TrustScoreEval: Trust Scores for AI/LLM Responses — Detect hallucinations, flags misinformation & Validate outputs. Build trustworthy AI.

Scale by Subtraction: Production-tested architectural patterns for AI agents. 90% lookup, 10% reasoning. Semantic Firewalls. Silent Swarms. 0% policy violations.

Framework structures causes for AI hallucinations and provides countermeasures

A robust RAG backend featuring semantic chunking, embedding caching, and a similarity-gated retrieval pipeline. Uses GPT-4 and FAISS to provide verifiable, source-backed answers from PDFs, DOCX, and Markdown.

Theorem of the Unnameable [⧉/⧉ₛ] — Epistemological framework for binary information classification (Fixed Point/Fluctuating Point). Application to LLMs via 3-6-9 anti-loop matrix. Empirical validation: 5 models, 73% savings, zero hallucination on marked zones.

An epistemic firewall for intelligence analysis. Implements "Loop 1.5" of the Sledgehammer Protocol to mathematically weigh evidence tiers (T1 Peer Review vs. T4 Opinion) and annihilate weak claims via time-decay algorithms.

Democratic governance layer for LangGraph multi-agent systems. Adds voting, consensus, adaptive prompting & audit trails to prevent AI hallucinations through collaborative decision-making.

Legality-gated evaluation for LLMs, a structural fix for hallucinations that penalizes confident errors more than abstentions.