We present a comprehensive framework for cognitive computing that transforms the paradigm of artificial intelligence from pattern-matching systems to genuinely interactive, social agents. This work introduces two complementary innovations: (1) the Speech Act Processor Model (SAPM), which integrates speech act theory with language models to enable sophisticated understanding of communicative intentions, and (2) the Intelligence-to-Intelligence (I2I) framework with its Intelligence-Intelligence Interface (I³), establishing a universal protocol for interactions between human and artificial intelligences.
The SAPM operationalizes the triadic nature of communication—encompassing elocutionary (linguistic structure), illocutionary (intention), and perlocutionary (effect) components—within a scalable computational framework. By embedding this model into agentic computing architectures and extending it through the I2I framework, we enable refined human-agent and agent-agent interactions, advancing toward truly cognitive and socio-culturally aware artificial intelligence systems.
Our architecture supports the evolution of current language models into two distinct agent classes: task-based (a) and autonomous (A), each with specific interaction patterns and capabilities. The framework includes standardized protocols for agent communication, decision-making, and collaborative problem-solving, with specific attention to blockchain-mediated consensus for decentralized coordination.
We detail the theoretical foundations, architectural design, and implementation of both SAPM and the I2I framework. Through evaluation on speech-act annotated corpora and simulated agentic environments, we demonstrate the architecture's effectiveness in enabling sophisticated multi-agent interactions while maintaining alignment with human intentions and social norms.
The landscape of artificial intelligence stands at a pivotal moment. Large language models (LLMs) such as GPT-4 and PaLM have revolutionized natural language processing (NLP), enabling fluent text generation and reasoning at scale. Yet, despite their remarkable linguistic capabilities, current LLMs operate primarily on syntactic and semantic patterns, lacking integrated models of communicative intention and social context. This limitation becomes increasingly apparent as we move toward complex, multi-agent systems requiring genuine understanding and social interaction.
The historical progression from rule-based systems through neural networks to current language models reveals a consistent pattern: each advancement brings us closer to human-like processing, yet falls short of true cognitive interaction. This gap manifests most clearly in the inability of current systems to engage in genuine dialogue, understand contextual nuances, or participate effectively in multi-agent scenarios.
The field of artificial intelligence is transitioning from isolated models to interconnected, agentic systems. This evolution necessitates a fundamental shift in how we conceptualize and implement AI systems. Rather than extending the pattern-matching paradigm, we need a comprehensive architecture for cognitive computing that transforms language models into true cognitive agents. This transformation must operate at multiple levels:
Central to meaningful human communication are speech acts, an analytical construct introduced in philosophy of language and pragmatics. Speech act theory, principally advanced by Austin (1962) and Searle (1969), identifies three core components of an utterance:
Current AI systems, while capable of generating contextually appropriate text, do not inherently encode these triadic layers of speech acts in a structured manner. This limitation impedes the development of truly cognitive systems capable of understanding and participating in human-like communication.
We address these challenges through two complementary innovations:
Together, these components create a universal protocol for intelligence interaction, enabling sophisticated multi-agent systems while maintaining alignment with human values and intentions.