A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs
Autoregressive (AR) language models build representations incrementally via left-to-right prediction, while diffusion language models (dLLMs) are trained through full-sequence denoising. Although recent dLLMs match AR performance, whether diffusion objectives fundamentally reshape internal representations remains unclear. We perform the first layer- and token-wise representational analysis comparing native dLLMs (LLaDA), native AR models (Qwen2.5), and AR-initialized dLLMs (Dream-7B), using cosine similarity across layers and tokens alongside static inference-time layer-skipping as an analytical probe of redundancy. We find that diffusion objectives produce more global representations wit...
| Authors | Raghavv Goel, Risheek Garrepalli, Sudhanshu Agrawal et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 March 2026 |
| Last updated at source | 2 August 2026 |
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