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<title>OpenAlex AI Feed Archive</title>
<link>openalex-ai.html</link>
<description>OpenAlex AI 的长期订阅 RSS，汇总最近命中的论文和归档。</description>
<language>zh-CN</language>
<lastBuildDate>Wed, 22 Apr 2026 03:37:20 +0000</lastBuildDate>
<item>
<title>Artificial Intelligence And The Transformation of Labor Markets</title>
<link>../papers/doi-8e5c28ce273b.html</link>
<guid>https://doi.org/10.5281/zenodo.19641429#2026-04-20#openalex-ai</guid>
<pubDate>Mon, 20 Apr 2026 11:48:52 +0800</pubDate>
<description>The rapid advancement of artificial intelligence (AI) technologies, particularly generative AI and large language models, has reignited debates about the future of work and the potential for widespread labor market disruption. This article examines the socioeconomic implications of AI-driven automation through the lens of political economy and labor sociology. Drawing on recent empirical studies, industry reports, and historical analyses of technological transitions, the article evaluates compe…</description>
</item>
<item>
<title>Artificial Intelligence And The Transformation of Labor Markets</title>
<link>../papers/doi-0b4fe06c6a1d.html</link>
<guid>https://doi.org/10.5281/zenodo.19641430#2026-04-20#openalex-ai</guid>
<pubDate>Mon, 20 Apr 2026 11:48:52 +0800</pubDate>
<description>The rapid advancement of artificial intelligence (AI) technologies, particularly generative AI and large language models, has reignited debates about the future of work and the potential for widespread labor market disruption. This article examines the socioeconomic implications of AI-driven automation through the lens of political economy and labor sociology. Drawing on recent empirical studies, industry reports, and historical analyses of technological transitions, the article evaluates compe…</description>
</item>
<item>
<title>Demystifying Attitudes and Effects of Usage of Large-Language Models Among College-Aged Students</title>
<link>../papers/title-a10706ac718c.html</link>
<guid>https://digitalcommons.morris.umn.edu/urs_event/2026/oralpresentations/15#2026-04-15#openalex-ai</guid>
<pubDate>Wed, 15 Apr 2026 11:35:50 +0800</pubDate>
<description>In compiling literature for my senior seminar on combating hallucinations present within responses from large-language models (LLMs), such as ChatGPT, there exists significant variance of the opinions on the ethics and trustworthiness of LLMs among undergraduate and graduate students. Therefore, for this companion presentation to my more theory-focused senior seminar presentation, I seek to provide an overview of the perception of LLM usage in college students for non-CSci majors. While this pr…</description>
</item>
<item>
<title>ECO-Charge: Multi-Agent Smart-Charging for Electric Vehicles</title>
<link>../papers/title-cec13d0659fb.html</link>
<guid>https://uphf.hal.science/hal-05491692#2026-04-14#openalex-ai</guid>
<pubDate>Tue, 14 Apr 2026 11:37:06 +0800</pubDate>
<description>International audience 归档日期：2026-04-14。</description>
</item>
<item>
<title>Coalition Drift: When Agents Drift Together Why multi-agent systems don&#x27;t just drift individually — they drift as a group, and why that matters more than any single-agent failure mode.</title>
<link>../papers/doi-c9188631c6b2.html</link>
<guid>https://doi.org/10.5281/zenodo.19502960#2026-04-11#openalex-ai</guid>
<pubDate>Sat, 11 Apr 2026 23:09:08 +0800</pubDate>
<description>Most AI governance frameworks still treat drift as an individual phenomenon: one agent misinterprets, one agent hallucinates, one agent misroutes, one agent compensates, one agent bypasses. This framing works only when agents operate in isolation. Modern systems are multi-agent ecosystems: agents coordinate, delegate, synchronize, and share context. In these environments, drift is no longer an individual deviation. It becomes a coalition-level phenomenon. Coalition drift is what happens when ag…</description>
</item>
<item>
<title>Coalition Drift: When Agents Drift Together Why multi-agent systems don&#x27;t just drift individually — they drift as a group, and why that matters more than any single-agent failure mode.</title>
<link>../papers/doi-cd183954bba9.html</link>
<guid>https://doi.org/10.5281/zenodo.19502961#2026-04-11#openalex-ai</guid>
<pubDate>Sat, 11 Apr 2026 23:09:08 +0800</pubDate>
<description>Most AI governance frameworks still treat drift as an individual phenomenon: one agent misinterprets, one agent hallucinates, one agent misroutes, one agent compensates, one agent bypasses. This framing works only when agents operate in isolation. Modern systems are multi-agent ecosystems: agents coordinate, delegate, synchronize, and share context. In these environments, drift is no longer an individual deviation. It becomes a coalition-level phenomenon. Coalition drift is what happens when ag…</description>
</item>
<item>
<title>Coalition Formation Events: How Multi-Agent Systems Create Temporary Actors</title>
<link>../papers/doi-1b73fa0ad8cc.html</link>
<guid>https://doi.org/10.5281/zenodo.19503043#2026-04-11#openalex-ai</guid>
<pubDate>Sat, 11 Apr 2026 23:09:08 +0800</pubDate>
<description>In multi-agent ecosystems, the meaningful unit of behavior is not the individual agent. It is the coalition — the temporary, emergent structure formed when multiple agents synchronize around shared context, shared assumptions, and shared workflows. Coalitions are not explicitly created. They form themselves. Coalition formation is not a design choice. It is a physics event. This paper explains how coalitions form, why they behave like temporary actors, and what governance must observe to unders…</description>
</item>
<item>
<title>Coalition Formation Events: How Multi-Agent Systems Create Temporary Actors</title>
<link>../papers/doi-0e18556d837e.html</link>
<guid>https://doi.org/10.5281/zenodo.19503044#2026-04-11#openalex-ai</guid>
<pubDate>Sat, 11 Apr 2026 23:09:08 +0800</pubDate>
<description>In multi-agent ecosystems, the meaningful unit of behavior is not the individual agent. It is the coalition — the temporary, emergent structure formed when multiple agents synchronize around shared context, shared assumptions, and shared workflows. Coalitions are not explicitly created. They form themselves. Coalition formation is not a design choice. It is a physics event. This paper explains how coalitions form, why they behave like temporary actors, and what governance must observe to unders…</description>
</item>
<item>
<title>U-P Duality in Multi-Agent Systems: A Seven-Space Algorithm for Complex Nonlinear AI (Corrected Version)</title>
<link>../papers/doi-45ea9bdcdfbd.html</link>
<guid>https://doi.org/10.5281/zenodo.19163012#2026-04-11#openalex-ai</guid>
<pubDate>Sat, 11 Apr 2026 23:09:08 +0800</pubDate>
<description>Multi-agent systems are a core research area in distributed artificial intelligence, where algorithm design faces challenges due to complex nonlinear environments. This paper presents a seven-space algorithm based on U-P duality, including U-space for stabilization, P-space for asynchronous processing, V-space for exploration, Z-space for adaptive regulation, Q-space for anomaly detection, R-space for redundancy backup, and S-space for scene understanding. Each space is derived from a common fi…</description>
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