Daily digest — 2026-09-23

YouTube (10)

Papers (10)

  • Task-Specific Efficiency Analysis: When Small Language Models Outperform Large Language Models — Jinghan Cao; Yu Ma; Xinjin Li; Qingyang Ren; Xiangyun Chen Large Language Models achieve remarkable performance but incur substantial computational costs unsuitable for resource-constrained deployments. This paper presents the first comprehensive task-specific efficiency analysis comparing 16 language models across five diverse NLP…
  • Low-bit Model Quantization for Deep Neural Networks: A Survey — Kai Liu; Qian Zheng; Kaiwen Tao; Zhiteng Li; Haotong Qin; Wenbo Li; Yong Guo; Xianglong Liu; Linghe Kong; Guihai Chen; Yulun Zhang; Xiaokang Yang With unprecedented rapid development, deep neural networks (DNNs) have deeply influenced almost all fields. However, their heavy computation costs and model sizes are usually unacceptable in real-world deployment. Model quantization, an effective weight-lighting technique,…
  • KubeAdaptor: A Docking Framework for Workflow Containerization on Kubernetes — Chenggang Shan; Guan Wang; Yuanqing Xia; Yufeng Zhan; Jinhui Zhang As Kubernetes becomes the infrastructure of the cloud-native era, the integration of workflow systems with Kubernetes is gaining more and more popularity. To our knowledge, workflow systems employ scheduling algorithms that optimize task execution order of workflow to improve…
  • XI Commandments of Kubernetes Security: A Systematization of Knowledge Related to Kubernetes Security Practices — Md. Shazibul Islam Shamim; Farzana Ahamed Bhuiyan; Akond Rahman Kubernetes is an open-source software for automating management of computerized services. Organizations, such as IBM, Capital One and Adidas use Kubernetes to deploy and manage their containers, and have reported benefits related to deployment frequency. Despite reported…
  • Context Kubernetes: Declarative Orchestration of Enterprise Knowledge for Agentic AI Systems — Charafeddine Mouzouni We introduce Context Kubernetes, an architecture for orchestrating enterprise knowledge in agentic AI systems, with a prototype implementation and eight experiments. The core observation is that delivering the right knowledge, to the right agent, with the right permissions,…
  • The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Study — Amr Mohamed; Maram Assi; Mariam Guizani Large language model assistants (LLM-assistants) present new opportunities to transform software development. Developers are increasingly adopting these tools across tasks, including coding, testing, debugging, documentation, and design. Yet, despite growing interest, there…
  • AI Observability for Developer Productivity Tools: Bridging Cost Awareness and Code Quality — Happy Bhati; Twinkll Sisodia As AI-assisted development tools proliferate, developers face a growing challenge: understanding the cost, quality, and behavioral patterns of AI interactions across their workflow. We present a unified approach to AI observability for developer productivity tools, combining…
  • Tierkreis: A Dataflow Framework for Hybrid Quantum-Classical Computing — Seyon Sivarajah; Lukas Heidemann; Alan Lawrence; Ross Duncan We present Tierkreis, a higher-order dataflow graph program representation and runtime designed for compositional, quantum-classical hybrid algorithms. The design of the system is motivated by the remote nature of quantum computers, the need for hybrid algorithms to involve…
  • RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents — Imad Aouali; Flavian Vasile; Otmane Sakhi; Alexandre Gilotte; Benjamin Heymann LLM recommendation agents increasingly produce structured recommendation reports: sets of items accompanied by natural-language justifications. Yet existing evaluations often reduce this setting to reranking small shortlisted candidate sets or judge reports mainly by semantic…
  • Agents at Risk: How Users Unwittingly Undermine LLM Safety — Fengchao Chen; Tingmin Wu; Van Nguyen; Surya. Nepal; Carsten Rudolph Large language model (LLM)-based agents are increasingly deployed in applications, such as trip-planning agents and web-use agents, to perform complex planning and execution tasks. Prior work has shown that LLM-based agents are vulnerable to context confusion, where external…