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DepthBench examines computational depth in Transformers
A September 29, 2026 preview presents a benchmark for comparing information-flow methods and assessing whether deeper Transformers use their layers effectively.
Published on September 29, 2026, DepthBench proposes a benchmark to examine whether methods such as LayerNorm Scaling, mHC, and AttnRes help deeper Transformers use their layers effectively. The preview notes that it remains unclear whether these approaches increase effective computational depth.
The work aims to compare approaches to information flow and depth utilization in Transformers. To assess its conclusions, consult the original publication and check how the benchmark defines effective computational depth, which methods it compares, and what results it reports; the preview provides no experimental findings.
Original source: DepthBench avalia conexões residuais e profundidade computacional