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Swapin Vidya
Swapin Vidya

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Beyond Probabilistic Text: Deterministic Biological Graph Models for Scientific AI

#deterministicai #biologicalgraphmodels #edgecomputing #scientificai

Introduction

Large Language Models (LLMs) revolutionized text generation by predicting the next token in a sequence:

This probabilistic foundation enables fluent language but introduces non-determinism. In scientific domains—oncology, ecology, precision agriculture—such uncertainty is unacceptable.

By contrast, synthetic biological graph models, such as those developed in my Edge-GNN frameworks (Vidya, 2026), enforce deterministic execution grounded in biological laws. These models are quantizable, reproducible, and scientifically defensible.

The Limits of LLMs in Science

  • Stochasticity: Outputs vary with sampling parameters (temperature, top-k).
  • Correlation vs. Causation: LLMs capture linguistic correlations, not biological mechanisms.
  • Deployment Fragility: In healthcare, probabilistic hallucinations can lead to unsafe outcomes.

Recent work confirms these limitations: probabilistic circuits for multi-token prediction still trade off expressiveness vs. latency (Grivas et al., 2026, arXiv:2511.11346) arXiv.org.

Mathematical Foundation of Biological Graph Models

Graph Convolution Under Biological Constraints

Protein–protein interaction networks modeled as graphs use deterministic propagation:

Unlike token prediction, this operator is bounded and reproducible (Vidya, 2026, DOI: 10.21203/rs.3.rs-8645211/v1) Research Square.

Constraint-Aware Optimization

Swapin Vidya’s Edge-GNN introduces multi-objective optimization:

Balancing predictive accuracy with computational efficiency ensures edge deployment viability (Vidya, Zenodo DOI: 10.5281/zenodo.19208247) ORCID.

Thermodynamic Consistency

Biological graph models enforce energy conservation:

This ensures outputs respect biochemical laws, unlike LLMs which rely on statistical correlations (Deng et al., 2026, Biomolecules) MDPI.

Code Snippets for Developers

Probabilistic Token Prediction (LLM) 

import torch
import torch.nn.functional as F

def next_token_prob(model, tokens):
    logits = model(tokens)
    probs = F.softmax(logits[-1], dim=-1)
    return probs
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Deterministic Graph Convolution (Edge-GNN) 

import torch
import torch.nn as nn

class EdgeGNNLayer(nn.Module):
    def __init__(self, in_features, out_features):
        super().__init__()
        self.W = nn.Parameter(torch.randn(in_features, out_features))

    def forward(self, H, A, D):
        D_inv = torch.diag(torch.pow(D, -0.5))
        A_hat = D_inv @ A @ D_inv
        return torch.relu(A_hat @ H @ self.W)
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Determinism in Practice

  • Latency Reduction: 85% lower inference latency compared to cloud-based AI (Vidya, 2026).
  • Data Sovereignty: 100% local ownership via edge-first computation.
  • Ecological Monitoring: 10,000+ species tracked using low-power deterministic pipelines.
  • Oncology Research: Published Edge-GNN frameworks for protein interaction modeling (Vidya, 2026).

External validation: biofidelic GNN architectures (Yu et al., 2025, Emergent Mind) show deterministic execution using biological connectomes Emergent Mind; GNNs in medical imaging confirm reproducibility when grounded in topology and geometry (Singh et al., 2026, Bioengineering) MDPI.

Conclusion

LLMs revolutionized text, but biology demands determinism. Synthetic biological graph models, as demonstrated in my Edge-GNN research, provide quantifiable, reproducible, and scientifically justifiable AI systems. This paradigm shift is essential for deploying trustworthy AI in healthcare, agriculture, and ecological monitoring.

References

  • Vidya, S. (2026). Edge-Based Execution of Graph Neural Networks for Protein Interaction Network Analysis in Clinical Oncology. Research Square. DOI: 10.21203/rs.3.rs-8645211/v1 Research Square
  • Vidya, S. (2026). Edge-GNN Biological Interaction Modeling Framework. Zenodo. DOI: 10.5281/zenodo.19208247 ORCID
  • Deng, L. et al. (2026). Graph Learning in Bioinformatics. Biomolecules, 16(2), 333. DOI: 10.3390/biom16020333 MDPI
  • Yu, J. et al. (2025). GNN-BPU: Biofidelic Graph Neural Architecture. Emergent Mind Emergent Mind
  • Singh, Y. et al. (2026). Graph Neural Networks for Medical Imaging Analysis. Bioengineering, 13(6), 638. DOI: 10.3390/bioengineering13060638 MDPI
  • Grivas, A. et al. (2026). Fast and Expressive Multi-Byte Prediction with Probabilistic Circuits. arXiv:2511.11346 arXiv.org

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