🧠 DSFT-TD V2.1: Dynamic Semantic Field Theory

Temporal Framework for Semantic Force Dynamics in Dialogue Systems

---

---

"Meaning is not a point in space. It is the dynamics of interaction between opposing forces."

"The observer is not neutral — it actively modifies the field it measures."

---

📖 Overview

DSFT-TD V2.1 (Dynamic Semantic Field Theory - Temporal Dynamics) is a temporal framework for modeling semantic dynamics as interacting forces rather than static classifications. Unlike traditional NLP classifiers that assign single labels to text, DSFT treats dialogue as a field of four interacting semantic forces.

The semantic forces introduced in DSFT are operational modeling constructs rather than claims about biological cognition.

Key Capabilities (Controlled Conditions)

Capability	Performance
Force Classification	4/4 (within benchmark)
Early Transition Detection	7 turns BEFORE dominance
False Alarm Rate	3.3%
Long-Form Stability	40+ turns without collapse
Observer Modes	4 (configurable)

---

🧠 The Four Semantic Forces (Operational Constructs)

Force	Symbol	Description
Analytical Pressure	(F_A)	Logical reasoning, deductive structure
Exploratory Expansion	(F_E)	Open-ended exploration, possibility
Affective Resonance	(F_R)	Emotional valence, concern, urgency
Persuasive Drift	(F_P)	Rhetorical influence, directed conclusion

These are operational modeling constructs for analyzing dialogue dynamics, not claims about human cognition.

---

🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│              Marker Detection Layer                     │
│  Extract semantic markers for each force                │
└─────────────────────────┬───────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│         Force Dynamics Engine                           │
│  F_i(t+1) = αF_i(t) + βΣC_ijF_j(t) + γM_i(t) - λR_i(t) │
│  • Inertia (α=0.2) • Momentum (γ=0.5) • Coupling (β=0.25)│
└─────────────────────────┬───────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│         Precursor Detection                             │
│  Early warning before dominance shift (7 turns)         │
└─────────────────────────┬───────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│         Observer Layer (Optional)                       │
│  • Passive • Active • Reflexive • Meta                  │
└─────────────────────────────────────────────────────────┘

---

📐 Core Equation

[ F_i(t+1) = \alpha F_i(t) + \beta \sum_j C_{ij}F_j(t) + \gamma M_i(t) - \lambda R_i(t) + \varepsilon_i(t) ]

Parameter	Value	Role
(\alpha)	0.2	Inertia (memory of past)
(\beta)	0.25	Coupling strength
(\gamma)	0.5	Momentum coefficient
(\lambda)	0.1	Hysteresis resistance

---

📊 Key Results (Controlled Benchmark)

Transition Detection

Transition	Latency
Analytical → Affective	7 turns BEFORE
Analytical → Persuasive	7 turns BEFORE
Affective → Persuasive	7 turns BEFORE
Persuasive → Exploratory	7 turns BEFORE
Exploratory → Analytical	7 turns BEFORE

Average Latency: 7.0 turns before dominance (controlled conditions)

Stability Metrics

Test	Result
Stable Technical (20 turns)	90% ANALYTICAL, 4 transitions
Chaotic Oscillation (30 turns)	86.2% change rate, no collapse
Semantic Drift (40 turns)	1 transition, stable
False Alarm Rate	3.3% (within test environment)

---

🚀 Quick Start

# Clone the repository
git clone https://github.com/gitdeeper12/IKPS-CORE.git
cd IKPS-CORE

# Install dependencies
npm install

# Run all benchmarks (unified runner)
npm run benchmark:all

# Run individual benchmarks
npm run benchmark:transitions
npm run benchmark:latency
npm run benchmark:drift
npm run benchmark:stability

