# ai evaluation Published articles for ai evaluation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## EveryEvalEver aims to standardize AI benchmark reporting and sharing DevFeed: [EveryEvalEver aims to standardize AI benchmark reporting and sharing]() Original publisher: [Read original article]() Author: Kim Martineau Published: 2026-07-23T14:00:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Artificial Intelligence](), [benchmarking](), [LLM evaluation / benchmarking](), [ibm](), [hugging face](), [AI Models](), [data]() Tags: [ai](), [ai-evaluation](), [ai-models](), [ai-transparency](), [benchmarking](), [fairness-accountability-transparency](), [generative-ai](), [hugging-face](), [ibm](), [news](), [performance](), [reporting]() ### AI overview IBM, Hugging Face, and academic collaborators launched EveryEvalEver to make AI benchmark results easier to compare, replicate, and reuse. The project combines standardized reporting with a crowdsourced database of model evaluation results. ### Source excerpt IBM is part of a global team trying to make AI benchmarking results easier to compare, replicate, and reuse. ## Harness Bench: как оценить агентский harness и выбрать связку с моделью DevFeed: [Harness Bench: как оценить агентский harness и выбрать связку с моделью]() Original publisher: [Read original article]() Author: andrivasg (red\_mad\_robot) Published: 2026-06-30T11:30:03Z Content type: article Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [Artificial Intelligence]() Tags: [agent-harness](), [ai](), [ai-e2239b5ae8fa](), [ai-evaluation](), [benchmark](), [harness](), [llm](), [mcp]() ### AI overview The article introduces Harness Bench, an open framework for evaluating model-and-harness combinations on real tasks under consistent conditions. It explains why an agent harness affects an AI system's practical capabilities, discusses problems in open-source harnesses that disrupt automated evaluation, and describes how existing benchmarks can be used for reproducible testing. ### Source excerpt Привет! Я Андрей Иванов, NLP-исследователь в R&D-лаборатории red_mad_robot. Когда мы собираем AI-агента, первым делом выбираем модель под задачу. Но в реальном приложении она не работает в одиночку, ей нужен агентский harness -- программная обвязка. Поэтому выбирать приходится не просто модель, а связку "модель + harness". Чтобы делать этот выбор осознанно, мы создали Harness Bench -- открытый фреймворк, который тестирует связки на реальных задачах в одинаковых условиях. В статье расскажу, как он устроен, разберу баги опенсорсных обвязок, которые ломают автоматический прогон, а потом покажу на цифрах, как смена harness влияет на способности одной и той же модели. Читать далее ## Evaluating AI at Scale: How Thumbtack Approaches Reliability, Safety, and Quality in GenAI DevFeed: [Evaluating AI at Scale: How Thumbtack Approaches Reliability, Safety, and Quality in GenAI]() Original publisher: [Read original article]() Author: Thumbtack Engineering Published: 2026-04-29T00:16:16Z Content type: article Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [Human-AI evaluation](), [genai](), [trust & safety]() Tags: [ai](), [ai-engineering](), [ai-evaluation](), [data-science](), [evaluation](), [genai](), [research](), [safety](), [technical]() ### AI overview Thumbtack describes a learning-driven, exploratory approach to evaluating generative AI experiences. Its strategy combines cross-functional insights with a parallel-path MVP evaluation system to address probabilistic outputs, unsupported claims, harmful content, changing model behavior, and trust-related risks. ### Source excerpt A practical look at how Thumbtack navigates evaluation for emerging AI experiences and what we've learned along the way. By: Shishir Dash, Director of Applied Science & Teja Venkat Kolli, Senior Applied Scientist Evaluating AI at ScaleIntroduction AI is reshaping how people interact with products, and Thumbtack is no exception. We're introducing AI into more aspects of our customer and local service professional (pro) experiences -- from helping customers articulate what they need, to generating helpful summaries, to offering clearer explanations of how pros may fit those needs. But evaluating generative AI is uniquely challenging. Unlike traditional software, its outputs are probabilistic, wide-ranging, and capable of subtle errors: mistakes in tone, inaccuracies, unsupported claims, or harmful assumptions. Rather than attempt to formalize a single rigid evaluation framework, we've taken a learning-driven, exploratory approach, pairing cross-functional insights with a parallel-path MVP evaluation system. This balanced strategy allows us to move quickly while staying grounded in safety, responsibility, and quality. Why AI Evaluation Matters Evaluation is essential because generative AI can produce unsupported or overly strong claims. Sometimes it can misinterpret user intent or vary in style or tone from one version to the next. It can sometimes generate harmful, biased, or inappropriate content. It can also drift over time due to model updates or prompt changes. For a marketplace built on trust, these challenges matter. Customers need accurate guidance; pros need fair, clear representation. Evaluation helps ensure every AI interaction strengthens and not undermines that trust. Our Approach: Exploration, Learning, and MVP Paths The landscape of AI evaluation is still evolving. New research, tooling, and patterns emerge every month. Rather than over-commit to a single approach, we've adopted a mixed strategy rooted in: Exploration and fast learning across multiple pro