Drafting a new Magic: The Gathering expansion begins before any pick from it has been observed: the complete card list is public, but the draft logs that supervised pick models train on do not yet exist. We study this day-zero regime directly.
arXiv:2608. 19568v1 Announce Type: cross Abstract: Drafting a new Magic: The Gathering expansion begins before any pick from it has been observed: the complete card list is public, but the draft logs that supervised pick models train on do not yet exist.
arXiv:2606. 05104v1 Announce Type: new Abstract: Knowledge benchmarks for LLMs face three issues: scaling-driven designs that do not operationalize disciplinary representativeness; flat-payment annotation that permits lazy consensus; and unaudited ranking instability under bounded test budgets.
arXiv:2610. 28355v1 Announce Type: new Abstract: In speculative decoding, a lightweight draft model proposes tokens that the target model verifies in parallel, speeding up generation without changing the target distribution.
The paper demonstrates that the outcome of a forecasting leaderboard is largely determined by the evaluator’s design choices rather than the models themselves. By fixing the data, horizon, and period, the authors varied three key evaluation decisions—unit of analysis, error pooling, and scoring metric—and showed that each can reverse or eliminate the apparent superiority of any forecasting method. The study also evaluates the practical impact of these choices on a deployed system, revealing that the selection rule captures a significant portion of the potential performance gain, and confirms the findings on an external public dataset.
The paper introduces a denoised counterfactual protocol to accurately measure the selectable headroom of world action models (WAMs) and uses these measurements to build a fail‑gated selector. Across four WAMs and two benchmarks, the study shows that the standard estimate overstates selectable headroom, that structured action perturbations can add up to 16 points of headroom, and that imagined futures retain candidate‑outcome information without loss. The selector improves closed‑loop task success from 76.9 % to 81.6 % on held‑out training episodes and from 62.6 % to 69.8 % on unseen tasks.
The paper introduces the concepts of an information floor and a model gap to analyze block drafting in language models. By estimating these metrics across multiple domains and models, it finds that the all-parallel floor limits per-slot acceptance to 71% on Qwen3-4B, that a single realized token can eliminate most of this floor, and that current drafters still operate far above their floors, indicating significant room for improvement. These results highlight the distinct contributions of short-range conditioning versus proposal quality in block drafting.
In speculative decoding, a lightweight draft model proposes tokens that the target model verifies in parallel, speeding up generation without changing the target distribution. D-PACE (Wu et al., 2026)...
arXiv:2607. 27031v1 Announce Type: new Abstract: Reports on how sparsification, compression, and lottery tickets change model behavior have been mixed in the prior literature, with beneficial effects observed in some studies and adverse effects in others.
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:2610.15417v1 Announce Type: new Abstract: Mixture-of-experts (MoE) models activate only a few experts per token, but when their expert weights are offloaded to host memory, host-to-device (H2D)...
arXiv:2607. 17765v1 Announce Type: cross Abstract: We introduce WC2026-Agents, a benchmark and dataset for evaluating large language models (LLMs) as autonomous forecasting agents on real, future events.
The paper investigates speculative decoding, a technique that speeds up language model inference by generating draft tokens and verifying them. It shows that when verification is relaxed (lossy), the resulting completions become longer—especially due to extended reasoning traces—despite accepting more tokens per round. The study evaluates various relaxed verification rules across multiple model pairs, finding that higher acceptance rates often lead to longer outputs, lower target-model probability tokens, and reduced accuracy, indicating that token acceptance alone is an unreliable metric for decoding speed.