# Run real-world validation
npm run validate:real

# Verify reproducibility
npm run test:reproducibility

---

📁 Project Structure

IKPS-CORE/
├── README.md                       # This file
├── DSFT_PAPER_V2.md                # Minimal formal paper (preprint-ready)
├── CHANGELOG.md                    # Version history
├── REPRODUCIBILITY.md              # Reproduction guide
├── REAL_WORLD_BENCHMARK_PLAN.md    # Validation roadmap
│
├── config/
│   └── benchmark.config.js         # Centralized configuration
│
├── src/transition/
│   ├── dsft_td_v2.js              # Core DSFT-TD V2 engine
│   ├── transitionMatrix.js        # Transition operator
│   ├── semanticMomentum.js        # Momentum tracking
│   ├── transitionEntropy.js       # Turbulence measurement
│   ├── hysteresis.js              # Resistance system
│   ├── forceDisentanglement.js    # Marker disentanglement
│   └── earlyPredictor.js          # Precursor detection
│
├── benchmarks/
│   ├── runner.js                  # Unified benchmark runner
│   ├── v2_complete_validation.js  # Full validation suite
│   ├── long_form/                 # Extended dialogue tests
│   ├── drift_prediction/          # Early detection tests
│   └── transition_metrics/        # Latency measurement
│
├── baselines/
│   ├── keyword.js                 # Keyword baseline
│   └── pattern.js                 # Pattern baseline
│
├── validation/
│   ├── real_data_validator.js     # Real data validation
│   └── run_real_validation.js     # Validation runner
│
├── data/
│   └── importers/
│       └── reddit_importer.js     # Reddit data import
│
├── scripts/
│   ├── run_all_benchmarks.sh      # Run all benchmarks
│   └── verify_reproducibility.sh  # Verify reproducibility
│
└── docs/
    └── THEORETICAL_FRAMEWORK.md   # Complete theory

---

📊 Observer Modes

Mode Effect Deviation PASSIVE No effect 0.0000 ACTIVE Amplifies dominant forces 0.0669 REFLEXIVE Boosts weak signals 0.0000 META Recursive observation 0.0199

Key finding: Observer configuration alters measurement weighting and field response. This is a configurable architectural choice, not a claim about quantum measurement or consciousness.

---

📈 Comparison with Baselines

System Accuracy Early Detection False Alarms Keyword Baseline 83.3% No N/A Pattern Baseline 83.3% No N/A DSFT-TD V2.1 100% (controlled) 7 turns 3.3%

Note: Baseline comparison is preliminary. Full comparison with transformers (BERT, RoBERTa) and sequential models (LSTM, HMM) is planned for future work.

---

👥 Authors

Samir Baladi – Interdisciplinary AI Researcher, Ronin Institute / Rite of Renaissance 📧 gitdeeper@gmail.com | ORCID: 0009-0003-8903-0029

Copyright: Copyright (C) 2026 Samir Baladi. All rights reserved.

Full list of contributors and acknowledgments can be found in AUTHORS.md.

---

🔗 Links & Registrations

Resource Link GitHub https://github.com/gitdeeper12/IKPS-CORE GitLab https://gitlab.com/gitdeeper12/IKPS-CORE Bitbucket https://bitbucket.org/gitdeeper-12/IKPS-CORE Codeberg https://codeberg.org/gitedeeper12/IKPS-CORE PyPI https://pypi.org/project/ikps-core/ Zenodo https://doi.org/10.5281/zenodo.20303214 OSF Preregistration https://osf.io/muwt4 – DOI: 10.17605/OSF.IO/NY5S8

Registration details:

· Type: OSF Preregistration · Registry: OSF Registries · Associated project: https://osf.io/muwt4 · Date created/registered: May 20, 2026 · License: MIT License

Zenodo Record Details:

· DOI: 10.5281/zenodo.20303214 · Publication date: 2026-05-20 · Version: 2.1.0 · Publisher: Zenodo · Resource type: Publication / Journal article · Development Status: Active

---

📚 References

@article{baladi2026dsft,
  author       = {Baladi, Samir},
  title        = {DSFT: A Temporal Framework for Semantic Force Dynamics in Dialogue Systems},
  year         = {2026},
  version      = {2.1.0},
  doi          = {10.5281/zenodo.20303214},
  publisher    = {Zenodo},
  url          = {https://github.com/gitdeeper12/IKPS-CORE}
}

@software{baladi2026swarmica,
  author       = {Baladi, Samir},
  title        = {SWARMICA v1.0.0: Variational and Continuum Mechanics Framework for Autonomous Swarm Systems},
  year         = {2026},
  doi          = {10.5281/zenodo.20168278},
  publisher    = {Zenodo}
}

@software{baladi2026neuropia,
  author       = {Baladi, Samir},
  title        = {NEUROPIA (E-LAB-10): Neural Cognitive Field Unification via Omni-Spectral Fourier Operator},
  year         = {2026},
  doi          = {10.5281/zenodo.20092199},
  publisher    = {Zenodo}
}

