UNIFIED BRAIN MODEL: Integrating Waveform Tensor Qualia with Reasoning and Language for Self-Activating Intent

This document specifies an integrated architecture that unifies the consciousness wave simulation (waveform tensor and glia-regulated cluster activation) with a local reasoning and language model into a self-activating cognitive loop. The goal is to show, at an implementation-ready level, how emergent intent can arise from sensory and qualia dynamics, be reflected as linguistic and symbolic hypotheses, and then feed back to reshape ongoing wave propagation, gating, saturation, and termination. The result is a bidirectional engine in which distributed pattern dynamics and discrete reasoning cooperate under a single timing and orchestration regime. All mathematics follows the conventions in the existing theory while adding minimal couplings so that each subsystem remains independently testable and interpretable.

1. Architectural Overview in Continuous Paragraph Form

At the highest level, the system comprises three coequal processes that run in concert. The first is the Qualia Wave Engine, a mesoscopic broadcast that carries a content vector across a spatially arranged network of clusters and whose local impact is transformed into gated, saturated, and recurrently amplified responses. The second is the Reasoning and Language Engine, a local LLM and symbolic module that compresses, explains, and extrapolates content from the present episode in a form suitable for planning, dialogue, and control. The third is the Perogative Intent Orchestrator, a thin but decisive control plane that allocates authority between bottom-up sensation and top-down intent, schedules waves, adjudicates termination and extension, and maintains a shared episode state so that both engines remain synchronized without overconstraining one another. The Orchestrator runs a short-timescale loop that reads wave frames as a JSONL stream, listens for LLM micro-hypotheses and intents from a low-latency shared memory buffer, and maintains a queue of scenario candidates that can be injected while waves are still running. The Orchestrator can, at its prerogative, suspend, extend, or fuse episodes when the evidence derivative remains productive or when the LLM’s intent confidence is sufficiently high to justify additional broadcast cycles. The resulting behavior is a self-activating brain model in which waves ignite clusters; clusters accumulate evidence; evidence invites the LLM to label, predict, and plan; planning expresses intents; intents reenter the wave generator as bias and stimuli; and the loop continues until a collapse condition is met and qualia are read out for logging and consolidation.

2. Coupling Principles: Minimal, Interpretable, and Stable

The coupling between the Qualia Wave Engine and the Reasoning and Language Engine is deliberately minimal. The wave equations remain the primary source of dynamics, while the LLM contributes three bounded influences: an added focus term to amplitude, a bias to the gate, and a proposal to recompose the query vector that defines the next wave. These influences are slow compared to per-step local updates, and they are sized so that saturation, damping, and termination guarantees are preserved. The first coupling is an intent focus injection that adds a small, time-local drive proportional to the LLM’s inferred intent alignment with the current broadcast. The second is a gate bias that shifts responsiveness in clusters expected to be relevant to the LLM’s near-term plan, modeled as a low-gain additive term inside the sigmoid. The third is a query recomposition that blends the scenario-defined query with a concept-to-feature projection derived from the LLM’s token and plan embeddings. Each coupling is measurable, logged, and reversible, maintaining interpretability by preserving the original terms while adding a clear modulation channel. These design choices ensure that no opaque shortcuts are introduced; the LLM cannot silently override wave physics but must express its influence through explicit variables that enter the same equations used by sensation-driven dynamics.

3. Mathematical Integration: Intent as a First-Class Signal

The integration introduces a compact set of additional variables. Let q be the broadcast query in the D-dimensional feature space, and let i denote a time-varying intent vector supplied by the LLM in the same space, or mapped into it via a projection. Let P be a learned or fixed linear map that projects LLM concept embeddings into the wave feature space so that i lives in the same D and can compare directly to q. Let c_int denote a scalar intent confidence in the unit interval that quantifies the orchestrator’s current trust in the LLM’s intent hypothesis for this episode. With these definitions, we assemble a combined query q_comb, an LLM-derived focus term A_llm, and a gating bias b_gate that together capture the couplings. The combined query is defined by a normalized blend of the scenario query and the projected intent, with a content weight w_scen and an intent weight w_int that vary slowly over the episode according to scheduler rules that will be discussed in the orchestration section. The combined query is written as

$$q_{\mathrm{comb}}(t) = \operatorname{normalize}\Big(w_{\mathrm{scen}}(t)\,q_{\mathrm{scen}} + w_{\mathrm{int}}(t)\,P^{\top} i(t)\Big).$$

