Natural-Synthesis-8B
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2026
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| _version_ | 1866901792376225792 |
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| author | José Carlos Perales Quiroga |
| author_facet | José Carlos Perales Quiroga |
| contents | <p>This technical report presents <strong>Natural-Synthesis-8B</strong>, an experimental fine-tune of Llama-3-8B trained on a synthetic dataset of 68 examples designed to install a biologically-inspired reasoning paradigm rather than domain-specific knowledge.</p> <p>The central hypothesis is that reasoning <em>process</em> and reasoning <em>content</em> are separable learning targets. Standard fine-tuning teaches models what to think. This work teaches a model <em>how</em> to think — specifically, through a five-stage cognitive growth cycle (Seed → Root Exploration → Principled Pruning → Canopy Formation → Homeostatic Review) governed by five evaluative nutrients (Coherence, Parsimony, Explanatory Power, Fecundity, Evidential Grounding).</p> <p>The result is a model that demonstrates consistent cross-domain structural reasoning, emergent systems thinking, and selective metacognitive activation at 8B parameters — capabilities that do not appear reliably in the base model. Custom evaluations show an 18% gain in cognitive flexibility over the base model baseline.</p> <p>This report documents the paradigm, training methodology, benchmark comparisons, qualitative behavioral evidence, and a frank analysis of failure modes — including the coherence-without-truth problem inherent to any coherence-optimized reasoning system.</p> <p>The model is available at: https://huggingface.co/JPQ24/llama-3-8b-Natural-synthesis-Lora-Merge</p> <p>The dataset is aviable at: https://huggingface.co/datasets/JPQ24/Natural_synthesis</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18966988 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Natural-Synthesis-8B José Carlos Perales Quiroga large language models fine-tuning metacognition systems thinking reasoning synthetic data cognitive flexibility emergent behavior <p>This technical report presents <strong>Natural-Synthesis-8B</strong>, an experimental fine-tune of Llama-3-8B trained on a synthetic dataset of 68 examples designed to install a biologically-inspired reasoning paradigm rather than domain-specific knowledge.</p> <p>The central hypothesis is that reasoning <em>process</em> and reasoning <em>content</em> are separable learning targets. Standard fine-tuning teaches models what to think. This work teaches a model <em>how</em> to think — specifically, through a five-stage cognitive growth cycle (Seed → Root Exploration → Principled Pruning → Canopy Formation → Homeostatic Review) governed by five evaluative nutrients (Coherence, Parsimony, Explanatory Power, Fecundity, Evidential Grounding).</p> <p>The result is a model that demonstrates consistent cross-domain structural reasoning, emergent systems thinking, and selective metacognitive activation at 8B parameters — capabilities that do not appear reliably in the base model. Custom evaluations show an 18% gain in cognitive flexibility over the base model baseline.</p> <p>This report documents the paradigm, training methodology, benchmark comparisons, qualitative behavioral evidence, and a frank analysis of failure modes — including the coherence-without-truth problem inherent to any coherence-optimized reasoning system.</p> <p>The model is available at: https://huggingface.co/JPQ24/llama-3-8b-Natural-synthesis-Lora-Merge</p> <p>The dataset is aviable at: https://huggingface.co/datasets/JPQ24/Natural_synthesis</p> |
| title | Natural-Synthesis-8B |
| topic | large language models fine-tuning metacognition systems thinking reasoning synthetic data cognitive flexibility emergent behavior |
| url | https://doi.org/10.5281/zenodo.18966988 |