Towards Understanding and Measuring COGNITIVE ATROPHY in LLM Behaviour
Recent incidents involving LLMs used for mental-health support reveal a critical evaluation gap: surface-level safety scores do not capture how models behave across realistic, emotionally sensitive interactions over time. Existing benchmarks measure knowledge, safety, or static response quality, but miss whether LLM interactions help users keep reflecting, coping, and making decisions themselves. We formalize this missing dimension as COGNITIVE ATROPHY, a process-level behavioural measure in AI-mediated mental-health support distinct from safety and helpfulness. To measure it, we introduce COGNITIVE ATROPHY BENCH, a clinically grounded benchmark built from 1,576 fully human-generated coun...
| Authors | Abeer Badawi, Moyosoreoluwa Olatosi, Negin Baghbanzadeh et al. |
|---|---|
| Primary category | cs.HC |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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