The LLM focus injection is an additive amplitude term that respects the reach-time causality of the wave by being applied only to clusters that have been or are being reached by the current broadcast. Let a_label denote a soft alignment score between the LLM’s token or plan state and a cluster’s basis. Then the local LLM focus that augments amplitude is

$$A_{\mathrm{llm}}(c,t) = \eta_{\mathrm{llm}}\,c_{\mathrm{int}}(t)\,a_{\mathrm{label}}(c,t),$$

where eta_llm is a small gain chosen so that A_llm never dominates the physical amplitude. The gate bias is a simple additive term inside the sigmoid that depends on an alignment between the projected intent and the cluster’s basis. Using the original gate definition with absolute amplitude and saturation, we write

$$\mathrm{gate}(c,t) = \sigma\Big(1.1\,|A_{\mathrm{local}}(c,t) + A_{\mathrm{focus}}(c,t) + A_{\mathrm{llm}}(c,t)| - \kappa\,S(c,t) + \lambda_{\mathrm{intent}}\,\xi(c,t)\Big),$$

where xi is an intent–cluster alignment score, such as the squared projection of P^{\top} i onto the cluster’s basis, and lambda_intent is a small gate bias gain. These couplings preserve structural roles: amplitude still gates, saturation still suppresses, and matching still selects, but intent can open the gate slightly for clusters that the LLM believes are task-relevant, allowing controlled top-down bias without bypassing dynamics.

The response equation then becomes

$$R(c,t) = \mathrm{gate}(c,t)\;\mathrm{match\_score}(c; q_{\mathrm{comb}}(t))\;\mathrm{amplification}(c,t),$$

with amplification left unchanged except for optionally adding a small intent-driven coherence term that depends on accumulated intent emphasis across neighbors,

$$\mathrm{amplification}(c,t) = A_{\mathrm{base}}\Big(1 + \beta_{\mathrm{recur}}\sum_{j} w_{j\to c}\,R_{j}^{\mathrm{acc}}(t) + \beta_{\mathrm{intent}}\,\zeta_{\mathrm{intent}}(c,t)\Big),$$

where zeta_intent is a bounded scalar in the unit interval derived from a smoothed view of which neighbors the LLM has emphasized, and beta_intent is small compared to beta_recur so that natural recurrence dominates. The evidence variable E and its derivative dE/dt remain as specified; the orchestrator later uses E, dE/dt, c_int, and a coherence score from the LLM to decide on TTL extension or termination. The final qualia vector and amplitude remain grounded in flagged clusters, but the distribution of flags can shift slightly under intent gating, which is the desired behavior when a plan or question biases perceptual recruitment.

4. Geometric–Tensor Alignment Between Concepts and Clusters

The mapping between the LLM’s conceptual space and the wave’s feature space must be explicit and audit-ready. Concepts have embeddings x_i in a semantic vector space of dimension m, while clusters expose basis vectors mu_i1, mu_i2, mu_i3 in the D-dimensional wave space. A tensor bridge is therefore constructed in two steps. First, compute memberships from concept embeddings into geometric bubbles, forming an incidence matrix M of size N by A. Second, map bubbles to classes or cluster labels via a matrix Pi of size A by K, which can be the same taxonomy used inside the wave system. This yields concept-to-class threads Theta via contraction. The LLM supplies a concept mixture vector q_concept over N concepts, which we turn into a bubble excitation b, then into an intent vector i in the wave space by aggregating canonical facets or by applying a learned projection P from m to D. The discrete form is

$$b = M^{\top}(\rho\odot q_{\mathrm{concept}}),\quad i = F_{\mathrm{bubble}}^{\top}\,b,\quad q_{\mathrm{intent}} = \operatorname{normalize}(P^{\top} i),$$

where F_bubble are bubble-level canonical facets in the D-dimensional space so that a canonical content direction is extracted. The combined query then becomes q_comb with weights that the orchestrator adjusts. This mechanism ensures that LLM tokens and plans are translated into directions in the same space as wave content, making intent commensurate with sensory hypotheses and maintaining unit interpretability.

