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…also “reduced inaccurate claims by 37.3% on especially challenging conversations users had flagged for factual errors.” (OpenAI has some information about how it evaluated the model in its GPT-5.5…
…When we evaluate “an agent,” we’re evaluating the harness and the model working together. For example, Claude Code is a flexible agent harness, and we used its core primitives through the…
…Bot for paper recommendations by tagging it in a comment: @ librarian-bot recommend No model linking this paper Cite arxiv.org/abs/2605.15188 in a model README.md to link it…
…Further ablation studies show that evaluation skills are critical for handling complex evaluation: removing them causes Eval@1 to drop significantly from 65% to 30%. No model linking this paper Cite arxiv…
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…
…Evaluating five state-of-the-art models, we find that the best model reaches only 48.45% average accuracy, performance drops nearly 47 points from early to late turns, and long-horizon…
…Published on May 30 Submitted by LeyiWu on Jun 2 RoboStressBench Team Authors: Leyi Wu , , , , , , , , , , , , , Abstract RoboStressBench presents a principled benchmark for evaluating vision-language model robustness to physical visual stress in…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Low-Rank Adaptation (LoRA) successfully enables personalization in text-to-image generation by adapting pre-trained diffusion models to specific visual concepts and styles…
…The framework makes coordination decisions observable and learnable from repeated trajectories, rather than treating skill, role, model, topology , and evaluation choices as fixed pipeline design. AgensFlow is evaluated on two corpora: distributed…
…frequency of a held-out reactor crystal governed by the same mechanism. The hidden data-generating process is a randomly sampled structural causal model (SCM), so success requires recovering both a causal…
…Multi-entity & Evolving Memory Evaluation Published on May 12 Submitted by Arnas Uselis on May 13 KAIST AI Authors: , , Arnas Uselis , , Abstract MEME benchmark evaluates memory systems across multiple entities and evolving…