[ P(O_1:T, S_1:T) = P(S_1) \prod_t=2^T P(S_t | S_t-1) \prod_t=1^T P(O_t | S_t) ]
The closest relative might be for compression, but Primorger emphasizes primary emergence — the new state must have qualitatively new emission properties. 8. Conclusion: The Primorger as a Minimal Model of Structural Learning The HMM Primorger is a thought experiment with practical potential. It bridges probabilistic sequence modeling and structural adaptation — two pillars of intelligence that are rarely integrated. By giving a Hidden Markov Model the ability to merge its own latent states into new primary entities , we move from inference to invention.
Now define a ( \Pi ), which acts when a certain condition ( C(S_t, O_t, \theta) ) is met (e.g., high posterior entropy, predictive divergence, or a merger opportunity score). hmm primorger
| Failure Mode | Classical HMM | HMM Primorger | |--------------|---------------|----------------| | Novel regime emergence | Cannot create new hidden states | Actively merges existing states to form new primary regimes | | Structural adaptation | Requires offline retraining | Online, interventionist restructuring | | Predictive horizon collapse | Degrades after distribution shift | Re-organizes state space to maintain predictive power | | Causal opacity | Only infers | Infers and acts to simplify causal structure |
And perhaps, at the deepest level, every act of human concept formation is just an HMM Primorger running in the neocortex, quietly merging old ideas into new ones, and mistaking the result for a discovery. This article is a theoretical construct. The term “HMM Primorger” is introduced here for speculative and analytical purposes. No existing software, commercial product, or academic paper currently describes such an entity — but that may change as autonomous probabilistic systems evolve. [ P(O_1:T, S_1:T) = P(S_1) \prod_t=2^T P(S_t |
[ \Pi: (\mathcalH, \mathcalO, \Theta) \rightarrow (\mathcalH', \mathcalO', \Theta') ]
| Concept | Relation to HMM Primorger | |---------|----------------------------| | (e.g., Dirichlet process HMMs) | Allows infinite states but does not merge existing ones into primary emergent states. | | Model merging in automata theory (e.g., DFA merging) | Similar in spirit, but deterministic and non-probabilistic. | | Hierarchical HMMs | Add hierarchy but no active merger operator. | | Active inference (Friston) | Agents act to minimize free energy; Primorger acts to restructure the generative model itself. | | Failure Mode | Classical HMM | HMM
In a world of non-stationary, combinatorially exploding systems, passive models fail. The primorger does not just predict the future; it reshapes the grammar by which the future unfolds. Whether in financial markets, protein evolution, or robot cognition, the ability to detect when two hidden realities have fused into one primary truth is not just a statistical trick — it is a form of understanding.
An HMM Primorger addresses four critical failures of standard HMMs: