RepBench: Compiling Benchmarks into Capability Representations for Large Language Models
Quick summary
arXiv:2607.28008v2 Announce Type: replace-cross Abstract: Representation engineering reads and steers capability directions in large language models, yet methods are typically evaluated on paper-specific synthetic data. The resulting measurements are difficult to compare or reproduce and may reflect surface patterns rather than capabilities. We present RepBench, a benchmark-grounded data layer for capability-aligned representation probing. Crawling 13,427 benchmark papers yields a taxonomy of 182 capability clusters in 13 families; harvesting 353 public benchmark datasets yields 46,149 audited
Key takeaways
- arXiv:2607.28008v2 Announce Type: replace-cross Abstract: Representation engineering reads and steers capability directions in large language models, yet methods are typically evaluated on paper-specific synthetic data.
- The resulting measurements are difficult to compare or reproduce and may reflect surface patterns rather than capabilities.
- We present RepBench, a benchmark-grounded data layer for capability-aligned representation probing.
Why it matters
“RepBench: Compiling Benchmarks into Capability Representations for Large Language Models” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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