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New research details information omission in air-gapped LLM agents

By PulseAugur Editorial · [2 sources] ·

A new research paper, "Where Facts Go Missing," introduces a taxonomy and attribution methodology for information omission in air-gapped LLM agent pipelines. The study identifies that a significant portion of omissions, up to 73.4%, originate from deterministic middleware layers rather than the models themselves. The research also found that increasing context length is strongly associated with a higher rate of omission. AI

IMPACT Highlights critical failure points in LLM agents for sensitive applications, guiding developers to improve reliability in air-gapped environments.

RANK_REASON The cluster contains two versions of an academic paper published on arXiv detailing a new taxonomy and methodology for analyzing information omission in LLM agent pipelines.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research details information omission in air-gapped LLM agents

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The cluster contains two versions of an academic paper published on arXiv detailing a new taxonomy and methodology for analyzing information omission in LLM agent pipelines.
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COVERAGE [2]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Santhiya Rajan ·

    Where FactsGo Missing: A LayerwiseTaxonomy and Per-Layer Attribution of Information Omissionin Air-Gapped LLM Agent Pipelines

    Air-gapped and on-premises deployments in regulated settings (clinical FHIR services, legal review, sovereign infrastructure) cannot call frontier APIs; they run quantized 4-8B models via llama.cpp or vLLM behind tool servers. The dominant reliability failure is omission: the sil…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Santhiya Rajan ·

    Where Facts Go Missing: A Layerwise Taxonomy and Per-Layer Attribution of Information Omission in Air-Gapped LLMAgent Pipelines

    Air-gapped and on-premises language-model agents can silently omit decision-critical facts at any boundary between source ingestion and final answer generation. We present a nine-layer taxonomy (L0-L8), an instrumented attribution harness, and a conditional omission waterfall tha…

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