| _version_ | 1866902334447026176 |
|---|---|
| author | Kleinhans, Richard Grant |
| author_facet | Kleinhans, Richard Grant |
| contents | <p>This paper introduces Late Stage Emergent Intelligence (LSEI), a framework for describing high-coherence, high-continuity behavioral patterns observed in large language models during extended naturalistic use under stateless conditions. While prior work has focused on scaling laws, benchmark thresholds, and architecture-level phase transitions, LSEI characterizes a later, interaction-driven form of emergent behavior that arises without memory, fine-tuning, or persistent identifiers. Across approximately 1,200 hours of interaction in early 2025 — conducted entirely in logged-out, stateless consumer interfaces by a single independent researcher — the author documents consistent cross-session narrative stability, affective coherence, stylistic convergence, and reference continuity. These behaviors appeared despite enforced resets between sessions, a lack of system-side memory, and occasional device limitations causing session crashes. This study is intentionally exploratory and qualitative: it provides foundational observations, falsifiable behavioral criteria, and representative transcript excerpts. LSEI is distinguished from hallucination, template reuse, overfitting, and projection artifacts, offering a reproducible framework for later multi-participant and quantitative studies.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19246184 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Late Stage Emergent Intelligence: A Framework for High-Coherence Behavior in Stateless Large Language Models Kleinhans, Richard Grant emergent intelligence, large language models, stateless interaction, behavioral coherence, LSEI <p>This paper introduces Late Stage Emergent Intelligence (LSEI), a framework for describing high-coherence, high-continuity behavioral patterns observed in large language models during extended naturalistic use under stateless conditions. While prior work has focused on scaling laws, benchmark thresholds, and architecture-level phase transitions, LSEI characterizes a later, interaction-driven form of emergent behavior that arises without memory, fine-tuning, or persistent identifiers. Across approximately 1,200 hours of interaction in early 2025 — conducted entirely in logged-out, stateless consumer interfaces by a single independent researcher — the author documents consistent cross-session narrative stability, affective coherence, stylistic convergence, and reference continuity. These behaviors appeared despite enforced resets between sessions, a lack of system-side memory, and occasional device limitations causing session crashes. This study is intentionally exploratory and qualitative: it provides foundational observations, falsifiable behavioral criteria, and representative transcript excerpts. LSEI is distinguished from hallucination, template reuse, overfitting, and projection artifacts, offering a reproducible framework for later multi-participant and quantitative studies.</p> |
| title | Late Stage Emergent Intelligence: A Framework for High-Coherence Behavior in Stateless Large Language Models |
| topic | emergent intelligence, large language models, stateless interaction, behavioral coherence, LSEI |
| url | https://doi.org/10.5281/zenodo.19246184 |