While model and agent evaluation are inextricably linked, their technical benchmarks and metrics for success are fundamentally different.
Mastering Agentic Techniques: AI Agent Evaluation | NVIDIA Technical Blog…Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks? Published on Jun 3 Submitted by taesiri on Jun 4 Authors: , , , Dongfu Jiang , , Hang Hua , , , , Lichi Li , , , , , , , , , Abstract AutoLab benchmark evaluates…
…The criteria explicitly penalized highly generic “AI slop” patterns, and by weighting design and originality more heavily it pushed the model toward more aesthetic risk-taking. I calibrated the evaluator using few…
Papers arxiv:2605.26396 Advancing Creative Physical Intelligence in Large Multimodal Models Published on May 25 Submitted by Cheng Qian on May 28 University of Illinois at Urbana-Champaign Authors: , , , , , , , , , , , , Abstract Large…
…by taesiri on May 5 Authors: Junjie Yu , , , , , , , , , , , , , , , , , , , , , Abstract AcademiClaw presents a comprehensive benchmark for evaluating AI agents on complex academic tasks spanning multiple domains, revealing significant capability gaps in current models…
Two years ago we released VQAScore: ask a VLM "does this image show {prompt}?" and use P(Yes) as the score. It became a go-to evaluation metric and reward model for image generation, replacing CLIPScore across the field …
Current LLM benchmarks are broken. We think long horizon "world" building could be an interesting additional way to evaluate LLMs, since it combines many aspects such as need for advanced reasoning, tool calling, working…
I built AptSelect to stop writing throwaway scripts every time I needed to test how different LLMs handle specific instructions and prompt edge cases.What it does:Parallel Execution: Send a single prompt to OpenAI, Anthr…
I'm excited to announce Elmo, an MIT-licensed, open source AEO/GEO tool.We help you scrape ChatGPT/Google AI Mode/etc using web scrapers like BrightData/Olostep/etc, evaluate prompts against the OpenAI/Anthropic/Mistral …
TLDR: Forked pytorch and triton internals . Changed attention so its linear first layer , middle quadratic layer, last linear layer Inference got much faster with a low perplexity hit in tests .Full attention O(n²): 17.9…
…on May 2 Submitted by 张道瑄 on May 6 Authors: Daoxuan Zhang , , , Abstract Embodied Search and Rescue task and benchmark are introduced to evaluate multimodal large language model-driven UAV agents in…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Reference-free faithfulness metrics verify each atomic claim a model makes against ground truth, and are increasingly used to evaluate grounded generation . We show…
…comparison between top-tier models. The result is a more reliable quality signal for both rapid iteration and rigorous release decisions backed by full human evaluation. HUE evaluates video generation quality using…
…Submitted by Yifei Li on Jun 4 Intern Large Models Authors: , , Yuhang Zang , , , , Abstract OVO-S-Bench presents a comprehensive benchmark for evaluating streaming spatial intelligence in multimodal language models through human…
…This allows developers to use the knowledge that has already been learned by a pretrained model. Improving performance and accuracy of the model. Data scientists and developers can create a model that…
…AI-generated summary LLM inference is still evaluated mainly as a model or software problem: accuracy, latency, throughput, and hardware utilization. This is incomplete. At deployment scale, the relevant output is a…