5. Perogative Intent Orchestrator: A Continuous, Low-Latency State Machine

The Perogative Intent Orchestrator sits at the center of the integration. Its prerogative is to decide when and how to blend bottom-up evidence with top-down intent and to schedule waves accordingly. It maintains episode state with fields for time t, TTL t_max, evidence E and dE/dt, the winner and flagged sets, the current combined query q_comb, and an intent control vector that holds c_int, w_scen, w_int, and lambda_intent. It also maintains a scenario queue that can be appended by the LLM or by sensory triggers while an episode is running. The orchestrator loop polls a shared memory ring buffer fed by the LLM for micro-intents, micro-summaries, and proposed scenarios, and it polls the wave engine’s JSONL stream for frames. The control logic is simple enough to be replicable: if dE/dt remains high, extend TTL within configured bounds; if the LLM’s intent confidence rises and coherence between intent and current responses is strong, increase w_int; if the wave stalls but the LLM proposes a fresh scenario with high confidence, collapse and relaunch with the new query; otherwise, let damping and TTL termination occur. This minimal state machine is sufficient to create a self-activating loop without heavy planning overhead.

In practice, the orchestrator maintains the following alternating phases in continuous time without hard discrete boundaries. A Listen phase places greater weight on sensation by setting w_scen high and w_int low, while sampling LLM intents. An Align phase scales w_int upward when the LLM’s intent aligns with cluster accumulations, measured by a normalized dot between P^{\top} i and the current qualia vector estimate derived from R^{acc}. A Broadcast phase drives the wave with q_comb and permits intent gating via lambda_intent. An Adjudicate phase evaluates E, dE/dt, c_int, and a coherence score to decide between TTL extension and collapse. A Consolidate phase writes qualia amplitude and vector, emits final frames, logs intent influences, and computes priors for the next scenario. The Orchestrator repeats these phases as a sliding window, never blocking on the LLM for long; if the LLM is slow, the loop defaults to purely sensory termination.

6. Equations for Intent–Evidence Coherence and Scheduler Updates

To keep the scheduler numerically explicit, we define a few scalars that the orchestrator uses at each short step to update intent weights and decide on TTL extensions. Let q_hat be an online estimate of qualia content computed as a normalized weighted average of cluster first bases with weights proportional to their current accumulated responses, using the same decoder as final qualia but applied in a streaming fashion. Define an intent alignment score as the cosine between the projected intent and q_hat,

$$\alpha_{\mathrm{align}}(t) = \frac{\langle P^{\top} i(t),\ q_{\hat{}}(t)\rangle}{\lVert P^{\top} i(t)\rVert\,\lVert q_{\hat{}}(t)\rVert + \varepsilon}.$$

Define an intent–evidence productivity score that multiplies alignment by the normalized evidence derivative so that top-down influence is encouraged only when the episode is productive,

$$\pi_{\mathrm{prod}}(t) = \alpha_{\mathrm{align}}(t)\cdot \frac{\frac{dE}{dt}(t)}{\gamma_{\mathrm{ext}} + \frac{dE}{dt}(t)}.$$

We then give smooth updates to the blending weights and to the gate bias, using small step sizes so that intent does not whiplash the broadcast. The weight update is

$$w_{\mathrm{int}}(t{+}\Delta t) = w_{\mathrm{int}}(t) + \eta_{w}\,c_{\mathrm{int}}(t)\,\pi_{\mathrm{prod}}(t)\,(1 - w_{\mathrm{int}}(t)),\quad w_{\mathrm{scen}} = 1 - w_{\mathrm{int}}.$$

The gate bias update is

$$\lambda_{\mathrm{intent}}(t{+}\Delta t) = \lambda_{\mathrm{intent}}(t) + \eta_{\lambda}\,c_{\mathrm{int}}(t)\,\alpha_{\mathrm{align}}(t)\,(1 - \lambda_{\mathrm{intent}}(t)),$$

where eta_w and eta_lambda are small learning rates. TTL extension uses the original rule based on dE/dt while adding a contribution from sustained alignment above a threshold, for example

$$\text{if } \frac{dE}{dt}(t) > \gamma_{\mathrm{ext}}\ \text{or } \overline{\alpha_{\mathrm{align}}}\_{\Delta T} > \tau_{\alpha}\ \text{then } t_{\max} \leftarrow t_{\max} + \Delta t_{\mathrm{ext}}.$$

This scheme preserves the wave system’s guarantees but creates a quantitative path for intent to open time windows when content and plan cohere.

