mlsanityData to 14 Oct 2026

A social path to human-like artificial intelligence

arXiv 2405.15815 ↗First posted 22 May 2024cs.AI cs.LGcited by 20

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Abstract (arXiv)

Traditionally, cognitive and computer scientists have viewed intelligence solipsistically, as a property of unitary agents devoid of social context. Given the success of contemporary learning algorithms, we argue that the bottleneck in artificial intelligence (AI) progress is shifting from data assimilation to novel data generation. We bring together evidence showing that natural intelligence emerges at multiple scales in networks of interacting agents via collective living, social relationships and major evolutionary transitions, which contribute to novel data generation through mechanisms such as population pressures, arms races, Machiavellian selection, social learning and cumulative culture. Many breakthroughs in AI exploit some of these processes, from multi-agent structures enabling algorithms to master complex games like Capture-The-Flag and StarCraft II, to strategic communication in Diplomacy and the shaping of AI data streams by other AIs. Moving beyond a solipsistic view of agency to integrate these mechanisms suggests a path to human-like compounding innovation through ongoing novel data generation.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 60 of its 133 references that are in the map. First posted on arXiv 22 May 2024.

Builds on

60 of 133 references.

References · 60

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38 more
  1. 2018An Algorithmic Perspective on Imitation Learningcited by 459
  2. 2023The Curse of Recursion: Training on Generated Data Makes Models Forgetcited by 356
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 7; 2026: 6.

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  1. 2024EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environmentcited by 42
  2. 2024Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacycited by 36
  3. 2024Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learningcited by 33
  4. 2024UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AIcited by 25
  5. 2025Cultural Learning-Based Culture Adaptation of Language Modelscited by 16
  6. 2025Seven Security Challenges in Cross-domain Multi-agent LLM Systemscited by 12
  7. 2025Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Researchcited by 10
  8. 2025Multi-Actor Generative Artificial Intelligence as a Game Enginecited by 8
  9. 2025Humanlike AI Design Increases Anthropomorphism but Yields Divergent Outcomes on Engagement and Trust Globallycited by 6
  10. 2026Social physics in the age of artificial intelligencecited by 5
  11. 2024Multi-agent cooperation through learning-aware policy gradientscited by 4
  12. 2024Social Learning through Interactions with Other Agents: A Surveycited by 3
8 more
  1. 2024Psychomatics -- A Multidisciplinary Framework for Understanding Artificial Mindscited by 2
  2. 2025Attention Mechanism for LLM-based Agents Dynamic Diffusion under Information Asymmetrycited by 1
  3. 2026On the Creativity of AI Agentscited by 1
  4. 2026Solipsistic Superintelligence is Unlikely to be Cooperativecited by 1
  5. 2026A Technical Taxonomy of LLM Agent Communication Protocolscited by 1
  6. 2025LFX: Towards Unified Light Field Dense Semantic Segmentation and Salient Object Detectioncited by 0
  7. 2026Exploiting Expertise of Non-Expert and Diverse Agents in Social Bandit Learning: A Free Energy Approachcited by 0
  8. 2026Interoceptive machine framework: Toward interoception-inspired regulatory architectures in artificial intelligencecited by 0