SciMIF C
AI benchmark
- Tier
- C why
- Snapshot
What is SciMIF?
SciMIF measures how well multimodal large language models follow complex, constraint-laden instructions on scientific tasks across chemistry, geography, biology, materials and physics.
Science mostly
“based on an extensive analysis of 22 distinct tasks across 5 representative scientific disciplines”
arxiv.org/abs/2608.25973v1 ↗“| Biology | Mol-Instructions, LAB-Bench |”
github.com/shenye7436/SciMIF ↗General capability
“SciMIF is a benchmark for evaluating whether Multimodal Large Language Models (MLLMs) can follow complex instructions in scientific domains.”
github.com/shenye7436/SciMIF ↗
Paper: arxiv.org/abs/2608.25973v1 ↗
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.
- 10 models scored
- in curated RL directories (1 pt)
How we got to tier C
- Adoption: 10 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 SciMIF 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.