Parameter-efficient fine-tuning (PEFT) and low-bit quantization are now standard tools for adapting language models under tight compute budgets, yet their interaction is most often studied on billion-parameter models where the design space is expensive to explore. We ask a complementary question: on a specific, fully reproducible 60M-parameter encoder-decoder model (T5-small) and a single-table text-to-SQL benchmark (WikiSQL), how much task accuracy does each efficiency knob actually cost?
arXiv:2602. 10387v2 Announce Type: replace-cross Abstract: Traditional query optimization relies on cost-based optimizers that estimate execution cost (e.
By Mehmet Hamza Erol, Xiangpeng Hao, Federico Bianchi, Ciro Greco, Jacopo Tagliabue, James Zou
We’re releasing gpt-oss-120b and gpt-oss-20b—two state-of-the-art open-weight language models that deliver strong real-world performance at low cost. Available under the flexible Apache 2.
arXiv:2608. 13076v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands.
By Divya Jyoti Bajpai, Kishan Kumar Upadhyay, Manjesh Kumar Hanawal
arXiv:2607. 23815v1 Announce Type: cross Abstract: Large language models are increasingly used as semantic operators for filtering, extracting, ranking, joining, and transforming unstructured data.
By Hojae Son, Md Ashraful Islam, Huy Gia Cao, Hui Guan, Marco Serafini
arXiv:2603. 28768v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) has recently emerged as the mainstream architecture for efficiently scaling large language models while maintaining near-constant computational cost.
By Adrian Zhao, Zhenkun Cai, Zhenyu Song, Lingfan Yu, Haozheng Fan, Jun Wu, Yida Wang, Nandita Vijaykumar
arXiv:2606. 11244v1 Announce Type: cross Abstract: Efficient large language model (LLM) serving is increasingly constrained by deployment cost.
By Hongyuan Liu, Yawei Li, Zhiqiang Que, Qinli Yang, Junming Shao, Guosheng Hu
arXiv:2607. 22595v1 Announce Type: new Abstract: Mechanistic interpretability (MI) has emerged as a powerful approach for analyzing and intervening in inference computations, with a growing number of applications such as jailbreak attempt detection, truthfulness evaluation, and hallucination detection.
By Michael Blum, Mark Silberstein, Yaniv David
arXiv:2607. 25583v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) and low-bit quantization are now standard tools for adapting language models under tight compute budgets, yet their interaction is most often studied on billion-parameter models where the design space is expensive to explore.
By Mahendra Singh Rathor, Anagheem Azzam
arXiv:2511. 10480v3 Announce Type: replace-cross Abstract: Optimizing the performance of large language models (LLMs) on large-scale AI training and inference systems requires a scalable and expressive mechanism to model distributed workload execution.
By Changhai Man, Joongun Park, Hanjiang Wu, Huan Xu, Srinivas Sridharan, Tushar Krishna
arXiv:2607. 16184v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) is a popular class of large language models (LLMs), offering high efficiency and accuracy.
By Yuchen Yang, Yifan Zhao, Anisha Dasgupta, Sasa Misailovic
arXiv:2502. 11201v3 Announce Type: replace-cross Abstract: NoSQL databases are core data infrastructure, yet natural-language access to them remains underdeveloped: correct query generation must recover how a non-relational data model represents entities, nested paths, arrays, missing fields, and dynamic keys.
By Jinwei Lu, Jiawei Lu, Chen Zhang, Zhiqian Qin, Haodi Zhang, Yuanfeng Song, Raymond Chi-Wing Wong