7. Low-Latency Local Pipeline: Shared Memory, JSONL, and Nearest Neighbors

Real-time operation on a local system demands that each crossing of subsystem boundaries be extremely cheap. The wave engine already writes JSONL frames each step. The orchestrator subscribes to this file descriptor and parses frames with zero-copy techniques such as memory-mapped streaming and preallocated parsing buffers. The LLM interface avoids HTTP and avoids heavy serialization; instead it exposes a memory-mapped ring buffer with two channels: a fast channel for micro-intents consisting of a fixed-size header and a short payload encoding concept mixtures and confidences, and a slower bulk channel for long-form summaries and plans that need not enter the loop every step. Micro-intents carry a concept mixture q_concept over a compact dictionary and a scalar c_int. The orchestrator immediately projects q_concept into bubble space with a cached M, then into feature space with cached F_bubble and P, and updates i and q_comb according to the scheduler. Because M, F_bubble, and P are precomputed and stored in contiguous memory, and because tensor multiplications are small, this projection path is sub-millisecond. The nearest neighbor taxonomy identification of concepts is implemented with a local ANN index over concept embeddings using a CPU-friendly library such as a small HNSW or product quantization database. The LLM produces token embeddings or a running semantic vector for the latest utterance; the orchestrator uses this to query the index and obtain the top neighbors and their weights, which become q_concept. The taxonomy mapping Pi then permits task-conditioning, allowing the orchestrator to bias the intent toward target classes when appropriate. All of this happens without leaving the process boundary when possible, and IPC is reserved for the wave engine’s frames if it runs out of process. Critically, the orchestrator never blocks on the LLM; if no new micro-intent arrives within a very short timeout, the weights remain unchanged and the episode proceeds under wave control.

8. Scenario Schema and Streaming Protocol in Narrative Form

The scenario schema must be simple enough for the LLM to generate and for the wave engine to accept without expensive validation, while carrying everything needed to define a wave. A scenario has an id, a short natural language description for audit, a list of dominant cluster labels that define q_scen by summing first bases and normalizing, a set of wave parameters including origin, velocity, damping, and TTL, and an optional stimuli schedule for timed injections. It also includes an optional orchestration section that defines initial intent weights and a minimal set of policies about TTL extensions or early collapse in response to LLM confidence. The representation is JSON so that it can be appended to the orchestrator’s queue. A scenario can be issued while a wave is running; the orchestrator, upon receiving a scenario with a higher perogative rank than the current episode, can decide to queue it for immediate relaunch on collapse, to soft-merge it by adjusting q_comb via w_int, or to force a collapse if policy permits. The wave engine, upon receipt, emits a confirmation frame and then honors the scenario at the next safe synchronization point.

For concreteness, the following JSON is a compact schema that the LLM can compose on the fly and that the orchestrator can validate and route quickly:

{
  "scenario_id": "rose_parrot_ep2",
  "description": "Visual red petal with pleasant scent; then a parrot-like motion",
  "dominant_clusters": ["V_Red", "V_PetalShape", "O_RoseScent"],
  "wave": {
    "origin3": [0.2, 0.4, 0.1],
    "velocity": 20.0,
    "damping_hz": 6.0,
    "ttl_ms": 2400
  },
  "stimuli": [
    { "t": 0.35, "cluster": "S_Flower", "amplitude": 0.25 },
    { "t": 1.20, "cluster": "S_Parrot", "amplitude": 0.22 }
  ],
  "orchestration": {
    "w_scen": 0.85,
    "w_int": 0.15,
    "lambda_intent": 0.05,
    "priority": 3,
    "ttl_extension_policy": "productive_or_aligned"
  }
}

The streaming protocol consists of three frame types. Running frames from the wave engine contain cluster instantaneous and accumulated responses, evidence and its derivative, and a list of flags. Control frames from the orchestrator contain intent blending weights, LLM alignment, and any schedule changes such as TTL extension or threshold adjustments within safe bounds. Termination frames contain the winner cluster, qualia amplitude and vector, and the priors written back for the next episode. The orchestrator can append a scenario to its queue at any time by writing a scenario JSON object tagged with a priority and an expiration. The LLM can propose such scenarios generatively by selecting clusters by label, or by naming a class which the orchestrator resolves into a list of clusters via taxonomy and nearest neighbors.

