Research
Apart Research develops the evidence and evaluation methods needed to identify harmful manipulation in frontier AI systems. This work supports the EU AI Office as it prepares to enforce the EU AI Act. Our involvement spans research published in Science, the Code of Practice, the AI Act Advisory Forum, the Expert Forum on Frontier AI, a harmful-manipulation evaluation tender and much more.
NeurIPS 2025
A method for detecting hidden capabilities by testing how models respond to noise in their weights.
Read paper: Noise Injection Reveals Hidden Capabilities of Sandbagging Language Models (opens in new tab)ICLR 2025 · Oral
A benchmark of 660 prompts examining manipulation, sycophancy and other dark patterns in leading language models.
Read paper: DarkBench: Benchmarking Dark Patterns in Large Language Models (opens in new tab)Cooperative AI Foundation 2025 · Technical report
Maps the failure modes that emerge when many AI agents interact.
Read paper: Multi-Agent Risks from Advanced AI (opens in new tab)AI evaluations should provide meaningful risk information without imposing excessive burden.
Science 2026 · Policy Forum
A method for detecting hidden capabilities by testing how models respond to noise in their weights.
NeurIPS 2025
A benchmark of 660 prompts examining manipulation, sycophancy and other dark patterns in leading language models.
Benchmark site (opens in new tab)ICLR 2025 · Oral
Maps the failure modes that emerge when many AI agents interact.
Cooperative AI Foundation 2025 · Technical report
AAAI 2025
NeurIPS 2024 · Workshop
NeurIPS 2024 · Workshop
NeurIPS 2024 · Workshop
arXiv preprint 2024
ICML 2024 · Workshop
arXiv preprint 2024
Findings of ACL 2023