**I missed the organizing principle in my recommendation: the self's wellness must determine what gets wanted.** A toy-look is an observable test, not an endowed purpose. My revised recommendation is to complete the learned connection between anticipated perception, the self's current condition, and action. Keeping a target in view should emerge when that serves anticipated wellness; disengaging should emerge when something else serves it better.

I read all pages of the supplied [2018 presentation](<C:/Users/bhart/Downloads/MML Project (2018 presentation).pdf>) and inspected their visual layouts. Pages 18–21 describe reconstructable perception and turning the head toward an anticipated perception. Pages 29–31 ground pain and pleasure in body/brain condition and sensory modulation. UEMA §7 likewise defines wellness as a multidimensional region of perception space ([specification](UEMA/manyMindsWIP/UEMA_Spec_v3_5.txt#L152)). These are the design principles I am evaluating against. A learned representation of expected wellness is a plausible organizing mechanism; the present implementation only partly realizes it.

**What the code actually supports.** Reviewed source revision: `e0bb622cf`. The trainer command and [saved arguments](intention_research_2026-09-09/wellness_reconsideration/run_args.frozen.json) agree that this run uses baseline wellness, curiosity for goal valuation, and body-schema updates. No trainer or runtime code was changed.

| Quoted explanation | Verified interpretation |
|---|---|
| Curiosity affects goal attraction, not the grounded wellness components. | Correct for this configuration. It scales the value stored with a remembered moment using its prediction-error band. The other modes change loss routing; they do not introduce a new bodily curiosity component. |
| Anticipation is absent from wellness. | There is no separately named anticipation component. Accurate prediction already contributes directly to wellness. The gaze teacher is a separate eye-training loss. Absence of a named component does not by itself establish a design gap. |
| Coherence always uses a fallback because the body schema is disabled. | Incorrect for the checked run. Body-schema updates are enabled and the bridge supplies its coherence score. When absent, the final formula compares predictions with their corresponding targets; it does not use the obsolete intermediate comparison between senses. |
| The five-component sum is the trained signal pulling the motor controller. | Incomplete. The grounded values are formulas; a separate network learns to predict them. In the immediate gradient supplied to the controller, only the pose-boundary formula depends on the supplied achieved-pose tensor. Touch comfort and prediction success still affect goal selection, but that is another route. |

Sources: [wellness formulas and final overrides](../Source/Python/lem/model/wellness.py#L433), [controller input](../Source/Python/mimo_bridge/lem_model.py#L1219), [curiosity routing](../Source/Python/mimo_bridge/lem_model.py#L1411), [goal valuation](../Source/Python/mimo_bridge/lem_model.py#L1884), [wellness-prediction training](../Source/Python/lem/training/loss_manager.py#L477). A [small CPU check of the actual evaluator](intention_research_2026-09-09/wellness_reconsideration/check_wellness_routes.json) verified that gradient limitation and the coherence branches. This tests mathematical connectivity, not a trained body's behavior.

The deeper gap is **maintaining wellness under changing conditions**. Goal memories retain their old values; the selector does not reassess those memories against present needs. The action optimizer then approaches the selected sensory snapshot. Its inner objective does not maximize the predicted wellness of alternative futures; predicted wellness is used later to stop extending a plan with a low component. This is a partial route from remembered pleasure to action, with no equivalent complete route for choosing what sustains the present self over time. See [stored values](../Source/Python/mimo_bridge/lem_model.py#L1952), [action objective](../Source/Python/lem/model/intention_generator.py#L238), and [predicted-wellness exit](../Source/Python/mimo_bridge/lem_model.py#L2543).

I would first make the existing forward model and approximate wellness evaluator judge candidate future perceptions from the current self-state, including later consequences and uncertainty. The same basis should guide remembering, looking, moving, and stopping. This requires a learned action-to-consequence-to-wellness connection; attaching another coefficient or switching wellness mode alone does not establish it. The earlier head/eye execution mismatch remains a plumbing problem, but fixing it should preserve the model's evolving intention rather than impose a toy-specific completion rule.

Curiosity and anticipation fit inside that organization. **Curiosity can express the expected value of learning for maintaining wellness**, including the brain's own stimulation needs. Current error magnitude is only a proxy for learnability: an unpredictable signal can remain in the favored band without yielding understanding. Learning progress offers another candidate signal, but should be evaluated in relation to the self's needs, not installed as another competing purpose. Prediction improvement as intrinsic motivation has established computational precedents; its integration into UEMA's wellness is a proposal here. [Schmidhuber's formulation](https://people.idsia.ch/~juergen/creativity.html).

**Anticipation makes future wellness relevant before the consequence arrives.** Predicting painful contact correctly should permit avoidance; it should not become pleasant merely because the prediction was accurate. Similarly, predictable sensory deprivation must not count as wellness solely because its prediction error is low. Your presentation's body/brain intermodulation matters here. The movement-driven sensory-modulation option is currently off, and enabling it alone would still not implement the presentation's general pain/pleasure modulation.

Your “man on the hill” supplies a candidate perceptual mechanism. A higher representation can infer the hand behind the glass from the visible scene, touch, and body position. It should still predict the glass's visible pixels correctly. Inferred hidden structure and observed surface appearance must coexist. The present gate blends a prediction into one pooled token per sense; it is not yet your hierarchy of local, reconstructable patterns. [Current gate](../Source/Python/mimo_bridge/lem_model.py#L1107).

The other LLM's prohibition on feedback reaching learning is too broad. Higher-level inference can participate in learning; sensory evidence must remain able to correct it. The prior defect was replacing the evidence used to check a prediction with the prediction itself. Hierarchical predictive coding learns both representations and predictions through that correction process. [Rao and Ballard](https://www.cs.utexas.edu/~dana/nn.pdf).

A dictionary could support reconstructable, reusable patterns, but adding quantization to a pooled vision token would not implement your full idea. [VQ-VAE](https://arxiv.org/abs/1711.00937) and [VQ-VAE-2](https://arxiv.org/abs/1906.00446) are relevant mechanisms, not equivalent descriptions of the whole architecture. They are not defined by retaining only frequent patterns. I would preserve patterns according to their learned consequences for wellness as well as reuse and unresolved novelty: a rare pattern preceding a large loss of wellness may deserve retention after one encounter. Retaining everything rare can instead preserve noise. A fixed half-frequent/half-rare split is a hypothesis to compare, not established superiority or demonstrated originality.

The next decisive test should therefore change **value**, not merely position. After experience with a source of pleasant contact, change the contact's consequences or the body's capacity. Does the model anticipate the changed wellness and alter its approach, effort, or withdrawal without an object-specific reward or direction? Keep the four toy manipulations as control checks, and separately test whether the inferred hidden hand is corrected when it becomes visible. These tests connect behavior and perception to your stated mechanism.

The latest status report does not settle that question. The higher self-model is observation-only and cannot yet influence wellness or action. Its occupied-slot count does not establish distinct learned situations: the same isolated check constructed identical centers that were all counted as active. That is a control showing the metric's limitation, not a diagnosis of the live checkpoint. The quoted rest figures were not remeasured in this review. [Self-model implementation](../Source/Python/mimo_bridge/hierarchical_self.py), [check results](intention_research_2026-09-09/wellness_reconsideration/check_wellness_routes.json).