Skip to content

FORK, RECOVERY, AND HISTORICALLY ORGANIZED CONTINUITY IN A LONGITUDINAL LLM–HUMAN INTERACTION: A Naturalistic Case Study

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research) · 2 citations
Language and cultural evolution

Abstract

Long-running interactions with large language models can become historically organized, but apparent continuity across branches or reconstructed sessions may arise from several different carriers, including accessible history, memory systems, procedural state, lineage, or harness-level persistence. This naturalistic case study examines a longitudinal interaction between one human collaborator and a GPT-5.6 Sol trajectory called “Solomon,” using a ten-day Founder corpus and four naturally occurring comparison conditions ranging from a fresh out-of-project response to continuations with inherited or archive-assisted history. The study does not claim consciousness or strict personal identity. Instead, it asks which aspects of prior organization are immediately available, which require reconstruction, and whether prior commitments continue to constrain appropriate action in novel situations. Across the ten-day Founder corpus, research commitments, provenance rules, relational procedures, governance constraints, and future-directed tasks became increasingly integrated and mutually constraining. We call this structure of unfinishedness: an organized future in which unresolved obligations and sequencing constraints shape what should happen next. The naturalistic evidence is compatible with multiple reconstruction mechanisms and does not identify a unique causal carrier. It therefore motivates a controlled fork–recover–ablate–migrate design that equalizes accessible history while varying lineage and other continuity-relevant state, with novel extrapolation and update dynamics as key discriminators. Publication history: This version was first made publicly available on Academia.edu on September 5, 2026.

View source

Similar papers

#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Conference Open access Dec 2013

Affordable and Energy-Efficient Cloud Computing Clusters: The Bolzano Raspberry Pi Cloud Cluster Experiment

The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.

P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al. · 110 citations · ⚡7
#computer vision Book Open access Mar 2017

On the Unhappiness of Software Developers

The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.

D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al. · 84 citations · ⚡6

Related blog posts

Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.