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…The following papers were recommended by the Semantic Scholar API HealthAdminBench: Evaluating Computer-Use Agents on Healthcare Administration Tasks (2026) Medical Reasoning with Large Language Models: A Survey and MR-Bench (2026…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Recent multimodal large language models have demonstrated strong reasoning ability, yet their reliability as automated evaluators remains limited by a critical weakness: when visual…
…AI-generated summary Crowdsourced pairwise evaluation has emerged as a scalable approach for assessing foundation models. However, applying it to Text to Speech (TTS) introduces high variance due to linguistic diversity and…
…May 7 Submitted by Yuwei Yin on May 11 University of British Columbia Authors: Yuwei Yin , Chuyuan Li , Abstract IntentGrasp is a benchmark for evaluating large language models' intent understanding capability, demonstrating…
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…
…Across the eight model backends, Lucky rates range from 0.5% to 23.2%, and some models move by as many as five rank positions when ranked by quality score instead of…
…We evaluate 10 frontier LLMs on Ψ-Bench and find that while most models can produce coherent and reasonable arguments, even state-of-the-art models still leave considerable room for improvement…
…to evaluate conversational agent reliability, revealing significant performance gaps among leading models. Generated by Qwen/Qwen2.5-Coder-32B-Instruct As recommender systems transition toward agentic, multi-turn conversational interfaces , evaluation paradigms…
…The following papers were recommended by the Semantic Scholar API GEditBench v2: A Human-Aligned Benchmark for General Image Editing (2026) Can Nano Banana 2 Replace Traditional Image Restoration Models? An Evaluation…
…The following papers were recommended by the Semantic Scholar API WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models (2026) WebGameBench: Requirement-to-Application Evaluation for Coding Agents via Browser-Native…
…Powered by AMD Instinct™: Purpose-built for large model training, fine-tuning, and high-throughput inference on AMD Instinct™ GPUs with up to 192GB of HBM3 memory. Scale without borders: Scale instantly…