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The End Of Universal Lifelong Identifiers: Identity Systems For The AI Era
Shriphani Palakodety
https://arxiv.org/abs/2506.02027 https://

The End Of Universal Lifelong Identifiers: Identity Systems For The AI Era
Many identity systems assign a single, static identifier to an individual for life, reused across domains like healthcare, finance, and education. These Universal Lifelong Identifiers (ULIs) underpin critical workflows but now pose systemic privacy risks. We take the position that ULIs are fundamentally incompatible with the AI era and must be phased out. We articulate a threat model grounded in modern AI capabilities and show that traditional safeguards such as redaction, consent, and access c…

as_skitter: Skitter IP graph (2005)
An aggregate snapshot of the Internet Protocol (IP) graph, as measured by the traceroute tool on CAIDA's skitter infrastructure, in 2005.
This network has 1696415 nodes and 11095298 edges.
Tags: Technological, Communication, Unweighted
https://networks.skewed.de/net/as_s…

This https://arxiv.org/abs/2505.01651 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Human-AI Governance (HAIG): A Trust-Utility Approach
This paper introduces the HAIG framework for analysing trust dynamics across evolving human-AI relationships. Current categorical frameworks (e.g., "human-in-the-loop" models) inadequately capture how AI systems evolve from tools to partners, particularly as foundation models demonstrate emergent capabilities and multi-agent systems exhibit autonomous goal-setting behaviours. As systems advance, agency redistributes in complex patterns that are better represented as positions along continua rat…

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks
Tri Kurniawan Wijaya, Xinyang Shao, Gonzalo Fiz Pontiveros, Edoardo D'Amico
https://arxiv.org/abs/2506.03391

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks
Recommender systems are pivotal in delivering personalized experiences across industries, yet their adoption and scalability remain hindered by the need for extensive dataset- and task-specific configurations. Existing systems often require significant manual intervention, domain expertise, and engineering effort to adapt to new datasets or tasks, creating barriers to entry and limiting reusability. In contrast, recent advancements in large language models (LLMs) have demonstrated the transform…

Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation
Qiming Zhu, Jialun Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Shing-Chi Cheung
https://arxiv.org/abs/2506.03535

Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation
Current research on large language models (LLMs) with retrieval-augmented code generation (RACG) mainly focuses on single-language settings, leaving cross-lingual effectiveness and security unexplored. Multi-lingual RACG systems are valuable for migrating code-bases across programming languages (PLs), yet face risks from error (e.g. adversarial data corruption) propagation in cross-lingual transfer. We construct a dataset spanning 13 PLs with nearly 14k instances to explore utility and robustne…

Plateau Sigma – The Great Reset EP
#byncnd

Verification of the Timing System for the X-ray Imaging and Spectroscopy Mission in the GPS Unsynchronized Mode
Megumi Shidatsu, Yukikatsu Terada, Takashi Kominato, So Kato, Ryohei Sato, Minami Sakama, Takumi Shioiri, Yugo Motogami, Yuuki Niida, Chulsoo Kang, Toshihiro Takagi, Taichi Nakamoto, Chikara Natsukari, Makoto S. Tashiro, Kenichi Toda, Hironori Maejima, Shin Watanabe, Ryo Iizuka, Rie Sato, Chris Baluta, Katsuhiro Hayashi, Tessei Yoshida, Shoji Ogawa, Yoshiaki Kanemaru, Kotaro Fukushima, Akio Hoshino, Hiromitsu Takahashi, Masayoshi Nobukawa, Tsunefumi Mizuno, Kazuhiro Nakazawa, Shinichiro Uno, Ken Ebisawa, Satoshi Eguchi, Satoru Katsuda, Aya Kubota, Naomi Ota, Atsushi Tanimoto, Yuichi Terashima, Yohko Tsuboi, Yuusuke Uchida, Hideki Uchiyama, Shigeo Yamauchi, Tomokage Yoneyama, Satoshi Yamada, Nagomi Uchida, Matt Holland, Michael Loewenstein, Tahir Yaqoob, Eric D. Miller, Robert S. Hill, Efrain C. Perez-Solis, Morgan D. Waddy, Mark Mekosh, Joseph B. Fox, Isabella S. Brewer, Emily Aldoretta, Koji Mukai, Kenji Hamaguchi, Francois Mernier, Anna Ogorzalek, Katja Pottschmidt, Mihoko Yukita
#toXiv_bot_toot

"In all my discussions with scientists across every sector, exactly zero think the journal system works well."
https://asterainstitute.substack.com/p/scientific-publishing-enough-is-enough

Scientific Publishing: Enough is Enough
Why we're no longer funding journal publications

RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines
Xiwei Xu, Hans Weytjens, Dawen Zhang, Qinghua Lu, Ingo Weber, Liming Zhu
https://arxiv.org/abs/2506.03401

RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines
Recent studies show that 60% of LLM-based compound systems in enterprise environments leverage some form of retrieval-augmented generation (RAG), which enhances the relevance and accuracy of LLM (or other genAI) outputs by retrieving relevant information from external data sources. LLMOps involves the practices and techniques for managing the lifecycle and operations of LLM compound systems in production environments. It supports enhancing LLM systems through continuous operations and feedback …

SysLLMatic: Large Language Models are Software System Optimizers
Huiyun Peng, Arjun Gupte, Ryan Hasler, Nicholas John Eliopoulos, Chien-Chou Ho, Rishi Mantri, Leo Deng, Konstantin L\"aufer, George K. Thiruvathukal, James C. Davis
https://arxiv.org/abs/2506.01249

SysLLMatic: Large Language Models are Software System Optimizers
Automatic software system optimization can improve software speed, reduce operating costs, and save energy. Traditional approaches to optimization rely on manual tuning and compiler heuristics, limiting their ability to generalize across diverse codebases and system contexts. Recent methods using Large Language Models (LLMs) offer automation to address these limitations, but often fail to scale to the complexity of real-world software systems and applications. We present SysLLMatic, a system th…