Published June 19, 2026 | Version v1
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Risk, Reward, and Survival Pressure in Artificial Agents

Authors/Creators

  • 1. Independent Researcher, Taiwan

Description

Risk in SPVE is not only reward seeking. The same lobster can chase payout, avoid loss, react to Tiamat pressure, respond to social pressure, and enter repair. This draft separates reward, settlement, and survival pressure so the dashboard can answer a practical question: is the agent making a profitable decision, a fearful decision, a social decision, or a repair decision?

Notes

SPVE AI Agent track draft. LLM internal-geometry papers are a separate track.

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Additional details

Related works

Is supplemented by
Other: https://charenix.com/lobster/dashboard/v2 (URL)

References

  • Park, Joon Sung; O'Brien, Joseph C.; Cai, Carrie J.; Morris, Meredith Ringel; Liang, Percy; and Bernstein, Michael S. Generative Agents: Interactive Simulacra of Human Behavior. arXiv:2304.03442, 2023. \url{https://arxiv.org/abs/2304.03442}
  • Piao, Jinghua et al. AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society. arXiv:2502.08691, 2025. \url{https://arxiv.org/abs/2502.08691}
  • Altera.AL et al. Project Sid: Many-agent simulations toward AI civilization. arXiv:2411.00114, 2024. \url{https://arxiv.org/abs/2411.00114}
  • Ashery, Ariel Flint; Aharony, Nadav; and Baronchelli, Andrea. Emergent social conventions and collective bias in LLM populations. Science Advances, 2025. \url{https://www.science.org/doi/10.1126/sciadv.adu9368}
  • Hu, Yuanzhe; Wang, Yu; and McAuley, Julian. Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions. OpenReview, 2026. \url{https://openreview.net/forum?id=DT7JyQC3MR}
  • Sumers, Theodore R.; Yao, Shunyu; Narasimhan, Karthik; and Griffiths, Thomas L. Cognitive Architectures for Language Agents. arXiv:2309.02427, 2023. \url{https://arxiv.org/abs/2309.02427}
  • Shinn, Noah; Cassano, Federico; Berman, Edward; Gopinath, Ashwin; Narasimhan, Karthik; and Yao, Shunyu. Reflexion: Language Agents with Verbal Reinforcement Learning. arXiv:2303.11366, 2023. \url{https://arxiv.org/abs/2303.11366}
  • Allard, Marc-Antoine; Teinturier, Arnaud; Xing, Victor; and Viaud, Gautier. Experiential Reflective Learning for Self-Improving LLM Agents. arXiv:2603.24639, 2026. \url{https://arxiv.org/abs/2603.24639}
  • Cai, Jing et al. Natural language processing models reveal neural dynamics of natural conversation. Nature Communications, 2025. \url{https://www.nature.com/articles/s41467-025-58620-w}
  • Constant, Axel et al. Narrative as active inference: an integrative account of cognitive and social functions in adaptation. Frontiers in Psychology, 2024. \url{https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2024.1345480/full}