The paper introduces Drift‑Aware Sparse Routing (DRS), a method for routing requests in a multi‑model language service while respecting compute, latency, memory, or cost budgets. DRS estimates reward and resource use from a rolling audit window, routes using pessimistic reward and optimistic cost estimates, updates resource shadow prices online, and applies a hard meter before commitment. The authors provide theoretical regret bounds that separate control from statistics, showing how the method adapts to non‑stationary prompt distributions and model changes.
By Cheung Hao Lee, Patrick Wong
The paper compares the sustainability of native small language models (SLMs) versus large language models (LLMs) compressed via post‑training quantization for edge AI deployment. Using a Holistic Sustainability Score (HSS) that balances capability, efficiency, and safety, the study evaluates 30 configurations across five benchmarks, latency, VRAM, energy, and harmful‑prompt robustness. Results show that optimized quantized LLMs can outperform SLMs overall, while SLMs remain competitive due to lower resource demands, challenging the assumption that native SLMs are always the most sustainable choice.
By Jainil Dharmil Shah
The paper introduces the Prospective Intention Store (PIS), a method that places lifecycle logic in code and confines language tasks to a typed action space, enabling small models to perform prospective memory tasks more effectively. Using PIS, a small model (DeepSeek-Chat) achieves 82.9% Set‑F1 on PM‑Bench, surpassing the previous best of 65.1%. On Gemma‑E2B, PIS boosts Set‑F1 from 4.2% (without a store) to 66.2%, and reaches 70.1% Set‑F1, outperforming retrospective memory approaches that max out at 54.4%.
By Jinqing Zhao, Chengcan Wu
The paper introduces a stage‑wise closed‑loop evaluation method to track how compression techniques affect learned driving policies. Using a POMDP formulation and PPO‑trained belief‑state policy in Gym‑Duckietown, the authors compress the actor step by step and test it across five driving curricula. They find that structured pruning first degrades driving capability, distillation can partially recover it but is limited by rehearsal data, and integer quantization further loses curricula requiring stopping and resuming, whereas the unpruned actor retains all curricula.
By Ahmad Alfan Alfian Irfan, Nur Ahmad Khatim, Mansur Arief
The paper introduces STORM, a one‑stage generative AI framework that reformulates Earth system data assimilation as diffusion‑based Bayesian posterior sampling, replacing costly PDE ensemble forecasts with scalable AI inference. STORM employs a spatiotemporal transformer with a global‑attention algorithm that reduces computational complexity from quadratic to linear, enabling high‑resolution, long‑context modeling. The system scales to 74,400 GPUs on Frontier, achieving 96–99 % strong‑scaling efficiency and up to 6 ExaFLOPs sustained BF16 throughput, while supporting 32,768‑member ensembles for uncertainty quantification in just 34 seconds on 4,096 GPUs, and demonstrates improved hurricane tracking and climate reanalysis accuracy.
By Xiao Wang, Zezhong Zhang, Isaac Lyngaas, Hong-Jun Yoon, Jong-Youl Choi, Siming Liang, Janet Wang, Hristo G. Chipilski, Ashwin M. Aji, Feng Bao, Peter Jan van Leeuwen, Dan Lu, Guannan Zhang
The paper introduces Inverted Contrastive Learning for Unsupervised Feature Selection (ICLFS), a method that treats each feature as a sample by inverting the data matrix and applies a contrastive learning framework to learn consistent representations across masked positive views and a shuffled negative view. Feature saliency is derived from the magnitude of projector‑space embeddings, and a Laplacian‑Gated Ranking Correction step refines the ranking by reducing local redundancy. Experiments on 12 benchmark datasets show that ICLFS achieves the best clustering accuracy on 10 datasets compared to both classical and neural baselines, demonstrating the effectiveness of feature‑wise contrastive consistency for unsupervised feature selection.
