In the controlled 1.5B comparison, LoGo reports the best language-modeling loss and Lambada perplexity among the listed full-attention and matched-budget static hybrid variants, with a higher commonsense-reasoning average than the full Transformer.

In the controlled 1.5B comparison, LoGo reports the best language-modeling loss and Lambada perplexity among the listed full-attention and matched-budget static hybrid variants, with a higher commonsense-reasoning average than the full Transformer. · CiteArk

Measurement results

0/3 assessed

0 supported0 disputed or conflicting3 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Metric and conditions	Reported	Latest observation	Evidence judgment
model_variant: LoGo · reported_scope: single reported evaluation · parameter_count: 1.5B · attention_budget: 0.5 · context_extension: 32k and 128k stages · pretraining_tokens: 100B	2.112 loss	—	Not assessed
dataset: Lambada · model_variant: LoGo · parameter_count: 1.5B · attention_budget: 0.5	7.5 perplexity	—	Not assessed
model_variant: LoGo · reported_scope: single reported evaluation · parameter_count: 1.5B · attention_budget: 0.5	56.3 percentage_points	—	Not assessed

In the controlled 1.5B comparison, LoGo reports the best language-modeling loss and Lambada perplexity among the listed full-attention and matched-budget static hybrid variants, with a higher commonsense-reasoning average than the full Transformer.

Reported and observed measurements

Assessments (0)

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Measurement results