Skip to content
#small language model Open access

CAMS Protocol R (CAMS-R-2026-001): preregistration, single-run results, and rubric erratum final

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

Protocol F (CAMS-F-2026-001): Design Rationale Companion to preregistration_F_draft.json — 2026-09-20 Status: DRAFT. The forecast file does not exist yet; no outcome-year scoring has begun. Protocols R and R2 answered does the relational pattern generalise? — twice, under lock and key. Protocol F asks the harder question the framework has been circling since its first preregistration: does a CAMS snapshot tell you anything about the future that you wouldn't already know by assuming nothing changes? Why this design is honest The forecasts will be public before the future exists. The forecast file — every society, node, metric and horizon, computed by a frozen formula — is sealed, hashed and published at registration. There is no possibility of fitting to outcomes: the outcomes haven't been scored, and the 2030 primary outcome doesn't exist at all. This is the strongest prospective posture available to a slow-moving societal model, and it is deliberately irreversible. The claim is deliberately weak. Persistence-plus-damped-drift (origin value plus half society drift, half roster drift, clipped to the scale) is close to the weakest non-trivial forecast a framework can make. That is a feature. If CAMS cannot beat a random walk, the framework's forecasting ambition should return to development — and the FAIL verdict says exactly that, pre-written. If it can, the door opens to stronger models in later registered studies. The baselines are the sceptic's baselines. Persistence ("nothing changes") and roster-mean drift ("everything follows the crowd") are the two null forecasts any sceptic would propose. The primary endpoint is the MAE ratio against persistence, gated at ≤ 0.90 — a 10% skill margin — with an exact permutation null on the pooled skill score. The backcast is labelled non-confirmatory. Origins 2000/2010 against known 2010/2020 outcomes will be computed and published with the preregistration, for transparency about whether the model form has any skill at all in-sample-of-history. It cannot count toward the verdict; those outcomes were visible during framework development. It exists so that a FAIL cannot be spun, in either direction. What happens when At registration (now): forecast generator frozen and hashed; forecast file generated from panels through 2025, sealed, published; society eligibility list frozen mechanically (panel complete through 2025). 2028: the 2027 outcome vintage is scored (blind, R2 rubric, flat-scan), sealed, and the k=1 interim is reported — report-only. 2031: the 2030 vintage lands; the primary verdict is computed and reported in the pre-registered language. 2036: the 2035 vintage; final report. This study will outlive any single session, and it is designed to: the custodianship terms, hashes and verdict language are written so that any future agent — or any human — can execute the outcome steps mechanically and the registration will still mean exactly what it means today. Immediate next steps Freeze the forecast generator (small script; the formula above, nothing else). Freeze society eligibility (panel-complete-through-2025 rule against the verified pool). Generate the forecast file; custodian seals and hashes it. Publish the preregistration + forecast file (Zenodo v5). Wait for 2027 — and score it blind.

View source

Similar papers

#small language model Dataset Open access Oct 2026

Socratic guiding questions in synthetic arithmetic data: matched LoRA runs (revision v2)

Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...

O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al. · 465 citations
#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 Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6

Related blog posts

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