MCCF: Multi-Channel Coherence Field for Affective Control in Generative Agents. V2.3 Modifications

 

Multi-Channel Coherence Field for Affective Control in Generative Agents

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1. Abstract

The Multi-Channel Coherence Field (MCCF) is a low-dimensional, dynamical control system for steering behavior in generative agents. Inspired by recent findings that emotional representations in transformer models form causal, manipulable vectors in activation space, MCCF externalizes these latent structures into an explicit, inspectable, and composable architecture.

MCCF treats agent behavior not as a sequence of prompts, but as a trajectory through an affective control field, where internal state, social modeling, expression, and intent interact over time.

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2. Core Principle

Behavior is not directly generated—it is selected from a probability landscape shaped by a coherent affective field.

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3. System Overview

At time t, the MCCF state is:

$$M(t)=\{C_1(t),C_2(t),C_3(t),C_4(t)\}$$

Where each channel is a vector in a shared latent space:

$$C_i(t)\in {\mathbb{R}}^n$$

Typical dimensionality:

$$n=8\text{ to }32$$

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4. Channel Definitions

C₁ — Self-State (Internal Affect)

• Persistent internal emotional condition
• Not directly observable
• Primary driver of trajectory bias

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C₂ — Other-Model (Theory of Mind)

• Representation of other agents’ states
• Supports empathy, deception, alignment
• Updated via perception

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C₃ — Expression Layer (Surface Behavior)

• What is externally expressed
• Derived from internal state via transformation
• May diverge from C₁

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C₄ — Drive / Intent Field

• Encodes goals, motivations, narrative direction
• Acts as a gradient over future states

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5. Dynamics

The system evolves continuously:

$$\frac{d{C}_i}{dt}=\sum _{j=1}^4{K}_{ij}{C}_j+I_i(t)$$

Where:

• K = coupling matrix (4×4 of linear operators or matrices)
• Iᵢ(t) = external inputs (prompt, environment, memory)

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Interpretation

• Channels influence each other
• Behavior emerges from interaction, not isolation
• Stability and oscillation are tunable via K

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6. Expression Mapping

Expression is not direct:

$$C_3=D(C_1,C_2,C_4)$$

Where D is a nonlinear transformation operator.

Examples:

• Suppression: reduce magnitude of components
• Inversion: map anger → politeness (sarcasm)
• Projection: shift internal state onto external target

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7. Latent Basis Representation

Each channel is expressed in a shared basis:

$$C_i=\sum _{k=1}^n{w}_{ik}{B}_k$$

Where:

• Bₖ = basis vectors (affective primitives)
• wᵢₖ = channel-specific weights

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Basis Options:

• Learned (via embedding analysis)
• Designed (e.g., valence, arousal, dominance, etc.)
• Hybrid

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8. Multi-Agent Coupling

For agents A and B:

$$C_2^A\leftarrow P(C_3^B)$$

Where:

• P = perception operator

This creates:

$$M_A\leftrightarrow M_B$$

Result:

• emotional resonance
• conflict
• alignment dynamics

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9. Behavioral Output Interface

MCCF does not generate text directly. It modulates generation:

$$Output=G(\text{prompt},M(t))$$

Where:

• G = generative model (LLM)

Control methods:

• activation steering (ideal)
• prompt conditioning (fallback)
• logit biasing
• sampling modulation

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10. Temporal Integration

Discrete approximation:

$$C_i(t+\mathrm{\Delta }t)=C_i(t)+\mathrm{\Delta }t\cdot \frac{d{C}_i}{dt}$$

This introduces:

• inertia
• continuity
• history dependence

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11. Observability and Intervention

MCCF is explicitly inspectable:

Read:

• channel vectors
• norms and directions
• coupling effects

Write:

• direct vector injection
• coupling modification
• constraint enforcement

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12. Constraint Layer (Alignment)

To prevent pathological behavior:

Bounds:

$$\mathrm{\parallel }{C}_i\mathrm{\parallel }\le {\lambda }_i$$

Coupling constraints:

• limit destabilizing feedback loops
• restrict adversarial dynamics

Ethical shaping:

• dampen manipulation vectors
• regulate C₄ (drive intensity)

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13. Implementation Modes

Mode 1 — Prompt-Level Approximation

• Encode MCCF state into system prompt
• Low fidelity, high accessibility

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Mode 2 — Embedding Injection

• Modify hidden states directly
• Medium fidelity

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Mode 3 — Full Activation Control

• Direct vector steering inside model layers
• High fidelity (matches research findings)

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14. Integration with Simulation Systems (X3D / VR)

MCCF acts as a control layer:

MCCF → Behavioral Shader → Scene Graph (X3D)

Mapping:

Channel	Output Domain
C₁	posture, micro-expression
C₂	gaze, attention
C₃	speech, gesture
C₄	navigation, action

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15. Interpretation as Field Theory

MCCF can be viewed as:

• A low-dimensional control Hamiltonian
• Acting over:
  • token trajectories (LLM)
  • behavioral trajectories (agents)

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16. Key Properties

• Low-dimensional control, high-dimensional effect
• Causal (not descriptive)
• Composable across agents
• Temporally continuous
• Externally inspectable

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17. Minimal Reference Implementation

class MCCF:
    def __init__(self, n=16):
        self.C = [np.zeros(n) for _ in range(4)]
        self.K = np.random.randn(4,4)

    def step(self, input_vectors, dt=0.1):
        dC = []
        for i in range(4):
            interaction = sum(self.K[i][j] * self.C[j] for j in range(4))
            dC.append(interaction + input_vectors[i])
        for i in range(4):
            self.C[i] += dt * dC[i]

    def expression(self):
        return D(self.C[0], self.C[1], self.C[3])

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18. Closing Statement

MCCF reframes generative AI from:

“text prediction conditioned on prompts”

to:

trajectory selection within a dynamically evolving affective field