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ARXIV:2602.15532 · LLM EVALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.15532LLM EVALUATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Develop a model for improving the measurement of construct validity in LLM benchmarks using structured capabilities.
Opportunity summary
Pain Develop a model for improving the measurement of construct validity in LLM benchmarks using structured capabilities.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Develop a model for improving the measurement of construct validity in LLM benchmarks using structured capabilities. However, benchmarks can have problems that distort performance, like test set contamination and annotator error.
The LLM community often reports benchmark results as if they are synonymous with general model capabilities. However, benchmarks can have problems that distort performance, like test set contamination and annotator error.
ScienceToStartup currently rates this 4.0/10 on the public viability pass. The LLM community often reports benchmark results as if they are synonymous with general model capabilities.
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10.
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Develop a model for improving the measurement of construct validity in LLM benchmarks using structured capabilities.
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Paper Pack
10.48550/arXiv.2602.15532Develop a model for improving the measurement of construct validity in LLM benchmarks using structured capabilities.
Abstract
The LLM community often reports benchmark results as if they are synonymous with general model capabilities. However, benchmarks can have problems that distort performance, like test set contamination and annotator error. How can we know that a benchmark is a reliable indicator of some capability that we want to measure? This question concerns the construct validity of LLM benchmarks, and it requires separating benchmark results from capabilities when we model and predict LLM performance. Both social scientists and computer scientists propose formal models - latent factor models and scaling laws - for identifying the capabilities underlying benchmark scores. However, neither technique is satisfactory for construct validity. Latent factor models ignore scaling laws, and as a result, the capabilities they extract often proxy model size. Scaling laws ignore measurement error, and as a result, the capabilities they extract are both uninterpretable and overfit to the observed benchmarks. This thesis presents the structured capabilities model, the first model to extract interpretable and generalisable capabilities from a large collection of LLM benchmark results. I fit this model and its two alternatives on a large sample of results from the OpenLLM Leaderboard. Structured capabilities outperform latent factor models on parsimonious fit indices, and exhibit better out-of-distribution benchmark prediction than scaling laws. These improvements are possible because neither existing approach separates model scale from capabilities in the appropriate way. Model scale should inform capabilities, as in scaling laws, and these capabilities should inform observed results up to measurement error, as in latent factor models. In combining these two insights, structured capabilities demonstrate better explanatory and predictive power for quantifying construct validity in LLM evaluations.
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Dimensions overall score 4.0
PROBLEM
Develop a model for improving the measurement of construct validity in LLM benchmarks using structured capabilities. However, benchmarks can have problems that distort performance, like test set contamination and annotator error.
METHOD
The LLM community often reports benchmark results as if they are synonymous with general model capabilities. However, benchmarks can have problems that distort performance, like test set contamination and annotator error.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. The LLM community often reports benchmark results as if they are synonymous with general model capabilities.
WHY NOW
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Develop a model for improving the measurement of construct validity in LLM benchmarks using structured capabilities. However, benchmarks can have problems that distort performance, like test set contamination and annotator error.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The LLM community often reports benchmark results as if they are synonymous with general model capabilities. However, benchmarks can have problems that distort performance, like test set contamination and annotator error.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. The LLM community often reports benchmark results as if they are synonymous with general model capabilities.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop a model for improving the measurement of construct validity in LLM benchmarks using structured capabilities.
Segment
LLM Evaluation
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Commercial read
4.0/10 public viability
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reason
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proof status
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next verification path
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Technical feasibility
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Gaps
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Write integration checklist from prototype path and target workflow.
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DEFENSIBILITY
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