10 articles tagged with “benchmarks”.
An AWS post introduces Self-Distilled Reasoning (SDR), an approach for adding reasoning traces to supervised fine-tuning datasets that lack them, validated across three benchmarks with Amazon Nova.
OpenAI CFO Sarah Friar presents a practical scorecard for evaluating AI ROI, based on useful work, cost per successful task, dependability, and return on compute.
A survey of 101 enterprises finds AI agents often produce confident but wrong answers traced to missing or inconsistent business context, exposing a trust gap in enterprise RAG infrastructure.
NVIDIA's Nemotron 3 Embed model has taken the top overall spot on the RTEB benchmark, positioned around advancing agentic retrieval.
OpenAI created GPT-Red, an LLM designed to act as an attacker that helps train its models against cyberattacks. The company says GPT-5.6 is its most robust release yet after such training.
AWS details how Thrad.ai deployed a multi-agent system using Strands Agents and Amazon Bedrock AgentCore to automate prospect discovery and personalized email generation, with benchmarks on two orchestration patterns.
AWS has launched a UI in Amazon SageMaker AI Studio that guides users to optimized generative AI inference configurations without deep infrastructure expertise.
Letting a model 'think' before answering measurably improves hard reasoning. Here is how chain-of-thought works, how it grew into dedicated reasoning models, and when the extra cost pays off.
Every word an LLM generates has a cost in compute, memory, and time. Here is what actually happens during inference — and why it explains latency, throughput, and per-token pricing.
A demo that works on five hand-picked prompts is not a working AI product. Here is how to measure LLM quality honestly — the metrics, the judge models, and the datasets — so you catch failures before your users do.