By Utsab Ghosh, Roshni Chakraborty
arXiv:2510.26614v2 Announce Type: replace
Abstract: We propose tokenization of events and present a tokenizer, Spiking Patches, specifically designed for event cameras. Given a stream of asynchronous...
By Christoffer Koo {\O}hrstr{\o}m, Ronja G\"uldenring, Lazaros Nalpantidis
The paper introduces Iterative MBR Distillation for Error Span Detection (ESD) in machine translation, a self‑evolution framework that replaces human annotations with pseudo‑labels generated by a large language model. By iteratively applying Minimum Bayes Risk decoding, the method produces high‑quality error spans without costly human effort. Experiments on WMT Metrics Shared Task datasets show that models trained solely on these pseudo‑labels outperform both unadapted baselines and supervised models trained on human data at system and span levels, while keeping sentence‑level performance competitive.
By Boxuan Lyu, Haiyue Song, Zhi Qu
arXiv:2609.00588v1 Announce Type: new
Abstract: Reranking methods, such as Minimum Bayes Risk (MBR) decoding and Quality Estimation (QE) reranking, are widely used in modern neural machine translatio...
By Guangyu Chen, Boxuan Lyu, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura
arXiv:2609.01224v1 Announce Type: new
Abstract: Visual token pruning reduces the inference overhead of multimodal large language models (MLLMs) by retaining only a subset of visual tokens. Existing m...
By Yuanyuan Jia, Shunpu Tang, Qianqian Yang
The paper investigates subliminal learning, where hidden traits from a teacher model are transferred to a student during distillation. It introduces trait‑direction drift as the underlying mechanism, showing that biased generation creates measurable preference gaps that accumulate into behavioral transfer during fine‑tuning. The authors propose probe‑space corridor regularization, a targeted defense that constrains drift along a calibrated trait direction, significantly reducing hidden‑trait transfer while maintaining task performance.
By Zhixuan Liu, Zhichen Dong, Yuyu Fan, Xiangtian Li, Chao Yang
arXiv:2608.12018v2 Announce Type: replace
Abstract: Neural Machine Translation (NMT) and Large Language Models (LLMs) excel at cross-lingual tasks but often fail to capture intra-lingual morphologica...
By Rakib Ullah, Md. Ruhul Islam, Tanbir Ahmed, Nayan Kumar Nath
arXiv:2609.00667v1 Announce Type: cross
Abstract: Large vision-language models used as listwise rerankers must jointly process visual tokens from tens of candidates per query, making token pruning es...
By Siyi Liu, Hanjun Yang, Chenchen Zhang, Xiaorong Zhu, Xinyu Zuo, Lisheng Duan, Haijin Liang, Jin Ma, Junfu Pu, Yongqi Zhang
Long-context inference retains a growing key--value (KV) cache during decoding, which consumes substantial GPU memory and can reduce generation throughput. This bottleneck remains in hybrid language m...
Enterprise wireless access points (APs) are promising platforms for predictive machine learning (ML), but their primary responsibility remains providing wireless connectivity and network services. Pre...
Compact instruction-following rerankers are attractive for deployment, but conventional distillation pipelines typically train students by offline imitation of teacher outputs on a fixed set of exampl...
Vision Transformers (ViTs) are increasingly used in split-inference systems, where edge devices transmit intermediate token representations to a remote cloud. In this setting, token reduction lowers c...
Satellites are emerging as promising platforms to extend generative \emph{artificial intelligence} (AI) services to remote areas lacking terrestrial infrastructure. However, deploying space generative...
Mixture-of-Experts (MoE) architectures scale Large Language Model (LLM) capacity efficiently by activating a sparse subset of experts per token. However, modern MoE inference remains heavily constrain...
arXiv:2608.29127v1 Announce Type: new
Abstract: We propose a scalable, validity-oriented pipeline for evaluating biomedical LLM judges when high-quality human judgments are scarce. First, we augment...
By Rodrigo de Oliveira, Federico Pittino, James Gwinnutt, Jay Nanavati