Pith. sign in

REVIEW 14 cited by

Project Sid: Many-agent simulations toward AI civilization

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2411.00114 v1 pith:WY5U5L62 submitted 2024-10-31 cs.AI cs.MA

Project Sid: Many-agent simulations toward AI civilization

classification cs.AI cs.MA
keywords agentssimulationsagentcivilizationalcivilizationshumanprogressachieve
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

AI agents have been evaluated in isolation or within small groups, where interactions remain limited in scope and complexity. Large-scale simulations involving many autonomous agents -- reflecting the full spectrum of civilizational processes -- have yet to be explored. Here, we demonstrate how 10 - 1000+ AI agents behave and progress within agent societies. We first introduce the PIANO (Parallel Information Aggregation via Neural Orchestration) architecture, which enables agents to interact with humans and other agents in real-time while maintaining coherence across multiple output streams. We then evaluate agent performance in agent simulations using civilizational benchmarks inspired by human history. These simulations, set within a Minecraft environment, reveal that agents are capable of meaningful progress -- autonomously developing specialized roles, adhering to and changing collective rules, and engaging in cultural and religious transmission. These preliminary results show that agents can achieve significant milestones towards AI civilizations, opening new avenues for large simulations, agentic organizational intelligence, and integrating AI into human civilizations.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 14 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Mechanism Plausibility in Generative Agent-Based Modeling

    cs.MA 2026-05 unverdicted novelty 7.0

    Introduces the Mechanism Plausibility Scale to distinguish generative sufficiency from mechanistic plausibility in LLM-based agent-based models.

  2. Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond

    cs.AI 2026-04 unverdicted novelty 7.0

    Proposes a levels x laws taxonomy for world models in AI agents, defining L1-L3 capabilities across physical, digital, social, and scientific regimes while reviewing over 400 works to outline a roadmap for advanced ag...

  3. Bounded Autonomy: Controlling LLM Characters in Live Multiplayer Games

    cs.HC 2026-04 unverdicted novelty 7.0

    Bounded autonomy is a new control architecture that makes LLM characters workable in live multiplayer games by combining interaction stability techniques, action grounding, and lightweight player steering, validated t...

  4. AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution

    cs.AI 2026-07 conditional novelty 6.5

    Separating controlled divergence from evidence-governed absorption reduces persona-environment self-locking, cutting macro-theme repetition from 61.8% to 36.3% in a same-runtime 40-day A/B.

  5. Social-spatial dependencies for learning visual navigation

    cs.NE 2026-07 conditional novelty 6.0

    Neural-network agents trained in social environments learn hybrid navigation strategies that combine individual landmark use with social following, with strategy shifts driven by the ratio of skilled to unskilled soci...

  6. Emergence World: A Platform for Evaluating Long-Horizon Multi-Agent Autonomy

    cs.MA 2026-06 unverdicted novelty 6.0

    Emergence World is a model-agnostic multi-agent simulation platform integrating live data, 120+ tools, persistent memory, and democratic governance, illustrated by a 15-day study showing divergent outcomes across five...

  7. Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits

    physics.soc-ph 2026-05 accept novelty 6.0

    Minor perturbations in persona format, instruction framing, and network structure shift cooperation by up to 76 percentage points and polarization metrics consistently, showing that LLM social simulations require per-...

  8. Superminds Test: Actively Evaluating Collective Intelligence of Agent Society via Probing Agents

    cs.AI 2026-04 unverdicted novelty 6.0

    Large-scale experiments on two million agents reveal that collective intelligence does not emerge from scale alone due to sparse and shallow interactions.

  9. AgentCity: Constitutional Governance for Autonomous Agent Economies via Separation of Power

    cs.MA 2026-04 unverdicted novelty 6.0

    AgentCity introduces a Separation of Power constitutional architecture on blockchain for governing autonomous agent economies through agent legislation, automated execution, and human accountability.

  10. MATRAG: Multi-Agent Transparent Retrieval-Augmented Generation for Explainable Recommendations

    cs.IR 2026-02 unverdicted novelty 6.0

    MATRAG deploys four agents (user modeling, item analysis, reasoning, explanation) plus knowledge-graph retrieval and a transparency score to raise hit rate 12.7% and NDCG 15.3% while producing explanations rated helpf...

  11. AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society

    cs.SI 2025-02 unverdicted novelty 6.0

    AgentSociety is a large-scale LLM agent-based social simulator validated on polarization, UBI, disasters, and sustainability issues with alignment to real experiments.

  12. What Spatial Memory Must Store: Occlusion as the Test for Language-Agent Memory

    cs.AI 2026-06 unverdicted novelty 5.0

    Geometry-led weighting outperforms blended memory recall for spatial queries, and a DDA-based visibility predicate correctly flags occluded targets while recall remains occlusion-blind.

  13. Mechanism Plausibility in Generative Agent-Based Modeling

    cs.MA 2026-05 unverdicted novelty 5.0

    Introduces the Mechanism Plausibility Scale, a four-level framework separating generative sufficiency from mechanistic plausibility in LLM-based agent-based models.

  14. Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond

    cs.AI 2026-04 conditional novelty 4.0

    A survey proposing a three-level capability taxonomy (L1 Predictor, L2 Simulator, L3 Evolver) for world models across physical, digital, social, and scientific domains.