Nvidia study finds AI agents keep accuracy with short contexts

A new paper suggests agents that compress their history into handoff notes can match full-history performance at a fraction of the context The AI industry has spent a lot of energy building models that can hold more and more text in memory at once. A new Nvidia paper suggests agents may not need most of it. The research, titled “You don’t need long context,” found that AI agents can preserve accuracy on complex tasks by regularly compacting their conversation history into short handoff notes. Researchers were able to shrink the working context to as little as 16,000 tokens while still matching full-history performance. What the researchers actually tested The paper is authored by Peter Belcak, Pavlo Molchanov, and nine co-authors. Its focus is frontier agents, meaning AI systems built to work through long, multi-step tasks rather than answer a single question. Instead of dragging the entire conversation forward at every step, the agent periodically condenses what has happened into a handoff note. The next step starts fresh with only that summary. The team ran the approach on two models: Anthropic’s Claude Opus 4.5 and OpenAI’s GPT 5.6 Sol. Testing covered thirteen benchmarks. Six o...