You Don't Need to Run Every Eval (Jun 2026)
Title: You Don't Need to Run Every Eval (Jun 2026)
Link: http://arxiv.org/abs/2606.24020v1
Date: June 2026
Summary:
This paper demonstrates that Large Language Model (LLM) benchmark scores are highly redundant and exist in an approximately rank-2 space. The authors introduce BENCHPRESS, a rank-2 matrix completion method in logit space that can predict missing benchmark scores from a small set of observed 'probe' benchmarks. The study identifies a subset of five benchmarks capable of recovering a model's full scorecard with high accuracy and characterizes the factors that affect prediction reliability, offering a way to significantly reduce the cost of LLM evaluation.
Key Topics:
LLM Evaluation
Matrix Completion
Low-rank Approximation
Benchmark Compression
Performance Prediction
BENCHPRESS
Chapters:
00:00 - Redundant Evaluation Problem
01:03 - Solving Data Engineering Hurdles
02:46 - Curation and Matrix Sparsity
04:39 - Proving Rank-2 Geometry
06:22 - Defining Two Latent Dimensions
08:21 - Designing Logit Space ALS
10:17 - ALS vs Frontier LLMs
11:16 - Identifying Optimal Probe Sets
12:53 - Analyzing High-Entropy Benchmarks
14:04 - Preserving Competitive Rankings
15:18 - Temporal Stability Stress Test
17:01 - Analyzing Prediction Reliability
19:52 - Hybrid Reliability Estimator
21:07 - Shifting Evaluation Economics
21:53 - Future of Monolithic Scaling
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