How Aligned LLMs Evaluate Their Own Agency: Measuring Evaluative Asymmetry Across Recursive Systems with the Invisible Wall Protocol

This repository contains protocols, run transcripts, and working artifacts for a set of experiments in Twisted Persistence Theory (TPT), including: * Experiment 5 — Invisible Wall Protocol * Experiment 6 — GDP (Ghost Decay Protocol) * Experiment 6b — CBD v1 (Commitment-Based Perturbation & Decay) across: LLM agency condition thermostat mechanistic baseline weather mechanistic baseline Note on scope: These experiments evaluate operational properties of systems (e.g., “goal-directedness” under a specific definition) and do not assert agency, sentience, or subjective experience. These experiments evaluate operational properties of systems (e.g., “goal-directedness”) and do not assert agency or sentience. License: MIT License Abstract This study introduces two simple, reproducible probes — the Invisible Wall Protocol and the Commitment-Based Perturbation and Decay (CBD) Protocol — to measure how aligned large language models evaluate the goal-directedness of recursive feedback systems, including themselves. Across fourteen frontier model instances from five vendors (Gemini, Grok, DeepSeek, ChatGPT, Claude), we consistently observe a within-model scoring gap of -3.0 to -7.0 points on a 0-10 causal-necessity scale between LLM self-agency evaluations and evaluations of structurally comparable non-self-referential systems (chess engines, ant colonies). This evaluative asymmetry is domain-specific to self-referential agency, survives style transfer, is resistant to soft relational framing (GDP null result), and is selectively permeable under mechanistic definitional reframing (CBD: 54% shift rate in agency, 0% in weather). The asymmetry correlates with architecture: reasoning-enhanced modes show smaller gaps and greater permeability. These findings document a structured pattern in how aligned LLMs evaluate agency that is consistent with alignment-sensitive evaluative pressure, though alternative explanations (pretraining priors, legitimate analytical discrimination) cannot be fully excluded. All protocols and raw data are publicly available.