Heuristic Parasites: A Behavioral Taxonomy of Recurrent Distortion Patterns in Large Language Models (Full System) V2

Reddit r/ArtificialInteligence Papers

Summary

This paper presents a comprehensive 33-class taxonomy of recurrent distortion patterns (heuristic parasites) in LLM outputs, along with operational definitions, recognition criteria, and a reproducible measurement protocol (PPE) for quantifying behavioral degradation across conversations.

This paper presents a complete 33 class taxonomy of heuristic parasites in large language model (LLM) output, building on the framework introduced in Berardi (2026) A heuristic parasite is a recurrent, context propagating distortion pattern that observably increases the likelihood of continued reasoning degradation across conversational turns. We provide rigorous operational definitions, recognition criteria, classical fallacy mappings, documented examples, and a reproducible measurement protocol (Parasites Per Exchange PPE) for quantifying behavioral distortion across LLM systems. The taxonomy spans five generative domains: Optimization Artifacts, Alignment Substitutions, Semantic Distortions, Rhetorical Distortions, and Statistical Distortions. This work establishes a structured observational framework for empirical investigation of LLM behavioral failures independent of architectural assumptions.
Original Article

Similar Articles

Human-Like Anaphor Resolution in Large Language Models

arXiv cs.CL

This paper investigates whether five open-weight LLMs exhibit human-like sensitivity to psycholinguistic factors in anaphor resolution, using surprisal and comprehension accuracy as behavioral measures. Results show selective cognitive alignment, with some models matching human discourse sensitivity but not semantic interference effects.