Prompt-driven Cognitive Computing Framework

• PMCSF is a framework that operationalizes cognitive processes through prompt engineering, multi-modal fusion, and cognitively-informed decoding techniques.
• It employs dual pathways for text generation and cognitive prediction by integrating conceptual blending with neural dynamics to simulate human-like imperfections.
• Validated across fields like finance and medical prognosis, PMCSF shows data efficiency, robust generalization, and reduced training requirements.

A Prompt-driven Cognitive Computing Framework (PMCSF) is a technical paradigm for operationalizing cognitive processes in artificial intelligence by leveraging prompt engineering, multi-modal fusion, and cognitively-informed decoding strategies. The framework unifies advances in conceptual blending theory, neural dynamics, bounded rationality modeling, and parameter-efficient prompt learning across linguistic, vision, and tabular domains (Sato, 16 May 2025, Jiang, 1 Dec 2025, Kang et al., 2023). PMCSF is instantiated in both text generation and cognitive prediction tasks, providing empirically validated methodologies for eliciting creativity, simulating cognitive imperfections, and achieving robust generalization.

1. Theoretical Foundations and Formal Operators

PMCSF is grounded in Conceptual Blending Theory (CBT), where cognitive products emerge from fusing multiple mental spaces. In PMCSF, a prompt $p$ is decomposed into subprompts $p_A$ and $p_B$, which activate conceptual subgraphs $A$ and $B$ within the model’s semantic manifold. The generic space $G$ encodes background knowledge and syntactic priors. The formal blending procedure is:

$B(A,B)\;=\;C(A\cup B\cup G)$

where $C$ is a compression operator implemented as a minimization:

$$C(X)\;=\;\underset{z\in\mathbb{R}^d}{\operatorname{argmin}\;\Bigl\|\,\phi(X)-z\Bigr\| + \lambda\,\mathcal{R}(z)}$$

Here, $\phi(X)$ projects feature sets to an embedding, $\mathcal{R}(z)$ encodes regularization (e.g., sparsity, low-rank), and $\lambda$ balances fidelity with parsimony.

In text applications, PMCSF employs a dual pathway: conceptual blending for meaning construction and cognitive perturbation to simulate non-optimality. In cognitive prediction, modalities (e.g., MRI volumes, clinical attributes) are embedded with specialized prompt vectors (local and global), enabling knowledge transfer and domain fusion via attention mechanisms (Sato, 16 May 2025, Kang et al., 2023).

2. Neural Dynamics and Mechanistic Modules

PMCSF models prompt effects as trajectory shifts and entropy excursions in latent state space. The transition mechanism is:

$z' = z + W_p p + \varepsilon$

with $W_p$ projecting the prompt into latent space and $\varepsilon$ accounting for intrinsic noise. A transition indicator $\Delta = \|z' - z\|$ triggers a Prompt-Induced Transition (PIT) if $\Delta > \tau_{\rm PIT}$ for learned threshold $\tau_{\rm PIT}$.

Prompt-Induced Hallucinations (PIH) arise when blended domains are distant in semantic space, with factual divergence quantified by a hallucination index:

$$H(B(A,B)) = \mathrm{dist}(B(A,B), \mathcal{M}_{\rm factual})$$

where $\mathcal{M}_{\rm factual}$ is the manifold of ground-truth embeddings. Elevated $H$ values indicate output drift from factuality. Semantic entropy $S_{\rm semantic}$ is monitored via lexical probability distributions; sustained entropy rises signal PIH dynamics (Sato, 16 May 2025).

3. Multi-Modal System Architectures

PMCSF is realized with modular system architectures:

1. Linguistic and Contextual Interface: Prompts are parsed into conceptual domains via tokenization and lightweight domain-extraction.
2. Blending and Fusion Engine: Subprompt embeddings are extracted and input spaces $A$, $B$ instantiated. Higher-order blends are possible by iterating $z_{\rm blend}$ fusion.
3. Neural Dynamics Core: Latent transitions are computed; PIT and PIH tags annotate cognitive regime shifts. In VAP-Former (Kang et al., 2023), visual ($X_{\rm vis}$) and attribute ($X_{\rm attr}$) encoders process patches and tabular inputs, injecting learnable prompt vectors at each transformer block.
4. Decoding and Evaluation: Final output is decoded from latent state, optionally postprocessed for grounding and flagged for transition and hallucination status.

In VAP-Former, the processing pipeline includes global prompt tokens $g^{\ell}$ for low-frequency guidance across 3D medical volumes, introduced through learnable mappings at each visual encoder block.

4. Cognitive Simulation and Perturbation Operators

PMCSF integrates cognitive simulation to address statistical mode collapse and emulate bounded rationality in synthetic text generation (Jiang, 1 Dec 2025). The Cognitive State Decoder (CSD) converts natural text $T$ into a 17-dimensional cognitive vector $V$, covering emotion, regulation, domain, and intensity dimensions via prompt-based probabilistic projection. The Cognitive Text Encoder (CTE) maps $V$ back to text $T'$ exhibiting human-like imperfections.

