What makes a territory a good place to live? For decades, planners and policymakers have leaned on numbers: unemployment rates, service counts, composite indices of opportunity and deprivation. But anyone who has watched a well-equipped town feel bleak, or a modest village feel vibrant, knows that statistics and lived experience do not always agree. A new study published in Machine Learning with Applications tackles exactly that mismatch, presenting an artificial intelligence architecture designed to reason simultaneously over objective territorial indicators and the subjective words of the people who actually live there.
The research, led by Ghinevra Comiti with Paul-Antoine Bisgambiglia and Nathalie Lameta, focuses on Corsica, an island whose sharp contrasts make it an ideal testing ground. A heavily developed coastline sits against a declining rural interior; a tourism-driven economy produces seasonal swings; and demographic growth concentrates in a handful of urban centers. The team built their system on a corpus covering all 361 Corsican municipalities, combining a composite well-being indicator called OppChoLiv, inventories of municipal amenities, and demographic profiles with survey verbatims from 246 respondents across 70 municipalities, qualitative interviews, and aggregated questionnaire scores spanning 16 dimensions of well-being, from transport and health to safety and housing.
At the heart of the system is retrieval-augmented generation, or RAG, a technique that grounds the answers of a large language model in a controlled document corpus rather than in the model’s training data alone. Standard RAG, however, struggles with the kind of questions decision-makers actually ask. A single query may cross several thematic dimensions, several municipalities, and several demographic groups at once. Semantic similarity search over fragmented text chunks handles localized factual questions well but falters when the answer requires a global synthesis, an effect compounded by the well-documented tendency of language models to lose track of information buried in the middle of long contexts.
The proposed architecture rests on three principles. First, the user’s question is explicitly decomposed into five sub-questions, each processed independently with its own retrieval step, so that every semantic dimension of a complex demand gets dedicated coverage. Second, the document index is enriched with 674 precomputed multi-level syntheses built along ten business-driven analytical views, such as a specific well-being dimension in a specific municipality, an approach inspired by the RAPTOR method but replacing its automatic clustering with a controlled partitioning by survey metadata. Each synthesis is attached to a known population of respondents, which makes every summary attributable, addressable, and reproducible, qualities that matter when a synthesis must be presented to territorial actors alongside its scope.
The third principle is a strict typing rule. A factual, quantitative query can only spawn factual sub-questions, and a qualitative query only qualitative ones, preventing the system from fabricating sub-questions for which no documentary support exists. Entity extraction is deliberately deterministic: municipalities are matched against a closed list, thematic dimensions against a fixed taxonomy of 16 categories, guaranteeing perfect precision at no inference cost. Retrieval then proceeds hierarchically, always prioritizing the most specific representation available, and injecting both the aggregated synthesis and the raw chunks behind it, so the generation model sees the big picture and the concrete evidence at once.
The team evaluated the architecture on a benchmark of 109 manually crafted questions spanning seven categories, from simple factual retrieval to causal reasoning, uncertainty handling, and deliberate trick questions about missing data. Answers were scored by an independent language model judge, GPT-4o, whose reliability was calibrated against human annotations and cross-checked against three alternative judges. The headline retrieval result is striking: the full architecture achieved an average Recall of 0.970, against 0.678 for vanilla RAG, a gap of nearly 30 percentage points reflecting a far more exhaustive mobilization of the available evidence.
On overall answer quality, the full system scored 4.62 out of 5, statistically indistinguishable from vanilla RAG’s 4.63, but the aggregate hides a crucial pattern. The advantage concentrates precisely where decisions are hardest: comparative reasoning, causal and counter-intuitive questions, and the delicate crossings between what indicators say and what inhabitants feel. Ablation tests confirmed that both the typing rule and the multi-level syntheses contribute significantly, and that removing the typing rule alone drops the score from 4.30 to 3.72, with the damage concentrated on targeted factual queries where an untyped decomposition spawns qualitative sub-questions that have no matching sources.
Case studies reveal why the design matters. Asked whether objective indicators and perceptions diverge in Bastia, vanilla RAG retrieved only quantitative scores and speculated about verbatims it never saw, while the full system articulated both registers, noting that mobility was rated objectively at 8.65 out of 10 even as residents scored it 2.24 out of 5, complaining of congestion. On a trick question about a municipality with good indicators but low perceived well-being, the full system correctly recognized that the available perception data could not be disaggregated by municipality, refusing to over-interpret a quantitative proxy as a measure of feeling. Yet the architecture has a documented weakness: on questions about missing information, its appetite for assembling rich contexts sometimes tempts it to approximate where a simpler system would refuse cleanly.
The authors are candid about costs and limits. The full pipeline issues roughly six times more language model calls per query than vanilla RAG, with latency about 4.6 times higher, though the offline synthesis construction cost only about $0.16 and is amortized once. Section-level differences often lose statistical significance after correction, and the Corsican corpus is entirely in French. The method transfers to other territories only where surveys carry structured metadata comparable to theirs. The team’s proposed remedy is a two-tier system that routes simple factual questions to cheap direct retrieval and reserves the heavyweight machinery for genuinely analytical queries, alongside an agentic extension in which a controller decides actively whether to answer or refuse, a direction they identify as the immediate next step toward AI assistants that policymakers can actually trust.
Subject of Research: A hybrid quantitative-qualitative retrieval-augmented generation architecture for territorial well-being decision support
Article Title: A hybrid quantitative–qualitative RAG approach for territorial well-being decision support
Article References: Comiti, G., Bisgambiglia, P.-A., & Lameta, N. (2026). A hybrid quantitative–qualitative RAG approach for territorial well-being decision support. Machine Learning with Applications, 26, Article 101005. https://doi.org/10.1016/j.mlwa.2026.101005
Image Credits: AI Generated
DOI: 10.1016/j.mlwa.2026.101005
Keywords: retrieval-augmented generation, large language models, territorial well-being, decision support systems, Corsica, survey data, RAPTOR, query decomposition, hallucination control, public policy, composite indicators, LLM-as-a-judge
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Denise Maddox. (October 1, 2026). Hybrid AI System Brings Hard Data and Human Feeling Into Territorial Decisions. Scienmag. https://scienmag.com/hybrid-ai-system-brings-hard-data-and-human-feeling-into-territorial-decisions/
Denise Maddox. “Hybrid AI System Brings Hard Data and Human Feeling Into Territorial Decisions.” Scienmag, 1 October 2026, https://scienmag.com/hybrid-ai-system-brings-hard-data-and-human-feeling-into-territorial-decisions/. Accessed 1 October 2026.
Denise Maddox. “Hybrid AI System Brings Hard Data and Human Feeling Into Territorial Decisions.” Scienmag. October 1, 2026. https://scienmag.com/hybrid-ai-system-brings-hard-data-and-human-feeling-into-territorial-decisions/
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Tags: AI for sustainable territorial managementAI in urban planningcombining objective data and subjective human experiencecomposite indicatorsCorsicaCorsica case studydata-driven policy analysisDecision Support Systemsdemographic and economic indicatorshallucination controlhybrid AI systemsintegrating quantitative and qualitative datalarge language modelsLLM-as-a-judgemachine learning for community well-beingPublic Policyquery decompositionRAPTORregional development analysisretrieval-augmented generationsentiment analysis in territorial decision-makingsurvey dataterritorial decision-makingterritorial well-being
