Provenance record
Position: Let's Strengthen Verifiability If We Can't Enforce Reproducibility
Source note: Preprints on societal impact.
Discourse valence
0
Mixed or uncertain
confidence 60% · 1 item
-100
-50
0
+50
+100
0
Adverse readingFavourable reading
Excerpt as ingested
arXiv:2609.35854v1 Announce Type: cross Abstract: In the field of Machine Learning, many papers contain empirical results supporting claimed statements or illustrating the performance of a proposed method. However, most practitioners know that (1) results are generally hard to reproduce, and increasingly so, (2) code is not often available to do so, and (3) it hinders the development of research. In this position paper, we analyze and quantify these issues, and make concrete proposals to improve result checkability, if not reproducibility. Code and supporting materials are available at https://github.com/giddyyupp/position-enforce-verifiability.
Every model that read this
| Model | Provider | Stage | Score | Conf. | Latency | Prompt | When |
|---|---|---|---|---|---|---|---|
| Llama 3.3 70B | Meta | analysis | 0 | 60% | 3299ms | v1.0.0 / m1.0.1 | 2026-09-30 05:41 |
Llama 3.3 70B · reading
Paper discusses reproducibility issues in ML research
evidence: speculative
horizon: n/a
Evidence extracted
- Results in Machine Learning papers are hard to reproduce
- Code for reproducing results is often not available
The chain
SOURCE arXiv cs.CY (Computers and Society) (tier 1)
↓
DOCUMENT e91f99fd-e2ab-4c26-a1bb-2bf6d8b3bd9e
https://arxiv.org/abs/2609.35854
↓
EVIDENCE 2 extracted excerpts
↓
MODEL RUN 1 run, methodology 1.0.1
↓
SCORE 0 (Mixed or uncertain)
↓
CONFIDENCE 60%