This document brainstorms (and maps) how the attention / curiosity / drives stack applies across MathNN and every RL path in _AugmentedIntelligence.

1. MathNN applications

Area	How to apply layers	Status / hook
Dense / conv nets	Use attention block outputs as features; curiosity weights which layer activations to read	_Attention already in architectures; wire layers focus over activation keys later
Transformer demos	nn attn-demo = pure attention; layers attn-demo wraps same	Done demos
Curiosity attention	Memory slots as keys; novelty from episodic mem	nn curiosity-demo + layers curiosity-demo
Tutor / solution steps	Attend over step history (nn math attn tutor)	MathNNMathExtended
Curriculum	High drive.curiosity → harder phase; high fatigue → easier	MathNNLearning curiosity_scale
World-model rollouts	Novelty of imagined next-state embeddings	RLAgentNet world model curiosity already
Embedding spaces	Observe embeddings into layers memory when indexing	Call ObserveEmbedding from TF/semantic path
Dropout schedule	Map fatigue → higher dropout (regularize when tired)	Future
Multi-task heads	Attention over task tokens	Future architecture

Commands to combine today

[text]
nn attn-demo
nn curiosity-demo
layers mode explore
layers cycle 0.8 0.3
layers rl <nn_agent_id>

Brainstorm next MathNN features

1. `nn attend <layer>` — dump soft weights over hidden units. 
2. Curiosity-gated training — up-weight batches with high prediction error. 
3. Drive-conditioned hyperparams — lr ∝ confidence; batch size ∝ attention. 
4. Slot memory module — shared with _CuriosityAttention::EpisodicMemory. 
5. Attention regularization loss — entropy bonus on weights in explore mode.

2. RL applications (all major tracks)

2.1 Core RL (RLExplore, RLAlgorithms, RLFeatures)

Mechanism	Application
_IntrinsicCuriosity	Already count-based; also layers curiosity bonus <state> for shared table
ε-greedy / Boltzmann	Scale ε with drives.curiosity and inverse confidence
UCB / Thompson	Bandit arms = skills/goals; novelty from layers
Reward shaping	layers shape r i / _CuriosityAttention::ShapeReward
Safe shield	Mode safety → only shield-legal actions; attention on ethics

[text]
layers mode explore
layers rl cartpole
rl cartpole 50   # existing; curiosity flags in workflows

2.2 Neural RL (RLAgentNet, MathNNLearning)

Hook	Application
SetCuriosityScale	`layers rl <agent>` pushes 0.02 * beta_eff
Curriculum phases	Explore mode → advance curriculum; fatigue → hold phase
Imagine / world model	Observe imagined embeddings; bonus if novel
Align / low curiosity	Body recover → layers safety → low β → align mode

2.3 Minecraft / Gaming / Life / Driving / Knowledge / Politics RL

Domain	Attention	Curiosity
Minecraft	Attend mobs, ore goal, chat hazards	Explore unseen chunks; count visit cells
Gaming FPS	Attend enemies/threats	Peek novel angles carefully (shield)
LifeRL	Attend calendar/stress goals	Explore routines when restorative
DrivingRL	Attend lane/obstacles	Curiosity low in safety mode
KnowledgeRL	Attend uncertain claims	Info-gain over quiz items
PoliticalRL	Attend stakeholders	Explore policies under ethics shield
Space Engineers	Attend damage/power	Explore build variants
Child/parenting RL	Social attention mode	Gentle novelty

Unified pattern

[text]
each RL step:
  novelty = f(state_visit, pred_error, layers.memory)
  layers.drives step novelty success
  beta = layers.beta_eff
  r’ = r + beta * intrinsic
  if layers.mode == safety: action = shield(action)
  focus = layers.focus(state_summary)  # optional auxiliary

2.4 Distributed / offline / RLAIF

Idea	Application
Peer trust dynamics	Attend trustworthy peers more (MathDynamics trust)
Offline datasets	Curiosity over rare transitions for prioritization
RLAIF	Attention over preference pairs; curiosity for disagreement cases
PBT	Mutate β and capacity as population genes

3. Cross-cutting recipes

Recipe	MathNN	RL
`focus_work`	mode task, low β	ε↓, shield on
`explore_desk`	mode explore, high β	ε↑, count bonus
`safety_watch`	mode safety	shield strict, β≈0
`recover_narrow`	capacity 1–2, fatigue high	freeze train, exploit only

Wire later as:

[text]
layers recipe focus_work|explore_desk|safety_watch|recover_narrow

4. Implementation checklist (done vs next)

Item	Done
Unified layers / attention / curiosity commands	Yes
Curiosity-biased focus over candidates	Yes
Drives ODE step	Yes
Count curiosity + shape reward	Yes
Body link for β	Yes
Body equilibrium calls cycle	Yes
layers rl <agent> → SetCuriosityScale	Yes
Per-domain auto-hook every Minecraft step	Next
ε schedule from drives	Next
Prioritized replay by novelty	Next
PerceptionBus → auto candidates	Next

5. Example session

[text]
layers selftest
layers seed
layers mode explore
layers cycle 0.7 0.4 what next
layers rl minecraft
nn curiosity-demo
body equilibrium
layers mode safety
layers focus

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Filed under: Uncategorized - @ July 18, 2026 6:26 am