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…Social deduction games have become a popular testbed for evaluating reasoning, deception, coordination, and belief modeling in large language models. However, most environments evaluate agents primarily through game outcomes like win rates…
…The following papers were recommended by the Semantic Scholar API Beyond Rating: A Comprehensive Evaluation and Benchmark for AI Reviews (2026) Teaching Language Models to Check Grounded Claim Factuality with Human Test…
…Can Video Generation Models Dream Executable Robot Manipulation? Published on Jun 4 Submitted by Rui Zhao on Jun 5 Authors: , , , , , , , , Abstract Video generation models were evaluated through robotic manipulation tasks to assess…
…narratives, necessitating benchmarks that evaluate psychological trajectory alignment rather than static factual recall, with ArcANE demonstrating superior performance when character arc information is conditioned into models. Generated by Qwen/Qwen2.5-Coder…
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 As video diffusion models (VDMs) advance toward world models , a key question arises: do they truly understand causality , or merely overfit to statistical temporal…
…principled evaluation. We systematically review text world models for LLM-based agents , organized around a formal framework and the agent lifecycle: (1) Foundations, defining text world models and characterizing them by state…
…on large generative models. Choose fully dedicated nodes or flexible VMs. You control the OS, networking, and stack, while Hot Aisle and AMD provide the performance foundation. Evaluate AMD Instinct GPUs on…
…saving should be achieved by exporting the model to deployment frameworks such as NVIDIA TensorRT . This simulation is crucial because it enables you to evaluate the model’s accuracy before committing to…
…framework that improves image edit evaluation by iteratively developing tools and skills from limited human demonstrations, achieving superior performance compared to existing models. AI-generated summary Evaluating instruction-guided image edits requires…
…This remains the most persistent weakness across all 17 evaluated models (including frontier models from Google, OpenAI, Anthropic, and the Qwen family). ⚖️ New Evaluation Paradigm: We decouple raw correctness from strict safety…