Multimodal-STEM-HLE-plus-plus: A STEM Benchmark for Frontier LLMs

TuringEnterprises created a multimodal STEM dataset designed to push the limits of state-of-the-art large language models. The dataset was last updated on May 13, 2026. Its design is empirically proven to address the bottleneck of finding data at the right difficulty level for current frontier models.

Use Cases

• Benchmarking frontier LLM performance based on multimodal STEM content
• Training models via reinforcement learning based on high-difficulty STEM problems
• Evaluating model saturation on tasks beyond MMLU and HLE benchmarks

Strengths

• Designed specifically for high-difficulty tasks for SOTA models
• Empirically proven to push model limits according to the description
• Targets a known bottleneck in frontier model improvement via RL

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download
• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment

Provenance

Source

TuringEnterprises

Freshness

Last updated 2026-05-13 21:46:48; freshness should be verified