{"slug": "public-services-are-increasingly-strained-by-llm-written-appeals-for-benefits", "title": "Public services are increasingly strained by LLM-written appeals for benefits", "summary": "A new arXiv preprint by Chris Schmitz and colleagues warns that AI agents using large language models are increasingly generating mass appeals to government services, a phenomenon they call 'agentic flooding,' which could strain unprepared public agencies. The study, based on 84 potential cases across 11 jurisdictions, finds near-term risk is highest for financially attractive but complex services, and recommends mitigation strategies that avoid trade-offs with equitable access.", "body_md": "# Computer Science > Computers and Society\n\n[Submitted on 17 Aug 2026 (\n\n[v1](https://arxiv.org/abs/2608.16603v1)), last revised 19 Aug 2026 (this version, v2)]# Title:Characterizing Agentic Flooding of Government Services\n\n[View PDF](/pdf/2608.16603)\n\n[HTML (experimental)](https://arxiv.org/html/2608.16603v2)\n\nAbstract:AI agents are making it easier for the public to interact with government, such as by helping them apply for benefits, understand complex policies, and make their opinions heard. Although improving service accessibility is beneficial, any resulting surges in demand could strain unprepared government services. We term such surges agentic flooding of government services (\"flooding\") and provide three contributions. First, based on a collected dataset of 84 potential cases of flooding across 11 jurisdictions, we posit that flooding is likely occurring widely today, mostly through large language models (LLMs) generating text cheaply. Second, we evaluate what services are most exposed to flooding. We develop a risk matrix to analyze a service's exposure, and suggest that near-term risk is highest for financially attractive, but complex services. Finally, we map possible government responses to flooding. Precedent suggests these responses will likely be sufficient to stop most cases of flooding, but the fastest to deploy - friction-inducing measures like fees - often trade off equitable access to public services. Accordingly, we close by recommending near-term actions that may allow governments to mitigate flooding without invoking this trade-off.\n\n## Submission history\n\nFrom: Chris Schmitz [[view email](/show-email/64e03926/2608.16603)]\n\n**Mon, 17 Aug 2026 13:59:28 UTC (248 KB)**\n\n[[v1]](/abs/2608.16603v1)**[v2]** Wed, 19 Aug 2026 16:17:45 UTC (248 KB)\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/public-services-are-increasingly-strained-by-llm-written-appeals-for-benefits", "canonical_source": "https://arxiv.org/abs/2608.16603", "published_at": "2026-08-24 16:30:21+00:00", "updated_at": "2026-08-24 17:12:50.442913+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-policy"], "entities": ["arXiv", "Chris Schmitz"], "alternates": {"html": "https://wpnews.pro/news/public-services-are-increasingly-strained-by-llm-written-appeals-for-benefits", "markdown": "https://wpnews.pro/news/public-services-are-increasingly-strained-by-llm-written-appeals-for-benefits.md", "text": "https://wpnews.pro/news/public-services-are-increasingly-strained-by-llm-written-appeals-for-benefits.txt", "jsonld": "https://wpnews.pro/news/public-services-are-increasingly-strained-by-llm-written-appeals-for-benefits.jsonld"}}