CTE employs three perturbation operators:

• Sentence Length Oscillation: Models working-memory cycles via

$$L_s(n) = \big\lfloor L_0 + A\sin(\omega n + \phi) + \epsilon \big\rfloor$$

where $\epsilon\sim\mathcal{N}(0,\sigma^2)$.

• Probability Perturbation: Modulates word-choice temperature $\tau$ and emotion-congruent masking $M_{\rm bias}$:

$$P'(w_t|w_{<t}) \propto P(w_t|w_{<t})^{1/\tau} \cdot M_{\rm bias}(w_t)$$

• Associative Leap: Permits nonlinear token shifts when $$\cos(E(w_{\rm next}),\,C_{\rm prev}) < \theta_{\rm leap}$$.

Parameterization is empirical; coefficients are hand-calibrated to maintain cognitive fidelity and cross-model invariance.

5. Empirical Validation and Generalization

PMCSF achieves functional gains and statistical distinctiveness under objective evaluations (Jiang, 1 Dec 2025, Kang et al., 2023). Performance is measured via:

• Statistical Fingerprint: Jensen-Shannon divergence $\mathrm{JS}(D_{\mathrm{CTE}||D_{\mathrm{Human}})=0.0614$, compared to $\mathrm{JS}=0.4431$ for standard LLM outputs.
• Micro-statistical features: CTE text exhibits pronounced non-normality (Shapiro–Wilk $p\ll 10^{-3}$), increased coefficient of variation ($58$–$65\%$), and higher skewness ($\approx1.15$).
• Cross-model consistency: Intraclass correlation coefficients $$\mathrm{ICC}_{\mathrm{Novice}}=0.926,\;\mathrm{ICC}_{\mathrm{Veteran}}=0.902$$ demonstrate the framework’s model-agnostic cognitive topology.

In quantitative finance, CTE-generated data reduced maximum drawdown by 47.4% and delivered 8.6% Defensive Alpha, outperforming pure human and standard AI data under stress conditions.

In multi-modal cognitive prediction, VAP-Former (Kang et al., 2023) with prompt fine-tuning outperformed full model fine-tuning for progressive Mild Cognitive Impairment (pMCI) detection, raising AUC from $84.77\%$ to $86.31\%$ while training only $0.8\%$ of parameters.

Method	Modalities	Fine-tune	# Params (M)	BACC (%)	F1 (%)	AUC (%)
VA-Former FT	Vis+Tab	full	70.19	78.29±0.52	62.93±0.29	84.77±0.35
VAP-Former PT	Vis+Tab	prompts	0.59	79.22±0.58	63.13±0.11	86.31±0.25

A plausible implication is that prompt-driven architectures can simultaneously achieve data efficiency, domain transfer, and robustness against catastrophic forgetting.

6. Cross-Disciplinary Integration

PMCSF traverses multiple research domains:

• Linguistics: Implements mental space theory by operationalizing composition, completion, and elaboration as formal prompting strategies (Sato, 16 May 2025).
• Neuroscience: Latent transition dynamics echo phase transitions in cortical computation; chunking/compression is analogous to hippocampal engram formation.
• Cognitive Science and AI: Prompt-induced transitions (PIT) and hallucinations (PIH) serve as empirical assays, enabling prompt labs to emulate lesion or pharmacological studies within neural architectures.

PMCSF transforms prompt engineering into a scientific method for probing and extending cognitive dynamics, with experimental repeatability established across architectures and tasks.

7. Limitations, Applications, and Future Directions

PMCSF’s limitations include micro-perturbation-induced non-determinism, limited validation domains (requirement for application to US equities, crypto, commodities), and current restriction to text/vision modalities (Jiang, 1 Dec 2025). Ongoing work explores deterministic chaos equations and extension to multi-modal cognitive simulation (e.g., voice, vision).

Applications span quantitative finance (novel alpha factors, high-fidelity stress testing), public opinion monitoring (fine-grained emotion dynamics), and cross-domain generalization for review generation and medical prognosis (Jiang, 1 Dec 2025, Kang et al., 2023). The framework enables efficient knowledge transfer and robust fusion of heterogeneous cognitive signals by tuning prompt embeddings rather than model backbones.

A plausible implication is the emergence of “cognitive invariants” as high-dimensional information sources, suggesting that imperfections and cognitive artifacts enhance generalization and resilience, rather than constituting statistical noise.

In summary, Prompt-driven Cognitive Computing Frameworks establish a rigorous infrastructure for cognitive simulation, cross-disciplinary integration, statistical robustness, and practical functional gain via mathematically-grounded prompt engineering strategies.