New paper: AI agents that matter
normaltech.ai · 1,456 words · saved by 1 readers
Rethinking AI agent benchmarking and evaluation
Some of the most exciting applications of large language models involve taking real-world action, such as booking flight tickets or finding and fixing software bugs. AI systems that carry out such tasks are called agents. They use LLMs in combination with other software to use tools such as web search and code terminals. The North Star of this field is to build assistants like Siri or Alexa and get them to actually work — handle complex tasks, accurately interpret users’ requests, and perform reliably. But this is far from a reality, and even the research direction is fairly new. To…
related reading
- Demystifying evals for AI agents \ Anthropicanthropic.com
- Agent Evaluation: A Detailed Guidecameronrwolfe.substack.com
- Building Effective AI Agents \ Anthropicanthropic.com
- Building Effective AI Agents \ Anthropicanthropic.com
- f316275b44ee2de533102913828a8107-Paper-Datasets_and_Benchmarks_Track.pdfproceedings.neurips.cc
- New Paper: Towards a science of AI agent reliabilitynormaltech.ai
- What We’ve Learned From A Year of Building with LLMs – Applied LLMsapplied-llms.org
- Critique of Agent Modelarxiv.org
- [2603.08640] PostTrainBench: Can LLM Agents Automate LLM Post-Training?arxiv.org
- Thoughts on AI in academiatheinfinitesimal.substack.com
- Agentshuyenchip.com
- Building reliable AI agents · parth sareenparthsareen.com