279 summarised stories about AI Benchmarking, each linking back to the original source. Browse all topics →
Product Hunt 1 week ago 30
Museum of Models launched a web page that records every new AI model since May 2024 using the same set of questions. The site includes 15,639 answers kept as the models produced them, with no ranking. As a result, users can browse and visually explore model responses for evaluation rather than rely on ordered performance lists.
MarkTechPost 1 week ago 39 ● 8 sources
Liquid AI released Open d1, two open-weight multimodal decision models (d1-3B and d1-omni-600M) that return calibrated typed answers with zero output tokens. d1-3B scored 48.57 on Decision Index v0.2.1 and takes 8 ms for one question on an RTX 4090. The models shift deployments from generating token-by-token text to making real-time probabilistic decisions across fixed options, including text+image and text+audio inputs.
Wiley Science and Engineering Content Hub 1 week ago 29
This white paper introduces HiPHI, a large-scale motion capture dataset and shows that reinforcement learning policies trained on it transfer to a real humanoid robot.
Hugging Face 1 week ago 39
Nemotron Labs fine-tuned Nemotron models for IOI 2026 and IMO 2026 using supervised fine-tuning, reinforcement learning, and a generate-evaluate-refine inference loop, producing gold-level performance. In IOI 2026, Nemotron-3-Ultra-CC scored 535.4 out of 600, above the 361.12 gold threshold. The work is packaged for reuse on Hugging Face with released checkpoints, training datasets, and an IMO benchmark, changing focus from one-off results to a repeatable specialization recipe.
MarkTechPost 1 week ago 45
Laya, Convai Innovations’ open-source non-autoregressive decision engine, is evaluated in a tutorial by running typed multiple-choice/score/yes-no queries and checking zero-shot behavior on labelled CLINC150 banking data. The guide specifies a 421-million-parameter encoder that scores all options in a single forward pass with zero output tokens. It reports how option wording/order, probability calibration via temperature, and an abstention gate affect accuracy and the trustworthiness of the returned probabilities for a production-style router.
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14 October 2026
Sakana AI added a new transparency layer to Sakana Chat with “My Fish,” letting users see what the chatbot remembers, when those memories get used, and how related facts connect. The interface surfaces memory activity as small “chips” during responses and backs it up with a heatmap of memory use plus read/write history and a connection map. It’s a practical answer to a familiar problem: people don’t just want outputs from AI systems, they want a way to audit what the system is carrying forward.
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