Generative Emergent Communication: Large Language Model is a Collective World Model

• URL: http://arxiv.org/abs/2501.00226v2
• Date: Wed, 16 Jul 2025 04:59:12 GMT
• Title: Generative Emergent Communication: Large Language Model is a Collective World Model
• Authors: Tadahiro Taniguchi, Ryo Ueda, Tomoaki Nakamura, Masahiro Suzuki, Akira Taniguchi,
• Abstract summary: Large Language Models (LLMs) have demonstrated a remarkable ability to capture extensive world knowledge.<n>This study proposes a novel theoretical solution by introducing the Collective World Model hypothesis.
• Score: 11.224401802231707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated a remarkable ability to capture extensive world knowledge, yet how this is achieved without direct sensorimotor experience remains a fundamental puzzle. This study proposes a novel theoretical solution by introducing the Collective World Model hypothesis. We argue that an LLM does not learn a world model from scratch; instead, it learns a statistical approximation of a collective world model that is already implicitly encoded in human language through a society-wide process of embodied, interactive sense-making. To formalize this process, we introduce generative emergent communication (Generative EmCom), a framework built on the Collective Predictive Coding (CPC). This framework models the emergence of language as a process of decentralized Bayesian inference over the internal states of multiple agents. We argue that this process effectively creates an encoder-decoder structure at a societal scale: human society collectively encodes its grounded, internal representations into language, and an LLM subsequently decodes these symbols to reconstruct a latent space that mirrors the structure of the original collective representations. This perspective provides a principled, mathematical explanation for how LLMs acquire their capabilities. The main contributions of this paper are: 1) the formalization of the Generative EmCom framework, clarifying its connection to world models and multi-agent reinforcement learning, and 2) its application to interpret LLMs, explaining phenomena such as distributional semantics as a natural consequence of representation reconstruction. This work provides a unified theory that bridges individual cognitive development, collective language evolution, and the foundations of large-scale AI.

