Block drafters generate multiple tokens in a single forward pass before earlier target tokens are produced, combining two loss components: missing within‑block path information and imperfect modeling of observable information. The study introduces an information floor—the minimum expected rejection for a given conditioning order—and defines the model gap as rejection above this floor. Across four domains and several models, the authors find that the all‑parallel floor limits per‑slot acceptance to 71% for Qwen3‑4B, that a single realized token can eliminate 86–100% of this floor, and that current drafters exhibit significant model gaps, accounting for 43–64% of DFlash rejection and 85–92% of DSpark’s oracle‑conditioned rejection.
arXiv:2608.30427v1 Announce Type: cross
Abstract: Speculative decoding speeds up generation with an efficient draft model (drafter) that proposes tokens for a target model to verify in one pass, pres...
By Ephrem Wu
arXiv:2607. 01893v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by drafting a block of tokens that the target model verifies left-to-right, committing only the longest accepted prefix.
By Tianjian Yang, Meng Li
Speculative decoding accelerates autoregressive generation by drafting a block of tokens that the target model verifies left-to-right, committing only the longest accepted prefix. Block (DLM-style) drafters predict the whole block in parallel, which is fast but trained with a full-block cross-entropy that supervises every position against the gold continuation -- even though inference discards every token after the first rejection.
arXiv:2608. 10145v1 Announce Type: new Abstract: LeWorldModel trains a latent world model with a prediction loss and a single anti-collapse regulariser, and reports approximately 87% of goals reached on TwoRoom, its simplest diagnostic environment.
By Joyjeet Singh
The paper investigates the safety of Code World Models, where a language model generates executable world models that a planner uses. It shows that accepting a model based on sampled transitions only guarantees sample consistency, not full safety, because the probability of missing critical events decays as (1‑r)^N. Experiments on hybrid instruments reveal that omitted mode‑boundaries can severely limit planner performance, and that even sophisticated LLMs (GPT‑5.x) struggle to repair such omissions in higher‑dimensional settings.
By Javier Aguilar Mart\'in