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autoregressive-decoding

topic2 events
papersTODAY 04:00 UTC

arXiv Paper Surveys Diffusion Language Models for Code Generation

A new arXiv preprint reviews how diffusion-based large language models can be applied to code generation, an area currently dominated by left-to-right autoregressive decoding. The authors examine the limitations of standard autoregressive generation and assess whether diffusion approaches offer advantages for producing source code. The work is a replacement submission (v3) to the cs.AI category.

code-generation/diffusion-models/llm/research
papersTODAY 04:00 UTC

arXiv Paper Proposes Self-Orchestrating LLMs to Cut Inference Latency

A new arXiv preprint introduces a method for having language models coordinate their own computation by exploiting semantic dependencies between generated tokens. The authors argue that standard autoregressive decoding is slow and leaves GPUs underused when batch sizes are small, and that their approach improves inference efficiency. The work is currently a research preprint and has not been peer reviewed or released as a product.

research/inference/efficiency/llm