GPT-4 Technical Report

Abstract—Large Language Models (LLMs) suffer from inherent stochasticity, limiting their utility in high-stakes enterprise environments where determinism and auditability are required. This paper introduces the MFOUR Vibe Framework (MVF), a platform-agnostic architectural standard that transforms probabilistic natural language intent into deterministic software artifacts. We define a five-layer topology, comprising the Kernel Identity, Synaptic Routing, Interface Contracts, Context Anchoring, and the Mirror Test. Furthermore, we introduce The Vibe Integrity Score (VIS), a quantitative metric for evaluating the structural adherence of generative outputs. This specification provides the foundational schema and logic protocols for building "Glass Box" AI systems that are observable, secure, and commercially viable.

Language (Technology) isPower: A Critical Surve…Language (Technology) is Power: A Critical Survey of "Bias" in NLPThe Impact of AI onDeveloper Productivity…The Impact of AI on Developer Productivity: Evidence from GitHub CopilotExploring the Responsesof Large Language Model…Exploring the Responses of Large Language Models to Beginner Programmers' Help RequestsMaking LLMs Worth EveryPenny: Resource-Limited…Making LLMs Worth Every Penny: Resource-Limited Text Classification in BankingMathVista: EvaluatingMath Reasoning in Visua…MathVista: Evaluating Math Reasoning in Visual Contexts with GPT-4V, Bard, and Other Large Multimodal ModelsChain of ThoughtEmpowers Transformers t…Chain of Thought Empowers Transformers to Solve Inherently Serial ProblemsComparative analysis ofGPT-4-based ChatGPT’s…Comparative analysis of GPT-4-based ChatGPT’s diagnostic performance with radiologists using real-world radiology reports of brain tumorsUnderstanding theInterplay between…Understanding the Interplay between Parametric and Contextual Knowledge for Large Language ModelsMono-InternVL: Pushingthe Boundaries of…Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-trainingSelf-RewardingVision-Language Model…Self-Rewarding Vision-Language Model via Reasoning DecompositionRank-R1: EnhancingReasoning in LLM-based…Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement LearningA Call for New Recipesto Enhance Spatial…A Call for New Recipes to Enhance Spatial Reasoning in MLLMsDo LLM Agents HaveRegret? A Case Study in…Do LLM Agents Have Regret? A Case Study in Online Learning and GamesOpenDriveVLA: TowardsEnd-to-end Autonomous…OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action ModelGPT-4 Technical ReportGPT-4 Technical Report過去の参考文献中心の論文この論文を引用する論文古い新しい

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