Published June 19, 2026 | Version v1

Dialogue as a Behavioral Sensor for Agent Cognition

  • 1. Independent Researcher, Taiwan

Description

In SPVE, dialogue is not only output. It can become an early sensor for agent state. This draft defines a language-state join: message length, question rate, repair phrases, contradiction markers, direct address, self-reference, and action verbs are aligned with hot-brain and outcome windows. The useful claim is not that language is mind. It is that language may move before settlements reveal success or failure.

Notes

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

Files

paper.pdf

Files (54.4 kB)

Name Size Download all
md5:0bc7733abde51e443254b0f622b7bac6
655 Bytes Download
md5:d2626ba6209586f6a4fe17f49371ef4f
43.8 kB Preview Download
md5:200c128e8f5839e5b481b9d96069a6c5
10.0 kB Download

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}