MACI: Multi-LLM Agent Collaborative Intelligence

Introduces SocraHealth, using LLM-based agents in structured debates to refine diagnoses and correct historical record inaccuracies from patient data. Case studies with GPT-4 and Bard across two experiments demonstrate logical, hallucination-free debates and a significant advance over traditional diagnostic techniques.

Applies LLMs to a five-stage sales-planning pipeline: market landscape survey, customer profiling, product usage analysis, strategy formulation, and pitch crafting, blending artificial with genuine data for confidentiality while optimizing value-oriented conversion and profitability.

Introduces the SocraSynth platform: conditional statistics and systematic context enhancement through continuous arguments with adjustable contentiousness; a human moderator and two opposing LLM agents run knowledge generation then Socratic and formal-logic evaluation, concluding with conciliatory synthesis. Case studies across three domains.

Investigates GPT-4's shortcomings in reasoning and ethics (hallucination, imitation without understanding, weak fact-checking) and proposes remedies: the CoCoMo framework with Socratic prompt ensembles, demonstration-taught ethical behavior from the Noora chatbot experience, and broader training objectives beyond cross-entropy.

A committee of a human moderator and two GPT-4 agents, ignited by the Adam and Eve narrative, explores myth in ecological interpretation, AI ethics, and the brain-technology nexus, demonstrating foundation models as catalysts for interdisciplinary dialogue beyond individual human scope.

A systematic approach to Socratic prompt templates: definition, elenchus, dialectic, maieutics, generalization, and counterfactual reasoning, mapped to inductive, deductive, and abductive inference, with the observation that conveying goal and intent before a dialogue connects the model to external context and improves performance.

Proposes consciousness modeling, reinforcement learning, and prompt-template formulation to build AI agents prioritizing fairness, beneficence, non-maleficence, empathy, adaptability, transparency, and critical and exploratory thinking: knowledge combined with compassion.

CRIT streamlines critical reading with structured, recursive prompts: extract conclusions and supporting reasons, scrutinize reason-to-claim arguments, suggest counterarguments, weigh source reliability, and emit an overall quality assessment; applicable to fact-checking foundation-model outputs and to K-12 education.

Addresses the small-data problem in medical imaging through data augmentation, transfer learning, federated learning, and GANs, proposing knowledge-guided generation that incorporates domain knowledge, with pre-trained language models as a source of high-quality knowledge.