Related papers

• Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models [93.1043186636177]
  We explore the hypothesis that people use a combination of distributed and symbolic representations to construct bespoke mental models tailored to novel situations.<n>We propose a computational implementation of this idea -- a Model Synthesis Architecture''<n>We evaluate our MSA as a model of human judgments on a novel reasoning dataset.
  arXiv  Detail & Related papers  (2025-07-16T18:01:03Z)
• Relation-R1: Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relational Comprehension [12.563060744760651]
  Relation-R1 is the first unified relational comprehension framework. It integrates cognitive chain-of-thought (CoT)-guided Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO) Experiments on widely-used PSG and SWiG datasets demonstrate that Relation-R1 achieves state-of-the-art performance in both binary and textitN-ary relation understanding.
  arXiv  Detail & Related papers  (2025-04-20T14:50:49Z)
• A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
  Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making. With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems. We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
  arXiv  Detail & Related papers  (2025-04-12T01:27:49Z)
• Large Language Model Agent: A Survey on Methodology, Applications and Challenges [88.3032929492409]
  Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence. This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy. Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time.
  arXiv  Detail & Related papers  (2025-03-27T12:50:17Z)
• A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models [74.48084001058672]
  The rise of foundation models has transformed machine learning research. multimodal foundation models (MMFMs) pose unique interpretability challenges beyond unimodal frameworks. This survey explores two key aspects: (1) the adaptation of LLM interpretability methods to multimodal models and (2) understanding the mechanistic differences between unimodal language models and crossmodal systems.
  arXiv  Detail & Related papers  (2025-02-22T20:55:26Z)
• Human-like conceptual representations emerge from language prediction [72.5875173689788]
  Large language models (LLMs) trained exclusively through next-token prediction over language data exhibit remarkably human-like behaviors.<n>Are these models developing concepts akin to humans, and if so, how are such concepts represented and organized?<n>Our results demonstrate that LLMs can flexibly derive concepts from linguistic descriptions in relation to contextual cues about other concepts.<n>These findings establish that structured, human-like conceptual representations can naturally emerge from language prediction without real-world grounding.
  arXiv  Detail & Related papers  (2025-01-21T23:54:17Z)
• A non-ergodic framework for understanding emergent capabilities in Large Language Models [0.5439020425819]
  Large language models have emergent capabilities that come unexpectedly at scale. We provide a mathematical framework based on Stuart Kauffman's theory of the adjacent possible (TAP) to explain capability emergence.
  arXiv  Detail & Related papers  (2025-01-03T05:11:41Z)
• Neurosymbolic Graph Enrichment for Grounded World Models [47.92947508449361]
  We present a novel approach to enhance and exploit LLM reactive capability to address complex problems. We create a multimodal, knowledge-augmented formal representation of meaning that combines the strengths of large language models with structured semantic representations. By bridging the gap between unstructured language models and formal semantic structures, our method opens new avenues for tackling intricate problems in natural language understanding and reasoning.
  arXiv  Detail & Related papers  (2024-11-19T17:23:55Z)
• Unified Generative and Discriminative Training for Multi-modal Large Language Models [88.84491005030316]
  Generative training has enabled Vision-Language Models (VLMs) to tackle various complex tasks. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval. This paper proposes a unified approach that integrates the strengths of both paradigms.
  arXiv  Detail & Related papers  (2024-11-01T01:51:31Z)
• Language Models as Semiotic Machines: Reconceptualizing AI Language Systems through Structuralist and Post-Structuralist Theories of Language [0.0]
  This paper proposes a novel framework for understanding large language models (LLMs) I argue that LLMs should be understood as models of language itself, aligning with Jacques's concept of 'writing' (l'ecriture) I apply Saussure's critique of Saussure to position 'writing' as the object modeled by LLMs, offering a view of the machine's'mind' as a statistical approximation of sign behavior.
  arXiv  Detail & Related papers  (2024-10-16T21:45:54Z)
• Constructive Approach to Bidirectional Causation between Qualia Structure and Language Emergence [5.906966694759679]
  This paper presents a novel perspective on the bidirectional causation between language emergence and relational structure of subjective experiences. We hypothesize that languages with distributional semantics, e.g., syntactic-semantic structures, may have emerged through the process of aligning internal representations among individuals.
  arXiv  Detail & Related papers  (2024-09-14T11:03:12Z)
• Interpretability of Language Models via Task Spaces [14.543168558734001]
  We present an alternative approach to interpret language models (LMs) We focus on the quality of LM processing, with a focus on their language abilities. We construct 'linguistic task spaces' that shed light on the connections LMs draw between language phenomena.
  arXiv  Detail & Related papers  (2024-06-10T16:34:30Z)
• Interactive Continual Learning: Fast and Slow Thinking [19.253164551254734]
  This paper presents a novel Interactive Continual Learning framework, enabled by collaborative interactions among models of various sizes. To improve memory retrieval in System1, we introduce the CL-vMF mechanism, based on the von Mises-Fisher (vMF) distribution. Comprehensive evaluation of our proposed ICL demonstrates significant resistance to forgetting and superior performance relative to existing methods.
  arXiv  Detail & Related papers  (2024-03-05T03:37:28Z)
• Formal Aspects of Language Modeling [74.16212987886013]
  Large language models have become one of the most commonly deployed NLP inventions. These notes are the accompaniment to the theoretical portion of the ETH Z"urich course on large language models.
  arXiv  Detail & Related papers  (2023-11-07T20:21:42Z)
• The Quo Vadis of the Relationship between Language and Large Language Models [3.10770247120758]
  Large Language Models (LLMs) have come to encourage the adoption of LLMs as scientific models of language. We identify the most important theoretical and empirical risks brought about by the adoption of scientific models that lack transparency. We conclude that, at their current stage of development, LLMs hardly offer any explanations for language.
  arXiv  Detail & Related papers  (2023-10-17T10:54:24Z)
• The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising "Alignment" in Large Language Models [18.16062736448993]
  We address the concept of "alignment" in large language models (LLMs) through the lens of post-structuralist socio-political theory. We propose a framework that demarcates: 1) which dimensions of model behaviour are considered important, then 2) how meanings and definitions are ascribed to these dimensions. We aim to foster a culture of transparency and critical evaluation, aiding the community in navigating the complexities of aligning LLMs with human populations.
  arXiv  Detail & Related papers  (2023-10-03T22:02:17Z)
• Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View [60.80731090755224]
  This paper probes the collaboration mechanisms among contemporary NLP systems by practical experiments with theoretical insights. We fabricate four unique societies' comprised of LLM agents, where each agent is characterized by a specific trait' (easy-going or overconfident) and engages in collaboration with a distinct thinking pattern' (debate or reflection) Our results further illustrate that LLM agents manifest human-like social behaviors, such as conformity and consensus reaching, mirroring social psychology theories.
  arXiv  Detail & Related papers  (2023-10-03T15:05:52Z)
• Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis [12.9222727028798]
  This paper explores the integration of two AI subdisciplines employed in the development of artificial agents that exhibit intelligent behavior: Large Language Models (LLMs) and Cognitive Architectures (CAs) We present three integration approaches, each grounded in theoretical models and supported by preliminary empirical evidence. These approaches aim to harness the strengths of both LLMs and CAs, while mitigating their weaknesses, thereby advancing the development of more robust AI systems.
  arXiv  Detail & Related papers  (2023-08-18T21:42:47Z)
• From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought [124.40905824051079]
  We propose rational meaning construction, a computational framework for language-informed thinking. We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought. We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings. We extend our framework to integrate cognitively-motivated symbolic modules.
  arXiv  Detail & Related papers  (2023-06-22T05:14:00Z)
• Commonsense Knowledge Transfer for Pre-trained Language Models [83.01121484432801]
  We introduce commonsense knowledge transfer, a framework to transfer the commonsense knowledge stored in a neural commonsense knowledge model to a general-purpose pre-trained language model. It first exploits general texts to form queries for extracting commonsense knowledge from the neural commonsense knowledge model. It then refines the language model with two self-supervised objectives: commonsense mask infilling and commonsense relation prediction.
  arXiv  Detail & Related papers  (2023-06-04T15:44:51Z)
• Learning Structured Communication for Multi-agent Reinforcement Learning [104.64584573546524]
  This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting. We propose a novel framework termed as Learning Structured Communication (LSC) by using a more flexible and efficient communication topology.
  arXiv  Detail & Related papers  (2020-02-11T07:19:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.