ARC-AGI-2
Harbor nativeARC-AGI-2: 更难的抽象推理
Verified100%Abstract Reasoning
- Samples
- 167
- Released
- 2025
- Scores
- 118
Mental Health Stigma Benchmark
Automatic Evaluation of Mental Health Stigma in Online Communication
A theory-grounded benchmark for evaluating mental health stigma in online news and social media text across six conditions using a fine-grained taxonomy.
How to run
ltb · mental-health-stigma-benchmark
$ npm i -g lettokenburn-cli$ ltb bench info mental-health-stigma-benchmark
External harness. No Harbor adapter has been generated for this benchmark yet. `ltb bench info` prints the known links.
Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs
Scores
4 sourced entries| Harness | Source | Confidence | Link | |||
|---|---|---|---|---|---|---|
| Gemini 3 Flash Preview | 0.7 | N/A | Independent | medium | Oct 2, 2026 | arxiv.org |
| Claude Sonnet 4.6 | 0.6 | N/A | Independent | medium | Oct 2, 2026 | arxiv.org |
| DeepSeek V4 Flash | 0.6 | N/A | Independent | medium | Oct 2, 2026 | arxiv.org |
| GPT-5.5 | 0.5 | N/A | Independent | medium | Oct 2, 2026 | arxiv.org |
Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.
Reproduced by the community
All runsLoading community runs…
About
The benchmark consists of naturally occurring online news and Reddit posts annotated with a multi-level stigma taxonomy (mode, domain, components) across six mental health conditions. It evaluates LLMs and classifiers, finding that standard toxicity/sentiment models fail to capture stigma nuances and LLMs often overpredict without explicit rules.