CFBB: Epistemic Collapse Under Non-Computable Tasks - A Negative Results Study

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Authors: Trent Slade
Cited by: 0

Abstract

Negative results study examining epistemic discipline in 7 language models when confronted with RAG-mediated counting tasks. Includes full interaction logs, behavioral taxonomy, and CFBB methodology. Models tested: lfm2.5-thinking, phi3:mini, gemma3:1b, llama3.1:8b-instruct, llama3:8b, mistral:7b-instruct, wizard-vicuna-uncensored. Ground truth: 147 sentence-ending full stops. Observed answers ranged from 0 to 146, with systematic failure modes including confident fabrication, procedural confabulation, and recursive epistemic collapse.

Fields of Study & Subjects

Topic Modeling Natural Language Processing Techniques Ethics and Social Impacts of AI

Publication details

Publication date
1 January 2026
Document type
Preprint