arXiv:2609.36426v1 Announce Type: cross
Abstract: A detector pretrained on a broad corpus is fine-tuned on a narrow domain, its in-domain accuracy improves, and it ships. We ask what happens meanwhil...
By Trung Minh Bui, Jongsul Moon, YoungOuk Kim, Jung-Hoon Hwang, Dongin Shin
Threshold-Aware Conformal Routing (TACR) is a method that decides whether to use a fast surrogate model or a full simulator based on whether the surrogate’s conformal interval lies entirely on one side of a fixed decision threshold. TACR learns input‑dependent interval widths that are tighter near the threshold, preserving distribution‑free coverage while reducing the number of simulator calls. Experiments on scientific and engineering datasets show TACR cuts simulator deferrals by 14‑75% compared to standard conformal prediction, and by an additional 10‑24% over a variant without threshold‑aware weighting.
By Shiwei Tan, Huzefa Rangwala, Danielle C. Maddix
The study investigates how the composition of data during the mid‑training phase of language models affects performance across multiple domains. Experiments with Qwen3‑8B‑Base on five distinct KOR‑Bench domains show that moderate coverage (10%‑40%) yields the best per‑domain results, and that alignment passes cannot fully close the performance gaps created by mid‑training data choices. Additionally, zero coverage in mid‑training severely degrades accuracy, while a carefully tuned allocation can provide the largest overall pipeline improvement.
By Yunpeng Xu, Kun Zheng
The paper investigates whether deeper predictor blocks in adaptive‑compute world models truly improve rollout accuracy and whether a router can select the appropriate depth per step. Experiments on nine DeepMind Control tasks show that deeper predictors do not always yield better accuracy, and that a greedy oracle selecting depth per step can reduce error by up to 37% compared to fixed‑depth mixtures, yet no trained router achieves this advantage. The study also finds that supervising early exits only at the first rollout step can mitigate depth‑inversion issues observed in some tasks.
By Achyuthan Sivasankar
arXiv:2606. 29054v1 Announce Type: new Abstract: Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and standard heuristic abstention policies miss user-specified risk targets by 7.
By Varun Kotte
The paper explores a new lossy strategy for speculative decoding called verifier skipping, which allows certain draft prefixes to be committed without invoking the verifier. It investigates which confidence signals—raw token confidence, learned marginal survival, or learned conditional survival—best schedule these skips, finding that raw confidence often performs best despite higher positionwise AUROC for learned signals. Experiments on HumanEval with DiffuCoder-7B-Instruct and Qwen3-32B show that all three signals can reduce verifier calls by 9.6% to 13.5% while maintaining the same pass@1 as Strict SDD.
By Haoxuan Luo, Jameson Sandler, Ferdinando Fioretto