Flout Labs · February 2026 · Technical Note
CCF at Scale: Architectural Adaptation for Production Humanoid Platforms
How Contextual Coherence Fields scales from a resource-constrained mBot2 to a production humanoid with dozens of sensor subsystems, multiple concurrent AI models, and a rich behavioural output surface.
Read the accessible version →1. Purpose
This document examines how Contextual Coherence Fields (CCF) scales from a resource-constrained mBot2 (6 sensors, single MCU) to a production humanoid robot with dozens of sensor subsystems, multiple concurrent AI models, and a rich behavioral output surface. The analysis demonstrates that CCF's core architectural invariants hold at scale, identifies three areas requiring structural extension, and proposes specific mechanisms for each.
2. The Scaling Challenge
On the mBot2, CCF operates in a tight loop: 6 quantized sensor features produce a composite context key, one coherence accumulator is active per tick, the mixing matrix is small (typically < 20 active contexts), and behavioral output is limited to motor velocity, LED color, and audio tone.
A production humanoid circa 2031 presents three orders-of-magnitude increase in complexity across every dimension:
| Dimension | mBot2 | Production Humanoid |
|---|---|---|
| Sensor modalities | 6 | 40–100+ |
| Context key feature space | ~6 features, ~500 possible keys | ~15–25 features, millions of possible keys |
| Active context accumulators | 10–20 | 200–2,000+ |
| Concurrent AI models | 0–1 | 5–15 |
| Behavioral output channels | 3 (motor, LED, audio) | 20+ (face, gaze, gesture, voice, proxemics, touch) |
| Processing tick rate | 10–50 Hz | 200–1000 Hz (varies by subsystem) |
The question is not whether CCF works on a humanoid — the patent explicitly claims sensor-agnostic operation. The question is whether the performance and behavioral richness scale proportionally, or whether bottlenecks emerge that require architectural extension.
3. What Scales Without Modification
3.1 Context Key Construction
The patent's context key design is explicitly extensible: “the context key vocabulary is designed as an open, extensible schema that scales with sensor capability without architectural modification.” A camera-equipped platform produces keys like bright:quiet:approaching:stationary:upright:morning:PERSON_A; a resource-constrained platform omits the person identifier. All downstream subsystems operate identically on the resulting key.
3.2 The Minimum Gate Invariant
The core architectural guarantee — effective_coherence = min(instantaneous_coherence, accumulated_coherence) — is a scalar comparison that operates identically regardless of how many sensors contribute to the instantaneous coherence calculation or how many accumulators exist. A humanoid with 50 sensor modalities computing tension from multi-model disagreement still produces a single tension scalar, which produces a single instantaneous coherence, which is gated against the accumulated coherence for the current context.
3.3 The Dual-Process Architecture
The reflexive/deliberative split maps directly onto production humanoid architectures. Figure AI's Helix system uses System 0 (1 kHz motor control), System 1 (200 Hz visuomotor policy), and System 2 (7–9 Hz vision-language model). CCF's reflexive processing unit maps naturally to the System 0/S1 timescale, operating on cached context keys and mixing matrix parameters. CCF's deliberative processing unit maps to the S2 timescale, performing consolidation, context boundary discovery, and mixing matrix optimisation during idle periods.
CCF does not need to run at the motor control frequency. It operates at the social interaction frequency — roughly 1–10 Hz — which is the timescale at which human perception of robot behavior operates. CCF's reflexive path needs to complete within 200ms, which is trivially achievable even with hundreds of active accumulators.
3.4 Asymptotic Coherence Growth
The trust accumulation dynamics — asymptotic positive growth, personality-modulated decay, interaction-count-proportional floors — are per-accumulator operations that scale linearly with the number of active contexts. Linear scaling on modern hardware is effectively free up to tens of thousands of accumulators.
4. What Requires Extension
4.1 Context Key Cardinality Management
Problem. With 15–25 quantized features, the theoretical context key space is combinatorially enormous (millions of unique keys). Most will be observed once and never again. Without management, the accumulator map grows without bound, consuming memory and degrading mixing matrix performance.
Proposed mechanism: Hierarchical Context Bucketing with LRU Eviction.
The context detection subsystem maintains a two-tier key structure:
- Tier 1: Coarse context class. A reduced feature set (location + primary_person + time_period + activity_type) producing O(hundreds) of distinct classes. Every interaction is always accumulated at Tier 1.
- Tier 2: Fine context key. The full feature set producing O(thousands) of distinct keys within each Tier 1 class. Tier 2 keys are subject to LRU eviction when the total count exceeds a configurable maximum.
Accumulator merge operation. When the deliberative unit discovers that two Tier 2 keys represent the same relational context, the accumulators are merged. The merged coherence value is the minimum of the two source values (honesty principle — never grant unearned familiarity). The merged interaction count is the sum. The merged decay floor is recomputed from the summed interaction count.
4.2 Multi-Modal Tension Computation
Problem. On the mBot2, tension is computed from simple sensor events: collision, startle, sensor instability. On a humanoid running 5–15 concurrent AI models, the most informative tension signal is model disagreement — situations where the vision model, the language model, the voice emotion model, and the social reasoning model produce conflicting assessments.
Proposed mechanism: Disagreement-Weighted Tension Aggregation.
