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…Models Published on Apr 23 Submitted by Yanpeng Zhao on Apr 30 Authors: , Yanpeng Zhao , , , Abstract Visual planning is reimagined as a single-step image transformation task using abstract puzzles for evaluation…
…We’ve been pleased to see Petri being used by external organizations: for example, the UK’s AI Security Institute (AISI) made it a major part of how they evaluate models for…
Agentic AI / Generative AI Evaluate Clinical ASR Models Faster with Agent Skills and NVIDIA Nemotron Speech Jun 09, 2026 By John Jahanipour , Ben Randoing , Abood Quraini and Jonny Hancox Discuss (0) Discuss…
…When it comes to newer models with agentic tools, though, Anthropic found that RLHF post-training did little to improve performance on misalignment evaluations that measure how “HHH” a model is in…
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…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Human annotation is the empirical foundation of much NLP research , from dataset construction to model evaluation, but papers often leave unclear who produced the…
…A production-aligned GPU kernel generation benchmark that doubles as a minimal inference framework, with compositional tasks from primitives to full models, deployable module interfaces, captured production tensors, and evaluation against real…
…Take advantage of the Alpamayo models as a teacher model to fine-tune and distill into smaller, run-time capable models. Evaluate in a High-Fidelity Closed-Loop: Deploy the model directly…
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