Adding Benchmaxxer Repellant to the Open ASR Leaderboard
Hugging Face describes measures taken to prevent benchmark gaming ('benchmaxxing') on the Open ASR Leaderboard by introducing private or held-out evaluation data. The post addresses the integrity of automatic speech recognition benchmarks, where models may be overfitted or tuned specifically to public test sets. This is part of a broader effort to maintain meaningful leaderboard rankings as ASR model submissions increase.
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Open ASR Leaderboard: Trends and Insights with New Multilingual & Long-Form Tracks
Hugging Face has updated its Open ASR Leaderboard to include new multilingual and long-form audio transcription evaluation tracks. The post analyzes trends across submitted automatic speech recognition models, providing comparative benchmarking data across languages and extended audio contexts. This expands the leaderboard's coverage beyond English short-form ASR to better reflect real-world deployment scenarios.
Introducing the Red-Teaming Resistance Leaderboard
Hugging Face and Haize Labs have launched a Red-Teaming Resistance Leaderboard to systematically benchmark how well AI models resist adversarial prompting and jailbreak attempts. The leaderboard provides a standardized evaluation framework for comparing model robustness against red-teaming attacks. This fills a gap in the evaluation ecosystem where safety and adversarial robustness metrics have been less formalized than capability benchmarks.
Automated Benchmark Auditing for AI Agents and Large Language Models (ABA)
The paper introduces Auto Benchmark Audit (ABA), an agentic framework that systematically audits AI benchmark tasks for issues such as ambiguous specifications, environment conflicts, and incorrect ground truths. Applied to 168 benchmarks across nine domains including NeurIPS publications, ABA identifies critical issues in over 25.7% of evaluated tasks. The authors demonstrate that filtering out flawed tasks materially shifts model rankings and improves average performance on SWE-bench Verified and Terminal-Bench 2 by 9.9% and 9.6% respectively, indicating that current benchmark scores are significantly distorted by task quality problems. The agentic tool and annotations are released publicly.
Benchmark Agent: Autonomous system for end-to-end benchmark construction
Researchers introduce Benchmark Agent, a fully autonomous agentic system that orchestrates the complete benchmark construction pipeline — from query analysis and subtask design to data annotation and quality control. The system was used to produce 15 benchmarks spanning text understanding, multimodal understanding, and domain-specific reasoning, with evaluation via human judges, LLM-as-a-judge, and consistency checks. The work addresses two persistent problems in the field: the labor intensity of benchmark creation and rapid performance saturation after release. Code and a demo will be publicly released.
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.
Introducing the LiveCodeBench Leaderboard - Holistic and Contamination-Free Evaluation of Code LLMs
Hugging Face introduces a leaderboard based on LiveCodeBench, a benchmark designed for holistic and contamination-free evaluation of code-generating large language models. The benchmark continuously collects new coding problems from competitive programming platforms to prevent data contamination that plagues static benchmarks. It evaluates models across multiple code-related tasks beyond just code generation, aiming to provide a more reliable signal of true model capability.
Evaluating Audio Reasoning with Big Bench Audio
Hugging Face introduces Big Bench Audio, a new benchmark designed to evaluate audio reasoning capabilities in AI models. The benchmark appears to extend the Big Bench evaluation framework into the audio domain, targeting multimodal models that process and reason over audio inputs. This release addresses a gap in evaluation tooling for audio-capable language models.
The Open Agent Leaderboard
IBM Research and Hugging Face have launched the Open Agent Leaderboard, a public benchmark for evaluating AI agents across standardized tasks. The leaderboard aims to provide transparent, reproducible comparisons of open and proprietary agent systems. This initiative addresses the growing need for rigorous evaluation infrastructure as the agent ecosystem matures.