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…Wei Pang , Xiangru Jian , , , , , Zhengyuan Dong , , , , , , Abstract TRL-Bench establishes a standardized benchmark for evaluating tabular representation learning models across multiple granularities, revealing that encoder performance varies by task type and requires…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Skills, i.e., structured workflow instructions distilled for large language models (LLMs), are becoming an increasingly important mechanism for improving agent performance on real…
…The following papers were recommended by the Semantic Scholar API MemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models (2026) UniVLR: Unifying Text and Vision in Visual Latent Reasoning for…
…Powered by AMD Instinct™: Purpose-built for large model training, fine-tuning, and high-throughput inference on the latest AMD Instinct™ GPUs with up to 288GB of blazing-fast HBM3e memory. Bare…
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…
…Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring Published on May 1 Submitted by Indraneil Paul on May 4 Themis Authors: Indraneil Paul , , Abstract Researchers introduce Themis-RM, a…
…The following papers were recommended by the Semantic Scholar API Benchmarking Speech-to-Speech Translation Models (2026) SpeechEditBench: A Bilingual Multi-Attribute Benchmark for Instruction-Guided Speech Editing (2026) KVoiceBench, KOpenAudioBench, and…
…When paired with the Llama Nemotron Embed VL model, it further increases accuracy by reranking retrieved text chunks and images. Evaluate and optimize with NVIDIA NeMo Building production agents requires not only…
Papers arxiv:2605.04045 Audio-Visual Intelligence in Large Foundation Models Published on May 5 Submitted by Hao Fei on May 8 National University of Singapore Authors: , , Shengqiong Wu , , , , , , , , , Lei Cui , , , Hao…
…The following papers were recommended by the Semantic Scholar API Adaptive Cost-Efficient Evaluation for Reliable Patent Claim Validation (2026) MetaGAI: A Large-Scale and High-Quality Benchmark for Generative AI Model…
…Unlike prior work that focuses on limited datasets or settings, MMDG-Bench unifies evaluation across multiple tasks, modalities, and real-world challenges, including corruption robustness, missing modalities, and model trustworthiness. the take…