IABench-CA C
AI benchmark
- Tier
- C why
- Snapshot
What is IABench-CA?
IABench-CA measures how deployment rules for allocating consequences causally shape collective safety behavior (e.g., fatality, targeting of agents) in multi-agent LLM populations playing consequence-allocation games.
General capability mostly
“Institutional Red-Teaming: Deployment Rules, Not Just Models, Causally Shape Multi-Agent AI Safety”
arxiv.org/abs/2607.07695v1 ↗“a consequence-allocation benchmark spanning 228 contexts, five canonical rules, and seven model populations (33,924 games)”
arxiv.org/abs/2607.07695v1 ↗
Paper: arxiv.org/abs/2607.07695v1 ↗
Tier C
Niche: a handful of models scored, or built by an RL vendor.
How much stands behind a tier: usage, who backs it, a confirmed line from its own site or paper, and a clear read on X. High with three of the four, medium with two, low otherwise.
- 7 models scored
- in curated RL directories (1 pt)
How we got to tier C
- Adoption: 7 models scored. Tier C.
- Not yet read on X.
- In curated RL directories: 1 point, a floor of none.
- Confirmed: a line from its own site or paper places it.
A clear view on X moves a tier one step up or down; most entries don't move. How tiers work.
Where IABench-CA sits
Its domain on the map, live. Hover a logo for its name; click to open it. Open it on the full map
Next: browse all benchmarks. Entry from the RL Research daily scrape of public sources, 2026-10-19 snapshot.