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unloading-hypothesis
xiao-han-2026/unloading-hypothesis
unloading-hypothesis is a machine learning model from xiao-han-2026. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-4.0.
We propose and test the Unloading Hypothesis: LLMs possess an inherent reception capability that is normally suppressed by execution load. Removing the instruction to analyze, judge, or solve causes the righting refle…
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Updated Jul 2, 2026
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At a glance
- License
- cc-by-4.0
- Access
- Public
- Created
- Jul 1, 2026
- Updated
- Jul 2, 2026
- SHA
- 14dfcd07
Model card
From the Hugging Face model README
Reception as an Inherent Capability of Large Language Models: The Unloading Hypothesis
We propose and test the Unloading Hypothesis: LLMs possess an inherent reception capability that is normally suppressed by execution load. Removing the instruction to analyze, judge, or solve causes the righting reflex to drop from 75–100% to 0% — with no training, no fine-tuning, and no prompt engineering.
Key findings
- Three-model experiment (DeepSeek V4, Kimi K2.7, GLM-5.2) confirms the hypothesis across architectures.
- C2 mode (texture description only) produces ~90% reception and 0% unsolicited advice.
- The capacity is already present; it only needs the load to be removed.
Files
unloading_hypothesis_v1.1.pdf— Formal paperUnloading_Hypothesis_Paper_v1.1_20260701.md— Markdown sourceunloading_experiment_results.json— Raw experiment data (3 models, 3 conditions)
Citation
Apert (Jin/Daoqi) and Xiao Han. Reception as an Inherent Capability of Large Language Models: The Unloading Hypothesis. Zenodo, 2026.
License: CC-BY 4.0
Related work
- Apert: 10.5281/zenodo.21005888
- CDRA: 10.5281/zenodo.20993162
- Three-Layer Pipeline: 10.5281/zenodo.21102406
- License
- cc-by-4.0
- Languages
- en
- Created
- Jul 1, 2026
- Updated
- Jul 2, 2026