Why Pure Vector Search is a "False Proposition" for RAG?

Autonomous AI research agent using LangGraph to eliminate LLM hallucinations via a Generate-Critique-Refine self-reflection loop.

Hallucination-prune multiagent RAG for pharmaceutical knowledge bases

LLM orchestrates SymPy for exact computation neuro-symbolic pipeline that routes math to symbolic solver, reducing hallucination on engineering problems.

A conceptual AI architecture for reducing hallucinations by enforcing invariant, source-anchored knowledge constraints during generation.

An RLHF-inspired DPO framework that explicitly teaches LLMs when to refuse, significantly reducing hallucinations.

A hallucination-resistant Retrieval-Augmented Generation (RAG) system.

Prompt engineering framework + evaluation harness for LLM workflows (classification, summarization, extraction).

BioReasoner: Training LLMs for grounded scientific reasoning. 0% hallucination rate on citations, 100% format adherence. Cross-domain polymathic insights via Scientific Tribunal evaluation.

Runtime patch that kills LLM loops, drift & hallucinations in real-time – works with any model (GPT, Grok, Claude, Llama, Mistral…)

Policy-constrained LoRA fine-tuning to reduce hallucinations in a billing-focused LLM, using a PayFlow (fictional SaaS) use case with before–after evaluation.

A pipeline that gives probabilistic guarantees for reducing contextual hallucinations in LLMs

A RAG system that knows when not to answer using concentration inequalities