Metric Hub
Updated from current HFEPX corpus (Feb 26, 2026). 24 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Frequently cited benchmark: MMLU. Common metric signal: calibration. Newest paper in this set is from Feb 25, 2026.
Human Feedback Mix
Agentic Evaluation
Masha Naslidnyk · Feb 25, 2026
Representing, comparing, and measuring the distance between probability distributions is a key task in computational statistics and machine learning.
Charafeddine Mouzouni · Feb 24, 2026
We validate across five benchmarks, five models from three families, and both synthetic and real data.
Mohammad Amanlou, Erfan Shafiee Moghaddam, Yasaman Amou Jafari, Mahdi Noori, Farhan Farsi · Feb 23, 2026
Results show that KNIGHT enables token- and cost-efficient generation from a reusable graph representation, achieves high quality across these criteria, and yields model rankings aligned with MMLU-style benchmarks, while supporting topic-sp
Arindam Khaled · Feb 23, 2026
In this work, we propose "Pyramid MoA", a hierarchical Mixture-of-Agents architecture that uses a lightweight Router to dynamically escalate queries only when necessary.
Minxue Tang, Yangyang Yu, Aolin Ding, Maziyar Baran Pouyan, Taha Belkhouja Yujia Bao · Feb 22, 2026
Recognizing implicit visual and textual patterns is essential in many real-world applications of modern AI.
Mengjie Qian, Guangzhi Sun, Mark J. F. Gales, Kate M. Knill · Feb 18, 2026
Large language models (LLMs) are increasingly applied as automatic evaluators for natural language generation assessment often using pairwise comparative judgements.
Jeffrey T. H. Wong, Zixi Zhang, Junyi Liu, Yiren Zhao · Feb 18, 2026
Existing Multi-Agent Systems (MAS) typically rely on static, homogeneous model configurations, limiting their ability to exploit the distinct strengths of differently post-trained models.
Yunshu Wu, Jiayi Cheng, Partha Thakuria, Rob Brekelmans, Evangelos E. Papalexakis · Feb 18, 2026
On OpenWebText, \textsc{DSL} fine-tuning yields large MAUVE gains at low step budgets, surpassing the MDLM+ReMDM baseline with \(\sim\)4$\times$ fewer denoiser evaluations, and matches autoregressive quality at high budgets.
Zhenhong Zhou, Yuanhe Zhang, Hongwei Cai, Moayad Aloqaily, Ouns Bouachir · Feb 15, 2026
The Model Context Protocol (MCP) standardizes tool use for LLM-based agents and enable third-party servers.
Chenxi Han, Yuheng Min, Zihao Huang, Ao Hong, Hang Liu · Feb 13, 2026
Recent advances in data-driven reinforcement learning and motion tracking have substantially improved humanoid locomotion, yet critical practical challenges remain.
Ha Na Cho, Sairam Sutari, Alexander Lopez, Hansen Bow, Kai Zheng · Jan 24, 2026
Such behavior poses substantial risks for real-world deployment, where overconfident or temporally invalid predictions can disrupt clinical workflows and compromise patient safety.
Haorui Yu, Xuehang Wen, Fengrui Zhang, Qiufeng Yi · Jan 12, 2026
Existing benchmarks assess perception without interpretation, and common evaluation proxies, such as automated metrics and LLM-judge averaging, are unreliable for culturally sensitive generative tasks.
Gaurab Chhetri, Subasish Das, Tausif Islam Chowdhury · Dec 30, 2025
This study develops WISE (Web Information Satire and Fakeness Evaluation) framework which benchmarks eight lightweight transformer models alongside two baseline models on a balanced dataset of 20,000 samples from Fakeddit, annotated as eith
Sohyeon Jeon, Hyung-Chul Lee · Oct 22, 2025
Despite the rapid expansion of Large Language Models (LLMs) in healthcare, robust and explainable evaluation of their ability to assess clinical trial reporting according to CONSORT standards remains an open challenge.
Shiyu Ni, Keping Bi, Jiafeng Guo, Minghao Tang, Jingtong Wu · Oct 20, 2025
To support a large-scale study, we release HonestyBench, a benchmark covering ten free-form QA datasets with 560k training and 70k evaluation instances annotated with correctness and self-consistency signals.
Antonio Martínez-Ibarra, Aurora González-Vidal, Adrián Cánovas-Rodríguez, Antonio F. Skarmeta · Oct 10, 2025
The Mar Menor, Europe's largest hypersaline coastal lagoon, located in southeastern Spain, has undergone severe eutrophication crises, with devastating impacts on biodiversity and water quality.
Tiago Fernandes Tavares · Sep 26, 2025
A qualitative audit by an independent LLM-as-a-judge confirms the discovery of meaningful functional axes, such as policy intent, that thematic ground-truth labels fail to capture.
Denis Makhov, Dmitriy Shopkhoev, Magauiya Zhussip, Ammar Ali, Stamatios Lefkimmiatis · Sep 26, 2025
Post-training compression of large language models (LLMs) often relies on low-rank weight approximations that represent each column of the weight matrix in a shared low-dimensional subspace.
Jing Xiong, Qiujiang Chen, Fanghua Ye, Zhongwei Wan, Chuanyang Zheng · Sep 18, 2025
Large language models (LLMs) benefit from test-time scaling but are often hampered by high inference latency.
Lucas Gautheron, Evan Kidd, Anton Malko, Marvin Lavechin, Alejandrina Cristia · Aug 21, 2025
With the advent of wearable recorders, scientists are increasingly turning to automated methods of analysis of audio and video data in order to measure children's experience, behavior, and outcomes, with a sizable literature employing long-
Dmitriy Shopkhoev, Ammar Ali, Magauiya Zhussip, Valentin Malykh, Stamatios Lefkimmiatis · May 5, 2025
Applied to several large language models (LLMs), ReplaceMe achieves up to 25\% pruning while retaining approximately 90\% of the original model's performance on open benchmarks - without any training or healing steps, resulting in minimal c
Miles Williams, George Chrysostomou, Vitor Jeronymo, Nikolaos Aletras · Feb 25, 2025
Compression techniques such as pruning and quantization offer a practical path towards efficient LM deployment, exemplified by their ability to preserve performance on general-purpose benchmarks.
Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu · Jan 24, 2025
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities.
Qing Lyu, Kumar Shridhar, Chaitanya Malaviya, Li Zhang, Yanai Elazar · Feb 21, 2024
We perform an extensive evaluation across various open and closed-source models on nine reasoning datasets.