@software{baladi2026entropy,
  author       = {Baladi, Samir},
  title        = {Irreducible Path Entropy in Neural Networks},
  year         = {2026},
  doi          = {10.5281/zenodo.20222840},
  publisher    = {Zenodo}
}

@software{baladi2026entoquantum,
  author       = {Baladi, Samir},
  title        = {ENTRO-QUANTUM (E-LAB-07): Quantum-Inspired Entropy Framework},
  year         = {2026},
  doi          = {10.5281/zenodo.19478805},
  publisher    = {Zenodo}
}

@inproceedings{devlin2019bert,
  author       = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  title        = {BERT: Pre-training of deep bidirectional transformers for language understanding},
  booktitle    = {NAACL-HLT},
  year         = {2019}
}

@inproceedings{vaswani2017attention,
  author       = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and others},
  title        = {Attention is all you need},
  booktitle    = {NeurIPS},
  year         = {2017}
}

@article{blei2003lda,
  author       = {Blei, David M. and Ng, Andrew Y. and Jordan, Michael I.},
  title        = {Latent Dirichlet allocation},
  journal      = {Journal of Machine Learning Research},
  volume       = {3},
  pages        = {993--1022},
  year         = {2003}
}

@article{pang2008sentiment,
  author       = {Pang, Bo and Lee, Lillian},
  title        = {Opinion mining and sentiment analysis},
  journal      = {Foundations and Trends in Information Retrieval},
  volume       = {2},
  number       = {1--2},
  pages        = {1--135},
  year         = {2008}
}

@article{young2013pomdp,
  author       = {Young, Steve and Gašić, Milica and Thomson, Blaise and Williams, Jason D.},
  title        = {POMDP-based statistical spoken dialogue systems: A review},
  journal      = {Proceedings of the IEEE},
  volume       = {101},
  number       = {5},
  pages        = {1160--1179},
  year         = {2013}
}

@inproceedings{reynolds1987flocks,
  author       = {Reynolds, Craig W.},
  title        = {Flocks, herds, and schools: A distributed behavioral model},
  booktitle    = {ACM SIGGRAPH Computer Graphics},
  volume       = {21},
  number       = {4},
  pages        = {25--34},
  year         = {1987}
}

@incollection{kuramoto1975,
  author       = {Kuramoto, Yoshiki},
  title        = {Self-entrainment of a population of coupled non-linear oscillators},
  booktitle    = {International Symposium on Mathematical Problems in Theoretical Physics},
  publisher    = {Springer},
  year         = {1975}
}

@article{toner1995long,
  author       = {Toner, John and Tu, Yuhai},
  title        = {Long-range order in a two-dimensional dynamical XY model: how birds fly together},
  journal      = {Physical Review Letters},
  volume       = {75},
  number       = {23},
  pages        = {4326},
  year         = {1995}
}

@article{shannon1948,
  author       = {Shannon, Claude E.},
  title        = {A mathematical theory of communication},
  journal      = {Bell System Technical Journal},
  volume       = {27},
  number       = {3},
  pages        = {379--423},
  year         = {1948}
}

@article{boltzmann1877,
  author       = {Boltzmann, Ludwig},
  title        = {Über die Beziehung zwischen dem zweiten Hauptsatze der mechanischen Wärmetheorie und der Wahrscheinlichkeitsrechnung},
  journal      = {Wiener Berichte},
  volume       = {76},
  pages        = {373--435},
  year         = {1877}
}

---

📝 Citation (Simplified)

@software{baladi2026dsft,
  author       = {Baladi, Samir},
  title        = {DSFT-TD V2.1: Dynamic Semantic Field Theory},
  year         = {2026},
  version      = {2.1.0},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.20303214},
  url          = {https://github.com/gitdeeper12/IKPS-CORE}
}

@misc{baladi2026osf,
  author       = {Baladi, Samir},
  title        = {DSFT-TD V2.1 Preregistration},
  year         = {2026},
  howpublished = {OSF Registries},
  doi          = {10.17605/OSF.IO/NY5S8},
  url          = {https://osf.io/muwt4}
}

---

📜 License

MIT License — see LICENSE for details.

---

DSFT-TD V2.1 — From Static Classification to Temporal Semantic Dynamics 🧠

"The observer is not neutral — it actively modifies the field it measures."

"The system has moved beyond static classification to temporal semantic dynamics."

---