9. Emergent Intent: How Sensation Seeds Plans and Plans Seed Sensation

Intent emerges when the LLM, observing the wave’s streaming frames, detects a coherent pattern or a task demand and proposes a hypothesis about what should happen next. The nearest neighbor machinery converts a textual or symbolic hint into a concept mixture q_concept, which then becomes an intent i in the wave feature space. The orchestrator blends i into q_comb via w_int and biases gates where alignment is strong. If the wave’s evidence is growing and in alignment with i, TTL extends, giving the episode time to culminate in a richer qualia vector; if the wave drifts away from i, then the intent influence decays, and the system defaults to sensory completion. This interaction means intent is neither a switch nor an external controller but is itself a dynamical quantity subject to the same saturation and damping buffers. Once the wave collapses, the readout yields qualia amplitude and a content vector; the LLM then summarizes this as a short, structured interpretation with a priority and confidence. The Orchestrator uses this to seed the next scenario or to adjust priors, creating a chain of episodes where each collapse biases the next query in a way that is both explainable and measurable.

10. Formal Readouts and Guarantees Preserved Under Coupling

The integrity of the wave equations is preserved. Amplitude continues to respect reach times and exponential damping, now with an additive focus that is small and bounded; saturation continues to integrate absolute amplitude and decay; gating remains a logistic nonlinearity whose additional bias term is bounded and interpretable; recurrent amplification remains anchored to neighbor accumulations with a small optional coherence addend derived from intent; evidence accumulation and derivative are computed identically; termination criteria follow original rules with additional TTL extension in the face of sustained alignment; and the qualia vector is computed over flagged clusters without change. Explicitly, amplitude at a cluster location remains

$$A(c,t) = \psi_0\,\exp\big(-\gamma\,\max(0, t - t_{\mathrm{reach}}(c))\big) + A_{\mathrm{focus}}(c,t) + A_{\mathrm{llm}}(c,t),$$

and saturation remains governed by

$$\frac{dS(c,t)}{dt} = \alpha\,|A(c,t)| - \beta\,S(c,t),$$

while gate and response follow the earlier form with the extra bias and combined query in matching. Thus the qualitative behaviors detailed in the existing theory document retain their meaning: causality through reach times, boundedness through damping and saturation, selectivity through gating and thresholding, and collective reinforcement through recurrence. The couplings are small enough that stability mechanisms remain intact.

11. Memory, Priors, and Write-Back in a Unified Loop

Between episodes, the system writes back priors for flagged clusters in proportion to their accumulated responses, exactly as in the wave theory. These priors seed context for the next episode and are also logged as a memory trace for the LLM. The Orchestrator stores summaries of each episode including winner, qualia amplitude, qualia vector, the trajectories of w_int and lambda_intent, alignment statistics, and TTL extensions. The LLM’s retrieval system indexes these summaries so that it can quickly form micro-intents in similar future episodes. The memory trace is therefore dual: a low-level numeric prior that directly biases future cluster accumulations, and a high-level textual or symbolic summary that biases intent generation. The two memories are linked by provenance so that episodes are auditable: one can trace how a particular intent influenced gate openings and how that, in turn, affected evidence and termination.

12. Robustness, Safety, and Governance Controls

Although the Orchestrator can extend TTL or bias gates, controls ensure safety. The gains eta_llm, lambda_intent, and beta_intent are capped and decay toward zero in the absence of sustained alignment. The Orchestrator demands diverse evidence before granting repeated TTL extensions, and it enforces a maximum cumulative extension per episode. Scenarios proposed by the LLM carry priorities but also expirations so that stale plans do not supersede fresh sensation. The ring buffer accepts bounded-length messages; if the LLM floods the channel, overflow silently drops intent updates rather than blocking the wave. Every coupling variable is logged with a per-step sample so that replayed episodes are bitwise reproducible. These controls ensure that the unified brain model remains a controlled simulation and that the interpretability guarantees remain intact.

