arXiv:2608.23744v1 Announce Type: new Abstract: Split conformal prediction, not the pruning rule, supplies finite-sample marginal coverage once a pruned model is fixed independently of the conformal...
arXiv:2609.30465v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) models activate few experts per token but store the full expert pool. Expert pruning reduces this storage burden; at a fixed...
arXiv:2608. 01378v1 Announce Type: new Abstract: Design campaigns in chemistry, materials science, and machine learning share a bottleneck: determining how good a candidate truly is requires an expensive evaluation - an experiment, a first-principles simulation, or a full training run.
arXiv:2607. 01444v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models offer inference speedups via selective activation but impose substantial memory requirements because the whole network must remain loaded.
arXiv:2607. 25136v1 Announce Type: new Abstract: Research on preference optimization often varies the training objective while holding the data fixed.
arXiv:2608. 02940v1 Announce Type: new Abstract: A reproducible compression statistic can still select the wrong candidate.
arXiv:2606. 03328v3 Announce Type: replace Abstract: Calibration data are often treated as a minor implementation detail in post-training LLM pruning because averaged evaluations suggest only modest effects.
PAC-CF introduces a method called Probably Approximately Correct Conformal Filtering to calibrate irreversible frontier pruning in large language model (LLM)-guided search. The approach treats tree pruning as a PAC‑guaranteed decision problem, deriving a conformal margin from score deficits on held‑out Native traces and applying a direct score‑gap filtering rule during deployment. Experiments across multiple domains and controllers show that PAC‑CF improves utility at various budgets and reduces workload metrics, notably boosting equal‑domain utility by 4.38 points and cutting physical requests by 18.95% with a 23.76% token reduction on a pruning‑aware ToolTree under a 100‑request budget.
arXiv:2610.14996v1 Announce Type: new Abstract: Choosing a few robust algorithms or schedules can require evaluating every candidate under many adverse scenarios, even though an agent only needs to i...
The paper introduces a structured post‑training pruning method for vision and language transformers called Damage‑Aware Bandit Pruning. It treats the selection of functional units (attention heads and MLP channel groups) as a multi‑armed bandit problem, using paired damage (masked loss minus base loss) as a reward to guide either UCB or Thompson Sampling policies. Experiments on a range of models (GPT‑2, OPT, Pythia, Qwen2.5, SmolLM2, ViT‑B/16, DeiT‑Tiny, Swin‑Tiny) show that the bandit approaches generally reduce degradation compared to budgeted‑greedy baselines, with statistically significant improvements in most comparisons.
arXiv:2609.18239v1 Announce Type: new Abstract: Structured pruning is commonly formulated as ranking individual channels, although channel responses can be complementary or cancel through downstream...
SimplexUQ introduces the first benchmark and reproducible protocol for evaluating how conformal prediction wrappers allocate coverage across simplex‑valued predictions. The framework, called SimplexTasks‑12, combines six synthetic regimes and six real tasks (e.g., class probabilities, topic mixtures, spectral abundances) to compare existing wrappers on metrics such as marginal coverage, worst‑stratum coverage, max disparity, and computational cost. Empirical results show that no single wrapper consistently dominates, with Mondrian and BatchMVP performing best in different settings, and that removing predictor bias only partially mitigates disparity.