Every Eval Ever: unified schema and community repository for AI evaluation results
Researchers introduce Every Eval Ever, a shared schema and crowdsourced repository designed to standardize AI evaluation results across incompatible formats, frameworks, and sources. The system ingests results from evaluation harnesses, papers, leaderboards, and custom repositories into a single JSON document format, with optional per-instance output storage. The repository, hosted on Hugging Face, currently covers 22,235 models, 2,273 unique benchmarks, and 31 evaluation formats. The work addresses a persistent infrastructure problem in AI evaluation science: divergent scores for nominally identical evaluations and scattered, incomparable metadata.
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EvalCards: A unified reporting layer for AI evaluation results with interpretive signals
Researchers introduce EvalCards, an operational schema and tooling layer that composes benchmark metadata, evaluation run data, and model metadata into a unified, interpretable record for AI evaluation reporting. The system derives a reporting schema from 52 papers and 10 stakeholder interviews, implements four interpretive signals (reproducibility, documentation completeness, provenance/risk, score comparability), and deploys a monitoring tool across 5,816 models, 635 benchmarks, and 101,843 results. The work targets the widespread inconsistency in how evaluation results are reported across leaderboards, model cards, and company blogs, making cross-source comparison unreliable. It addresses a structural gap in the evaluation ecosystem by providing extraction infrastructure, not just a proposal.
Announcing Evaluation on the Hub
Hugging Face announced Evaluation on the Hub, a new feature enabling users to evaluate any model on any dataset directly within the Hugging Face Hub infrastructure. The tool aims to lower the barrier to standardized model evaluation by integrating evaluation workflows into the existing model and dataset hosting platform. This represents an infrastructure step toward more accessible and reproducible benchmarking in the ML community.
Community Evals: Because we're done trusting black-box leaderboards over the community
Hugging Face introduces Community Evals, a framework aimed at replacing or supplementing opaque black-box leaderboards with community-driven model evaluations. The initiative reflects growing skepticism about the reliability and transparency of existing benchmark leaderboards. By crowdsourcing evaluations, Hugging Face seeks to make model assessment more transparent, diverse, and resistant to gaming. This represents a structural shift in how the open-source AI community approaches model comparison and trust.
AllenAI releases olmo-eval evaluation workbench for model development
AllenAI published a blog post on Hugging Face introducing olmo-eval, an evaluation workbench designed to integrate into the model development loop. The tool appears aimed at streamlining evaluation workflows for researchers iterating on open-weights models. This is relevant to the OLMo model family ecosystem and the broader open-weights evaluation infrastructure space.
Rethinking how we measure AI intelligence
DeepMind has announced Game Arena, a new open-source evaluation platform designed for rigorous head-to-head comparison of frontier AI models. The platform uses environments with clear winning conditions to assess model capabilities. This represents DeepMind's contribution to addressing ongoing concerns about the adequacy of existing AI benchmarks.
Judge Arena: Benchmarking LLMs as Evaluators
Hugging Face and Atla have launched Judge Arena, a platform for benchmarking large language models in their role as automated evaluators. The initiative uses an Elo-based ranking system to compare how well different LLMs judge the quality of model outputs, addressing the growing reliance on LLM-as-judge paradigms in evaluation pipelines. This fills a meta-evaluation gap: as LLM judges become standard practice, understanding their relative reliability and biases becomes critical infrastructure for the field.
Introducing RTEB: A New Standard for Retrieval Evaluation
Hugging Face introduces RTEB (Retrieval Text Embedding Benchmark), a new benchmark designed to standardize evaluation of retrieval systems and text embeddings. The benchmark aims to address gaps in existing evaluation frameworks by providing more comprehensive and realistic retrieval tasks. This represents an effort to improve how the community measures progress in retrieval-augmented generation and semantic search systems.
AI-Assisted Systematization for Evaluating GenAI Systems
This paper addresses a foundational gap in GenAI evaluation: the underspecification of broad, contested concepts like 'reasoning,' 'fairness,' or 'creativity.' The authors introduce a structured artifact called a 'concept spec' and a validation worksheet, then build two AI-assisted systematizers—a zero-shot approach and a multi-agent approach—to convert vague evaluation targets into measurable, structured accounts. They apply these tools to hate-based rhetoric and digital empathy, assessing the resulting specs on content validity and information recoverability. The work positions AI assistance as a scalable aid for the cognitively demanding process of evaluation design.