, Levina Li, Rui Sun, Xiao Liang, Yubei Li · Apr 1, 2026 · Citations: 0
Automatic Metrics Multi Agent
In this paper, we propose Agent Q-Mix, a reinforcement learning framework that reformulates topology selection as a cooperative Multi-Agent Reinforcement Learning (MARL) problem.
- Cross-Context Verification: Hierarchical Detection of Benchmark Contamination through Session-Isolated Analysis
Tae-Eun Song · Mar 23, 2026 · Citations: 0
Automatic Metrics Multi Agent
LLM coding benchmarks face a credibility crisis: widespread solution leakage and test quality issues undermine SWE-bench Verified, while existing detection methods--paraphrase consistency, n-gram overlap, perplexity analysis--never directly…
- Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models
Teng Wang, Zhangyi Jiang, Zhenqi He, Shenyang Tong, Wenhan Yang · Mar 16, 2025 · Citations: 0
Automatic Metrics Long Horizon
Empirical results on the PRM800K dataset show that HRM, together with HNC, provides more stable and reliable evaluations than PRM.