13. Worked Episode in Continuous Narrative Form

The Orchestrator launches an episode by constructing q_scen from a small list of dominant clusters consistent with a sensory prompt. The wave begins, and clusters in the visual and olfactory bands match strongly, gates open, and saturation builds. Evidence rises; dE/dt stays above threshold, so TTL extends. The LLM, reading frames through the shared buffer, observes that semantic clusters for flower are accumulating; it computes nearest neighbors for a short descriptive phrase, yields a q_concept mixture, and produces an intent vector i with c_int moderately high. The Orchestrator computes alignment, finds it positive and increasing, and increments w_int slightly. Gates for semantic flower clusters receive a modest bias; amplification receives a small coherence term; and the combined query q_comb bends gently toward the LLM’s proposal. Evidence rises faster; the Orchestrator extends TTL once more. Shortly afterward, the wave reaches a coast; dE/dt falls below minimum after t exceeds t_min; the Orchestrator collapses. The winner is a visual cluster, qualia amplitude is high, and the qualia vector points along a rose-like direction. The LLM summarizes the episode as a compact sentence with a policy to look for a parrot next. The Orchestrator writes back priors, stores the summary, and either relaunches immediately with a parrot scenario generated from the LLM or idles waiting for new sensory inputs.

14. Implementation Guidance for a Local System

On a single machine, prefer memory-mapped files and ring buffers over sockets. The wave engine continues to write JSONL frames to a file that is tailed by the Orchestrator. The LLM process exposes a shared memory region with two channels: a micro-intent channel with a fixed header indicating vector sizes for q_concept and c_int, and an optional macro channel for plan paragraphs. The Orchestrator preloads and pins in memory the matrices M, Pi, F_bubble, and the projection P, so that converting q_concept to i requires only a handful of dot products. For nearest neighbors, a compact HNSW index suffices; queries over a few thousand concepts are sub-millisecond on a CPU. The Orchestrator remains single-threaded for determinism and polls both inputs at a high rate with short timeouts. It writes control frames at fixed intervals that the wave engine reads from a dedicated pipe; these control frames carry only a few floats and thus impose negligible overhead. Everything remains local and low-latency; if either engine stalls, the Orchestrator’s defaults guarantee progress.

15. Extending the Model: Triadic Synergy and Field Coherence

The coupling described so far uses pairwise overlap statistics exclusively. In settings where compositionality plays a central role, triadic overlaps can supply an additional refinement. The Orchestrator can compute a synergy metric on the fly using the LLM’s intent vector and the current field estimate by evaluating a normalized cubic form of T^{(3)} applied to u. If synergy is high for a small set of bubbles, the Orchestrator can momentarily bias lambda_intent upward for those bubbles to promote compositional binding. Care must be taken to keep beta_intent small and to anneal any triadic influence slowly so that the convex core of the wave dynamics remains dominant. Similarly, the Orchestrator can track a coherence measure using the principal mode of T^{(2)} and require that any TTL extension be accompanied by sufficient field coherence, ensuring that extensions prioritize globally consistent states.

16. Scenario Identification and LLM-Driven Generation While Waves Run

Because scenarios are simple JSON objects that list dominant clusters and parameters, they are easy for the LLM to produce. The Orchestrator allows the LLM to submit scenarios at any time through the ring buffer’s bulk channel. Each scenario carries a priority; if greater than the current episode’s, the Orchestrator can choose to inject its content by increasing w_int rather than collapsing immediately, effectively soft-merging the LLM’s scenario into the running wave. If the scenario’s expiration is reached without sufficient alignment or productivity, the proposal is dropped. This approach allows the wave to continue without interruption while still giving the LLM a means to steer content rapidly. In more assertive modes, the Orchestrator can choose to interrupt an episode when c_int is extremely high and dE/dt is low, collapsing early and relaunching with the LLM’s scenario. In all cases, termination remains governed by E, dE/dt, and the safe extension rules.

