AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

Claire Radar

PoolBench

PoolBench isolates pooling strategies as the experimental variable in concept representation evaluation for decoder-only LLMs. It covers 17 concepts, 19 pooling strategies, and 3 open-weight models (Llama-3.1-8B, Gemma-2-9B, Mistral-7B) on a corpus of 37,693 real-text passages, with primary axis linear separability (AUROC) and diagnostic axes for steering and disentanglement.

Evidence available for this benchmark

Numeric results have not been collected for this source record. Use the paper, code or dataset below to check the task and experiments; this does not imply that the original work contains no results.

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Cited documents and dates (1)

Catalog collected on 2026-10-14; resources and fields reflect that saved source.

Evaluation context and evidence

Source
Claire Radar
Publisher
Not recorded
Benchmark release
2026-05-26
Collected
2026-10-14
Modality
Not recorded

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Reported results 0

This source record has no numeric results in our collection. That does not mean a score of zero or that no public results exist. Consult the original resources above.