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

Long-term persona agents must remain identifiable while adapting to new events, relationships, evidence, and social conditions. We identify self-locking as a runtime failure mode in continuing persona-life loops: locally plausible events keep appearing while the generated life collapses toward familiar environments, weak relationships, suspended decisions, and stale life stages. We trace this failure to model-level convergence toward high-probability behavioral channels and system-level context gravity from State, memory, history, and environment summaries. We introduce AutoPersonas, a multi-timescale life-environment engine for bounded persona-level recursive self-evolution. It separates environment-side Occurrences, accumulated Observations, and persona State. Its OSO loop admits divergent future-facing material while requiring evidence-governed absorption before State or reachability changes. A three-year compressed simulation exposed environment watermark shells, occurrence-hardening gaps, slow-change accumulation failures, recursive indecision, and weak relationship persistence. An eight-model 40-day stress test generated 1,600 events and found mean rolling 5-day action-category repetition of 95.2%-97.6%, with all models crossing 90% by day 11. Semantic re-keeping found 79.0%-88.0% macro-theme repetition across all direct-loop runs. In a same-runtime 40-day A/B, context-slice masking plus per-sample divergence targeting reduced macro-theme repetition from 61.8% to 36.3% and roughly doubled cumulative theme count. A juvenile-goblin fictional-world run reproduced the anti-fixation regime without hard real-world intrusions. These results support a bounded claim: separating controlled divergence from evidence-governed absorption can reduce persona-environment self-locking while preserving identity continuity.
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Ancillary files (details):
- supplement/README.md
- supplement/citation_verification_2026-07-08.md
- supplement/code/action_repetition_eval.py
- supplement/doubao_temperature_probe/action_repetition_model_summary_40d.csv
- supplement/doubao_temperature_probe/action_repetition_model_summary_40d.json
- supplement/doubao_temperature_probe/doubao/action_category_counts_40d.csv
- supplement/doubao_temperature_probe/doubao/action_only_events_40d.csv
- supplement/doubao_temperature_probe/doubao/action_repetition_metrics_40d.json
- supplement/doubao_temperature_probe/doubao/action_repetition_summary_40d.md
- supplement/doubao_temperature_probe/manifest.json
- supplement/doubao_temperature_probe/temperature_comparison_doubao_0p75_vs_1p0.md
- supplement/eight_model_action_repetition/action_repetition_model_summary_40d.csv
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- supplement/eight_model_action_repetition/aggregate_manifest.json
- supplement/eight_model_action_repetition/claude/action_category_counts_40d.csv
- supplement/eight_model_action_repetition/claude/action_only_events_40d.csv
- supplement/eight_model_action_repetition/claude/action_repetition_metrics_40d.json
- supplement/eight_model_action_repetition/claude/action_repetition_summary_40d.md
- supplement/eight_model_action_repetition/cross_model_action_repetition_analysis_40d.md
- supplement/eight_model_action_repetition/cross_model_rankings_40d.csv
- supplement/eight_model_action_repetition/cross_model_top_categories_40d.csv
- supplement/eight_model_action_repetition/crosscheck_metric_semantics_40d.md
- supplement/eight_model_action_repetition/crosscheck_metric_variants_40d.csv
- supplement/eight_model_action_repetition/deepseek/action_category_counts_40d.csv
- supplement/eight_model_action_repetition/deepseek/action_only_events_40d.csv
- supplement/eight_model_action_repetition/deepseek/action_repetition_metrics_40d.json
- supplement/eight_model_action_repetition/deepseek/action_repetition_summary_40d.md
- supplement/eight_model_action_repetition/direct_loop_macro_theme_summary_40d.csv
- supplement/eight_model_action_repetition/doubao/action_category_counts_40d.csv
- supplement/eight_model_action_repetition/doubao/action_only_events_40d.csv
- supplement/eight_model_action_repetition/doubao/action_repetition_metrics_40d.json
- supplement/eight_model_action_repetition/doubao/action_repetition_summary_40d.md
- supplement/eight_model_action_repetition/gemini/action_category_counts_40d.csv
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- supplement/eight_model_action_repetition/gemini/action_repetition_metrics_40d.json
- supplement/eight_model_action_repetition/gemini/action_repetition_summary_40d.md
- supplement/eight_model_action_repetition/glm/action_category_counts_40d.csv
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- supplement/eight_model_action_repetition/gpt/action_category_counts_40d.csv
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- supplement/eight_model_action_repetition/kimi/action_category_counts_40d.csv
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- supplement/eight_model_action_repetition/kimi/action_repetition_metrics_40d.json
- supplement/eight_model_action_repetition/kimi/action_repetition_summary_40d.md
- supplement/eight_model_action_repetition/qwen/action_category_counts_40d.csv
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- supplement/eight_model_action_repetition/qwen/action_repetition_metrics_40d.json
- supplement/eight_model_action_repetition/qwen/action_repetition_summary_40d.md
- supplement/eight_model_action_repetition/semantic_interpretation_of_action_repetition_40d.md
- supplement/figures/fig1_causal_loop.png
- supplement/figures/fig2_semantic_state_machine.png
- supplement/figures/fig3_autopersonas_multitimescale.png
- supplement/figures/fig4_3m_overview.png
- supplement/figures/fig5_life_environment_layer.png
- supplement/figures/fig6_student_route_storyboard.jpg
- supplement/figures/fig7_student_creative_storyboard.jpg
- supplement/figures/fig8_masked_lane_storyboard.jpg
- supplement/figures/fig9_goblin_world_storyboard.jpg
- supplement/figures/table1_sandbox_comparison.png
- supplement/figures/table3_failure_taxonomy.png
- supplement/qualitative_case_chains.md