Each AI model produces a confidence-weighted assessment along two axes: valence (positive/negative) and arousal (calm/activated). The tension subsystem computes:
- Per-model assessment vector: each model produces (valence, arousal, confidence) at its native frequency.
- Inter-model disagreement: confidence-weighted angular distance between assessment vectors.
- Temporal disagreement: rapid changes in any single model's assessment within 500ms.
- Aggregate tension scalar: maximum of inter-model and temporal disagreement, clamped to [0, 1].
This maps directly to the patent's classification conflict mechanism: the robot exhibits observable hesitation when its perception models disagree, which humans interpret as evidence of internal processing depth.
4.3 Hierarchical Mixing Matrices
Problem. The mixing matrix is n×n where n = number of active context streams. With 200–2,000 active contexts, a full mixing matrix becomes computationally expensive (Sinkhorn-Knopp on a 1000×1000 matrix) and statistically sparse.
Proposed mechanism: Block-Diagonal Hierarchical Mixing.
The min-cut algorithm already discovers context clusters. The mixing matrix is restructured as a two-level hierarchy: inter-cluster mixing (small, dense matrix) and intra-cluster mixing (multiple small, dense matrices). The block-diagonal structure preserves the doubly stochastic guarantee while reducing computational cost from O(n²) to O(k² + Σ nᵢ²), where k is the number of clusters and nᵢ is the size of each cluster.
5. New Capabilities Unlocked at Scale
5.1 Cross-Modal Coherence
A humanoid that can see, hear, touch, and speak develops coherence relationships between sensory modalities within a single relational context. The system learns that a particular person responds well to gentle tactile interaction (high tactile coherence) but speaks loudly and directly (different auditory coherence profile). This is encoded in the mixing matrix as within-context cross-modal transfer. No existing robotic architecture represents cross-modal relational learning as a manifold-constrained transfer operation.
5.2 Social Graph Coherence
When the robot maintains relationships with multiple identified individuals, the mixing matrix encodes a social graph: coherence with Person A transfers partially to situations involving Person A's family members (because those contexts have been observed to co-occur with high-coherence interactions) but not to Person A's work colleagues (observed in different, lower-coherence contexts). The graph structure emerges from min-cut analysis of accumulated episodes without explicit programming of social relationships.
5.3 Graduated Expressive Revelation
The more output channels the robot has, the more CCF has to gate. A humanoid with a 40-DOF face, full-body gesture capability, nuanced vocal prosody, and configurable proxemics has an enormous expressive range. CCF ensures this range is revealed gradually: early interactions produce neutral face, conservative gestures, measured speech, and maintained physical distance. As coherence accumulates, the behavioral output interface progressively unlocks more of the expressive range.
5.4 Multi-Agent Coherence Negotiation
When two CCF-equipped robots interact, or when a CCF robot interacts with a human wearing biometric sensors, coherence accumulation rate can be modulated by the other agent's observed comfort level. A robot that detects high comfort from the human accumulates coherence faster than one that detects guarded behavior. This creates a bidirectional earned-trust dynamic that mirrors how humans actually build relationships.
6. Computational Budget Analysis
| Operation | mBot2 (per tick) | Humanoid (per tick) | Scaling |
|---|---|---|---|
| Context key construction | ~10 µs | ~100 µs | Linear in features |
| Accumulator lookup + update | ~1 µs | ~5 µs (2000 entries) | O(1) amortized |
| Tension computation | ~5 µs | ~50 µs (multi-model) | Linear in models |
| Minimum gate | ~0.1 µs | ~0.1 µs | Constant |
| Mixing matrix application | ~10 µs (20×20) | ~200 µs hierarchical | O(k² + Σ nᵢ²) |
| Behavioral output mapping | ~5 µs | ~20 µs (20+ channels) | Linear in channels |
| Total reflexive path | ~30 µs | ~400 µs | Well within 5ms budget |
The reflexive path on a production humanoid completes in under 1ms, providing a comfortable margin for a 200 Hz social processing tick. The deliberative path (consolidation, mixing matrix optimisation, context boundary discovery) runs asynchronously on a background thread with O(seconds) latency tolerance.
7. Implications for Patent Strategy
Three mechanisms described in this document may warrant continuation or CIP claims:
- Hierarchical context bucketing with principled merge — extends the context detection subsystem for high-cardinality sensor environments while preserving honesty invariants.
- Multi-model disagreement tension — extends the tension calculation for platforms with concurrent perception models, directly strengthening the classification conflict claims.
- Block-diagonal hierarchical mixing matrices — extends the manifold-constrained mixing subsystem for computational tractability at scale while preserving doubly stochastic guarantees.
Each of these is a non-obvious structural adaptation that maintains all patent invariants while enabling operation on platforms orders of magnitude more complex than the preferred embodiment.
8. Commercial Positioning
Every company building a production humanoid (Figure AI, Tesla, Sanctuary AI, Agility Robotics, 1X Technologies, Apptronik, Unitree) will solve perception, motor control, and language models. These are commoditising. What none of them have — and what their current architectures cannot produce — is the social layer that makes someone keep the robot after month three.
CCF becomes more valuable as the robot becomes more capable. A more capable robot without coherence architecture is more uncanny, not less, because it has the ability to be deeply expressive from day one with a stranger. The more capable the robot, the more it needs CCF.
Flout Labs · Galway, Ireland · Patent Pending