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 BlogPapers arxiv:2605.28591 Models That Know How Evaluations Are Designed Score Safer Published on May 27 Submitted by Haritz Puerto on May 28 COMPASS research group at ELLIS Institute Tübingen Authors: , Haritz Puerto , , Abstract Fine-tuning models on synthetic documents describing evaluation traits … …
… These limitations hinder reliable assessment of both image editing models and reward models. To address these challenges, we introduce Edit-Compass and EditReward-Compass, a unified evaluation suite for image editing and reward modeling. …
Papers arxiv:2606.00267 StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement Published on May 29 Submitted by taesiri on Jun 2 Authors: , , , , , , , , Abstract StressDream enhances video world models by steering diffusion-based imaginations toward high-impact yet p… …
Agentic AI / Generative AI Mastering Agentic Techniques: AI Agent Evaluation May 19, 2026 By Edward Li , Vanessa Bellotti , Nicola Sessions and Rebecca Kao Discuss 0 Discuss 0 L T F R E AI-Generated Summary Like Dislike Evaluating AI models focuses on assessing the foundation model's capabilities u… …
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…
Papers arxiv:2605.09269 DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification Published on May 10 Submitted by Rui Liu on May 12 Tencent Hunyuan Authors: Rui Liu , , , , , Runpeng Dai , , , Abstract DeltaRubric introduces a two-step multimodal preference evaluation … …
… To support reliable evaluation of next-generation frontier models , we introduce Soohak, a 439-problem benchmark newly authored from scratch by 64 mathematicians. …
Papers arxiv:2605.12034 Boosting Omni-Modal Language Models: Staged Post-Training with Visually Debiased Evaluation Published on May 13 Submitted by liu on May 15 StepFun Authors: , , , , , , Abstract Research demonstrates that current omni-modal benchmarks may inflate performance through visual sh… …
Papers arxiv:2605.15185 Quantitative Video World Model Evaluation for Geometric-Consistency Published on May 14 Submitted by taesiri on May 15 Authors: , , , , Abstract A quantitative framework called PDI-Bench is introduced for evaluating geometric coherence in generated videos through monocular r… …
Papers arxiv:2605.09591 From Pixels to Concepts: Do Segmentation Models Understand What They Segment? Published on May 10 Submitted by T. …
Papers arxiv:2605.10912 WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation Published on May 11 Submitted by Shuangrui Ding on May 15 Intern Large Models Authors: , Xuanlang Dai , , , , Yang JingYi , , , , , , , , , , , Yuhang Zang Abstract WildClawBench evaluates language and … …