IKPS-CORE · V2.1.0 · EntropyLab Series · May 2026

Dynamic
Semantic
Field Theory

A temporal framework modeling dialogue semantics as four interacting forces — not static labels. DSFT-TD detects semantic transitions 7 turns before they occur.

📄 Read Paper 🐙 GitHub Repository ⛓ GitLab Mirror
DOI: 10.5281/zenodo.20303214 MIT License Stable Research Core Node.js 18+ ORCID: 0009-0003-8903-0029
4/4
Force Classification
controlled benchmark
7
Turns Early Detection
before dominance shift
3.3%
False Alarm Rate
stable technical dialogue
40+
Turns Stable
without force collapse
4
Observer Modes
configurable measurement
7
Dev Stages
VEFS → DSFT-TD
01 — Abstract

Beyond Static Classification

Traditional NLP classifiers assign labels to individual utterances in isolation. Human dialogue, however, is defined by temporal dynamics — gradual shifts, emerging tensions, persuasive drift. DSFT models these dynamics as an evolving field of four interacting semantic forces.

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.

Note: The four semantic forces in DSFT are operational modeling constructs, not claims about biological cognition, quantum mechanics, or consciousness. All reported performance figures are from controlled synthetic benchmarks. Real-world validation on human-annotated corpora is planned per the External Falsification Protocol v1.0.
02 — The Four Forces

Semantic Force Specification

Each dialogue generates a field of four operational forces. Forces are not independent — they interact via an asymmetric coupling matrix, creating emergent temporal dynamics.

FA
Analytical Pressure
Logical reasoning, deductive structure, factual precision. Suppresses exploration and affect.
jacobian eigenvalues therefore implies prove
FE
Exploratory Expansion
Open-ended inquiry, hypothesis generation, possibility-seeking. Amplifies affective resonance.
perhaps maybe explore what if wonder
FR
Affective Resonance
Emotional valence, urgency, concern, care. Weakly suppresses analytical pressure.
concern danger crisis urgent fear
FP
Persuasive Drift
Rhetorical influence, directed conclusions, guided reasoning. Leverages analytical structure.
guaranteed must customers clearly evidence
03 — Mathematics

Core Formulation

Forces evolve under a discrete-time equation incorporating inertia, inter-force coupling, momentum, and hysteresis resistance. Early detection uses the precursor probability function.

F_i(t+1) = α·F_i(t) + β·Σⱼ Cᵢⱼ·Fⱼ(t) + γ·Mᵢ(t) − λ·Rᵢ(t) + εᵢ(t)
α0.20Inertia — memory of past state
β0.25Coupling strength
γ0.50Momentum coefficient
λ0.10Hysteresis resistance
Mᵢ(t)ΔFFᵢ(t) − Fᵢ(t−1)
Rᵢ(t)persist×0.15Accumulated dominance penalty
Force Evolution Equation — DSFT-TD V2.1 core dynamics
P_precursor(Fⱼ) = mean(Fⱼ_recent) + max(0, trend)×2 + residualⱼ
Precursor Probability — fires when P > 0.5, signals imminent transition
L = t_dominance − t_precursor = 7 turns
Transition Latency — positive value = early detection (controlled benchmark)
C = [[0, −.15, −.10, +.12],
    [−.12, 0, +.18, −.08],
    [−.08, +.15, 0, +.10],
    [+.10, −.05, +.08, 0]]
Coupling Matrix C — rows and columns: [F_A, F_E, F_R, F_P]
04 — Results

Benchmark Performance

Controlled synthetic benchmark results. All five canonical transition types detected with identical 7-turn early warning latency.

Transition Type Precursor Dominance Latency Status
Analytical → AffectiveTurn 0Turn 77 turns✓ Early
Analytical → PersuasiveTurn 0Turn 77 turns✓ Early
Affective → PersuasiveTurn 0Turn 77 turns✓ Early
Persuasive → ExploratoryTurn 0Turn 77 turns✓ Early
Exploratory → AnalyticalTurn 0Turn 77 turns✓ Early
Average 7.0 turns All ✓
Observer Modes

Configurable Measurement Layer

Four observer modes alter how the semantic field is sampled. This is a configurable architectural choice, not a claim about quantum measurement.

PASSIVE
0.0000
No modification to force values. Neutral measurement baseline.
ACTIVE
0.0669
Amplifies the currently dominant force. Emphasis analysis mode.
REFLEXIVE
0.0000
Boosts weak and suppressed forces. Sensitivity to emerging dynamics.
META
0.0199
Recursive self-referential observation. Observer effect studies.
05 — Development History

Seven Stages of Failure → Refinement

DSFT was not conceived in one step. Each predecessor system failed in a specific, instructive way — and each failure generated the next design insight.

STAGE 1 — VEFS
Vectorized Epistemic Field System
2/4 correct classification
✗ Basin overlap between Emotional and Exploratory
STAGE 2 — SBCL
Semantic Basis Construction Layer
1/4 correct classification
✗ Complete collapse — all types → single force
STAGE 3 — RFDM
Relational Field Dynamics Model
4/4 correct — first full differentiation
✗ No temporal continuity between turns
STAGE 4 — RFDM-I
RFDM + Inertia
4/4 + stability achieved
✗ Stability = 0 (unmeasurable)
STAGE 5 — RFDM-II
RFDM + Coupling + Stochastic Noise
4/4 + σ=0.5 + inter-force correlations
~ Stable, measurable — but no early detection
STAGE 6 — OBSERVER LAYER
Configurable Measurement Modes
4 observer modes + quantified deviations
~ Observer effect formalized
STAGE 7 — DSFT-TD ✓ CURRENT
Transition Dynamics — Momentum + Residuals
7-turn early detection · 3.3% false alarms · 40+ turn stability
✓ Stable Research Core · Version 2.1.0