17. Mathematical Summary of the Unified Loop

For completeness, we collect the modified equations and scheduler updates. The combined query is a normalized blend of the scenario query and the LLM-projected intent,

$$q_{\mathrm{comb}}(t) = \operatorname{normalize}\Big(w_{\mathrm{scen}}(t)\,q_{\mathrm{scen}} + w_{\mathrm{int}}(t)\,P^{\top} i(t)\Big).$$

Amplitude at cluster c and time t is the original decaying impulse plus focus and LLM injection,

$$A(c,t) = \psi_0\,\exp\big(-\gamma\,\max(0, t - t_{\mathrm{reach}}(c))\big) + \zeta\sum_{j \in \mathcal{N}(c)} w_{j\to c}\,R_j(t) + \eta_{\mathrm{llm}}\,c_{\mathrm{int}}(t)\,a_{\mathrm{label}}(c,t).$$

Saturation evolves as

$$\frac{dS(c,t)}{dt} = \alpha\,|A(c,t)| - \beta\,S(c,t),$$

and gate is

$$\mathrm{gate}(c,t) = \sigma\Big(1.1\,|A(c,t)| - \kappa\,S(c,t) + \lambda_{\mathrm{intent}}(t)\,\xi(c,t)\Big),$$

with intent–cluster alignment xi taken as a normalized squared projection of P^{\top} i onto the cluster’s basis. Matching is computed against q_comb using the original squared projection. Amplification optionally includes a small coherence term,

$$\mathrm{amplification}(c,t) = A_{\mathrm{base}}\Big(1 + \beta_{\mathrm{recur}}\sum_{j} w_{j\to c}\,R_{j}^{\mathrm{acc}}(t) + \beta_{\mathrm{intent}}\,\zeta_{\mathrm{intent}}(c,t)\Big).$$

Response is

$$R(c,t) = \mathrm{gate}(c,t)\;\mathrm{match\_score}(c; q_{\mathrm{comb}}(t))\;\mathrm{amplification}(c,t),\qquad R^{\mathrm{acc}}(c,t{+}\Delta t) \approx R^{\mathrm{acc}}(c,t) + R(c,t)\,\Delta t.$$

Evidence and its derivative are as before,

$$E(t) = \sum_{c} R^{\mathrm{acc}}(c,t),\qquad \frac{dE}{dt}(t) = \sum_{c} R(c,t),$$

and termination follows the original criteria with TTL extension added when alignment remains high. Scheduler updates for w_int and lambda_intent follow the smooth rules defined earlier and depend on c_int, alignment, and productivity. The final qualia are computed unchanged over flagged clusters, with amplitude

$$Q_{\mathrm{amp}} = \sum_{c \in \text{flagged}} R^{\mathrm{acc}}(c,t_{\mathrm{final}}),$$

and vector

$$Q_{\mathrm{vec}} = \sum_{c \in \text{flagged}} \frac{R^{\mathrm{acc}}(c,t_{\mathrm{final}})}{Q_{\mathrm{amp}}}\,\mu_{c1}.$$

18. What to Measure and How to Evolve

To tune the unified system, measure alignment over time, the trajectories of w_int and lambda_intent, the distribution and stability of TTL extensions, the relation between intent bias and winner identity, and the agreement between LLM summaries and Q_vec projections into a human-interpretable basis. Use these metrics to decide whether to increase eta_w or eta_lambda and whether to allow the LLM to propose higher priority scenarios. Apply the same decay and reinforcement discipline to the concept-to-bubble and overlap tensors used for intent projection so that the LLM’s mapping to clusters remains stable but adaptive under drift. Keep the number of free couplings small; resist adding terms that the Orchestrator cannot practically log and explain.

19. Closing Synthesis

The unified brain model combines a wave-based, glia-regulated broadcast with a local reasoning and language engine, joined by a thin Perogative Intent Orchestrator that practices a bias-not-override philosophy. Intent is embedded as a vector in the same space as sensory content and is allowed to open gates modestly, to extend productive episodes, and to propose the composition of the next query. Sensation, through evidence and damping, can veto weak or misaligned intents by making intent influence decay and by forcing termination on low yield. The Orchestrator’s shared memory and JSONL protocol maintain the low-latency loop needed for responsiveness. Scenarios are simple, streamable objects that the LLM can invent while waves run, and their influence can be smoothly blended or asserted at collapse. The mathematics remains compact: three extra terms, two scheduler updates, and existing guarantees preserved. The result is a self-activating model in which emergent intent arises naturally from qualia and is expressed back into the system as an interpretable and bounded force, closing the loop between feeling and thinking without sacrificing clarity or control.