Unconstrained CTI-REALM with Open-Weights
Organisations that want language-model agents for security work face a choice between open-weight models, which they can run and audit themselves, and far more expensive closed frontier models. We study that choice on CTI-REALM (arXiv:2603.13517), a benchmark in which an agent turns a threat-intelligence report into a working detection by querying a live telemetry store. We evaluate twenty-six models, nineteen open-weight and seven closed, under a common harness, scorer and panel of LLM judges. Our main finding is that an open-weight model can match the strongest closed models at less than a tenth of their cost, provided it is given enough turns. The benchmark's published limit on agent m...
| Authors | Sahil Garg, Ruhi Gottumukkala, Vishal Agarwal et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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