ANIMUS v4.0: Honest Reconstruction of an Autonomous Knowledge System — Engine Migration, Phantom Memory Audit, and Epistemic Honesty Engineering on Consumer GPU

ANIMUS (Autonomous Network for Intelligence, Memory, and Understanding Systems) is an autonomous knowledge architecture that builds and maintains an episodic memory graph through continuous unsupervised operation. This version (v4.0) constitutes an honest reconstruction of the system: it documents and corrects incorrect claims made in prior versions, reports the findings of an audit that revealed 52% of graph nodes were undetected duplicates, and presents a complete engine migration to Gemma 4 E2B on consumer GPU. The central contributions of v4.0 are: (1) a graph audit that reduced 1,892 reported nodes to 911 verified unique nodes, with reproducible methodology and scripts fully documented; (2) an engine migration from fine-tuned Llama 3.2 3B (3.5 tok/s, CPU-only) through BitNet b1.58-2B (19 tok/s, CPU-only) to Gemma 4 E2B UD-Q4_K_XL on RTX 3050 (76.83 tok/s, 4× improvement); and (3) a four-layer epistemic honesty architecture — manual template without reasoning channel, structural marker contract, rfind()-based extraction, and validation guard — that makes it structurally impossible for confabulated or truncated content to enter the graph. The system includes a Value Cycle that generates business opportunity hypotheses every 25 autonomous cycles, with seven documented proposals evolving from generic to domain-specific (ISO 8583 message validation in financial transaction systems). Four behavioral calibration probes characterize the model's behavioral envelope, including the finding that Gemma 4 E2B confabulates with expert-level fluency — more operationally dangerous than the obvious hallucination of prior models. Implemented in Rust with a Python fetcher. Operation verified on Dell Precision 7610, i7-11800H, RTX 3050 4GB, Windows 11. Developed independently in Santo Domingo, Dominican Republic, June 2026. Supersedes v3.0 (DOI: 10.5281/zenodo.18932137).