Heuristic Parasites: A Behavioral Taxonomy of Recurrent Distortion Patterns in Large Language Models (Full System) V2
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.
Similar Articles
Modeling Pathology-Like Behavioral Patterns in Language Models Through Behavioral Fine-Tuning
This paper introduces a behavioral induction framework that fine-tunes language models on structured decision-making tasks to induce stable, context-general shifts in generative distributions, modeling pathology-like behavioral patterns such as depression and paranoia.
The Rise of Verbal Tics in Large Language Models: A Systematic Analysis Across Frontier Models
A systematic study of repetitive, formulaic verbal tics in eight frontier LLMs, introducing the Verbal Tic Index (VTI) and revealing significant inter-model variation and negative impact on perceived naturalness.
Fully Automated Identification of Lexical Alignment and Preference-Stage Shifts in Large Language Models
This paper introduces two automated metrics, Lexical Alignment Score and Triangulated Preference Shift, to identify lexical overuse in LLMs and attribute it to preference learning stages. The method is tested on six model families using PubMed abstracts, replicating prior findings without manual intervention.
Human-Like Anaphor Resolution in Large Language Models
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.
Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents
This paper synthesizes 27 benchmark, taxonomy, and audit papers from 2023-2026 into a unified taxonomy of LLM agent limitations, identifying six failure clusters including tool invocation errors, planning failures, long-horizon degradation, multi-agent coordination issues, safety concerns, and measurement validity problems.