Knowledge catalogue
Standardizing independent evaluation, fairness, and interpretability for mechanism-informed generative AI in healthcare The post MLCommons Joins EU-Funded AIRIS Project to Build and Benchmark Next-Gen
Latest version of the benchmark introduces two new tests for emerging AI deployment patterns, including Agentic Inference The post MLCommons Sets Participation Record with New MLPerf Inference v6.1 Be
TensorRT Edge‑LLM ran Qwen 3.6‑27B on a single NVIDIA Jetson AGX Thor and completed the MLPerf Edge Agentic benchmark in 24 min 36 s—52.33 tokens/s—which is 6.4× faster than the llama.cpp reference (2
The Financial Services Working Group's flagship initiative advances to the final round of the C:>DIR Global 'Agentic Regulator' Hackathon, with winners announced September 18. The post MLCommons Agent
MLCommons announced the release of MLPerf Storage v3.0 benchmark results, expanding the suite to cover the full spectrum of AI storage workloads, including an S3 object‑storage access layer in additio
arXiv:2610.02827v1 Announce Type: new Abstract: Modern AI systems are designed to refuse hazardous requests. A jailbreak is a prompt crafted to bypass those safeguards and elicit outputs that the syst
arXiv:2605.11333v1 Announce Type: cross Abstract: The fast pace of artificial intelligence~(AI) innovation demands an agile methodology for observation, reproduction and optimization of distributed ma
At Google Cloud Next 2026 in Las Vegas, MLCommons and Google Cloud demonstrated a powerful new capability for trustworthy medical AI - one that protects patient data, model IP, and benchmark integrity
Evolving MLPerf Training from feature-interaction baselines to production-scale sequential recommendation with HSTU and the 560 GB Yambda-5B embedding benchmark. The post DLRMv4: The Next-Generation R
MLPerf Training v6.1 adds an agentic reinforcement-learning workload that measures how quickly systems can teach a 397-billion-parameter language model to repair real software projects. The post MLPer
In this analysis, MLPerf Inference Working Group chairs Miro Hodak and Frank Han break down a record field of 30 submitters and 120 systems, the arrival of agentic and end-to-end benchmarks, and what
Benchmarking Retrieval-Augmented Generation End to End — from Building the Vector Database to Serving an Iterative, Multi-Hop Question-Answering Inference Pipeline The post Introducing the MLPerf End-
New benchmark categories for generative AI and agentic workflows arrive alongside updated LLM tests, as the industry standard for measuring PC AI performance evolves. The post MLPerf Client v2.0 Expan
The intersection of medicine and AI has led to remarkable innovations. However, developers now face the thorny challenge of building robust medical AI tools that have been tested and evaluated on dive
arXiv:2602.02016v2 Announce Type: replace Abstract: Shampoo is one of the leading approximate second-order optimizers: a variant of it has won the MLCommons AlgoPerf competition, and it has been shown
In this post, you learn how to prompt Amazon Nova 2 Lite for content moderation using structured and free-form approaches, grounded in the MLCommons AILuminate Assessment Standard. The prompting techn
arXiv:2609.04007v1 Announce Type: new Abstract: Despite strong performance on held-out electroencephalography (EEG) data, seizure detectors may fail under real-world acquisition variability, artifacts
arXiv:2609.28758v1 Announce Type: cross Abstract: We describe our submission to the Unsupervised Speech in the Wild (UPS) Challenge at Interspeech 2026, a bidirectional Mamba-2 (BiMamba2) encoder trai
arXiv:2510.27065v2 Announce Type: replace Abstract: We present MLPerf Automotive, the first standardized public performance benchmark for evaluating Machine Learning systems that are deployed for AI a
arXiv:2604.18487v1 Announce Type: new Abstract: The Adversarial Humanities Benchmark (AHB) evaluates whether model safety refusals survive a shift away from familiar harmful prompt forms. Starting fro
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arXiv:2604.07345v1 Announce Type: cross Abstract: The rapid growth of generative artificial intelligence (AI) has introduced unprecedented computational demands, driving significant increases in the e