Model-Agnostic Ideation Framework

• Model-agnostic ideation frameworks are formalized approaches that generate and refine ideas independent of any specific model architecture, enabling flexible innovation.
• They employ modular, multi-stage pipelines such as SIPA and latent space exploration to decouple idea generation from model internals while ensuring rigorous evaluation.
• These frameworks drive advances in scientific discovery, product development, and AI interpretability by unifying methodologies across diverse domains.

A model-agnostic ideation framework refers to a formalized approach for generating, exploring, or selecting novel ideas or hypotheses that is not bound to a specific learning, reasoning, or generative model architecture. Instead, it provides unified methodologies, workflow abstractions, or modular architectures that can be instantiated atop various model classes—be they probabilistic, neural, cognitive, or agentic—in fields such as scientific discovery, product innovation, AI model interpretation, and group creativity support.

1. Principles and Formalisms Underpinning Model-Agnostic Ideation

At the core, a model-agnostic ideation framework is characterized by mechanisms that separate the generative process (how ideas are produced, refined, or selected) from the particulars of the model implementation. In the seminal work "Exploratory Model Building" (1302.6789), ideation is based on constructing hypothetical situation-descriptions defined as $\langle T, R \rangle$, where $T$ is a set of domain attributes and $R$ a set of causal or probabilistic dependencies, drawn from diverse domain databases. The process enables "controlled imagination" through selection and recombination of context-invariant relationships, supporting hypothesis generation unconstrained by any one probability model.

This abstraction manifests across domains; for interpretability in machine learning, the SIPA (Sampling, Intervention, Prediction, Aggregation) framework (Scholbeck et al., 2019) offers a four-stage pipeline that unifies existing model-agnostic explanation methods—regardless of the nature of the learned predictor—by focusing on how data is perturbed, predictions are made, and effects are aggregated. Similar model-agnostic design is central to scientific ideation systems (Keya et al., 25 Mar 2025), creativity-support tools, and cognitive models for AGI (Komarovsky, 2023), where dynamic operational memory and operational models enable broad applicability and adaptability.

The shared attribute is that ideation steps (such as sampling, transformation, modular decomposition, or evaluation) are formulated to be independent of internal model specifics, focusing instead on workflow, search, or manipulation in a space of ideas or explanatory constructs.

2. Modular Architectures and Representative Pipelines

Model-agnostic ideation frameworks typically employ modular, multi-stage pipelines, where each component can interface with various underlying models:

• Exploratory Model Building (1302.6789) decomposes ideation as:
  • Attribute and dependency selection from collective domain knowledge
  • Hypothetical context assembly ($\langle T, R \rangle$)
  • Hypothesis ranking by "interestingness," using probabilistic merit functions
• Agentic Multi-Agent Systems: In Agent Ideate (Kanumolu et al., 2 Jul 2025), modular agents—such as Patent Analyst, Keyword Extractor, Business Idea Generator, and Validator—perform subtasks in parallel or sequence, connected via standardized protocols (e.g., JSON), allowing agents to be backed by any LLM or reasoning tool.
• Latent Space Exploration: The latent-space ideation framework (Bystroński et al., 18 Jul 2025) routes ideas through
  1. A semantic encoder mapping input text to latent representations,
  2. A latent explorer (for interpolation, extrapolation, or noise perturbation),
  3. A cross-modal projector for token embedding alignment,
  4. A decoder LLM, and
  5. An evaluator LLM for originality and relevance scoring.

  This design, illustrated in Figure 1 of (Bystroński et al., 18 Jul 2025), is modular: each stage’s implementation may be swapped out or tuned to suit the task.

• Human-in-the-Loop and Feedback Integration: Frameworks such as IRIS (Garikaparthi et al., 23 Apr 2025) integrate human agency at multiple junctures, employing test-time compute expansion strategies (e.g., Monte Carlo Tree Search) while keeping human feedback central for validation and refinement.

3. Evaluation, Novelty, and Interestingness Criteria

A distinguishing feature is the formalization of what qualifies as an "interesting" or "novel" idea.

• In probabilistic frameworks (1302.6789), "interestingness" of a context $S$ is given by the merit function $F(S) = P[d = d^* | E(S)]$, with $d^*$ as the desired outcome and $E(S)$ as the set of constraints. During search (e.g., via A*), hypothetical scenarios are ranked by this measure, not mere observed likelihood.
• In SCI-IDEA (Keya et al., 25 Mar 2025), idea novelty is operationalized using cosine similarity:

  $$\text{Novelty}(c_i) = 1 - \max(\text{Cosine\_similarity}(c_i, c_j)),\quad \forall c_j \in \mathcal{C}$$

Surprise is computed as $-\log p(c_i|\mathcal{C})$, where $p$ is model-estimated probability.

• In agentic frameworks for patent-based ideation (Kanumolu et al., 2 Jul 2025), outputs are evaluated across multiple criteria including technical validity, innovativeness, specificity, and market need, typically by an LLM-as-a-judge protocol or by human raters. Aggregation may follow a scoring formula such as $E = \sum_{i=1}^{6} w_i \cdot c_i$ with $w_i$ as weights and $c_i$ as criterion scores.

4. Adaptability and Domain-Agnostic Implementation

Adaptability is central to model-agnostic ideation frameworks:

• Plug-and-Play Modules: Systems like Agent Ideate (Kanumolu et al., 2 Jul 2025) and Acceleron (Nigam et al., 7 Mar 2024) separate agent logic from LLM internals, allowing rapid substitution of back-end models (e.g., swapping GPT-4, Llama-3, or custom domain models without pipeline changes).
• Generalized Workflow Templates: The Hourglass Ideation Framework (Li et al., 2 Mar 2025) abstracts the full ideation cycle across preparation (scope, material structuring), divergence (generation, refinement), and convergence (evaluation, selection) with iterative human and LLM interplay. This structure was shown to map onto 61 distinct studies across individual and group ideation, regardless of the underlying AI system.
• Flexible Data and Task Formats: Model-agnostic frameworks are applicable to images, text, tabular data, and more (Barbalau et al., 2020). In video captioning, the MAMS framework (Lee et al., 30 Jan 2025) selects modules and attention masks dynamically based on video content, improving performance across SwinBERT, mPLUG-2, and UniVL, further demonstrating architectural independence.

5. Applications Across Domains

Model-agnostic ideation frameworks have been deployed across a spectrum of domains:

• Scientific Hypothesis Generation: Systems such as IRIS (Garikaparthi et al., 23 Apr 2025) and SCI-IDEA (Keya et al., 25 Mar 2025) use LLM-powered agents, semantic embeddings, and human-in-the-loop processes to generate, refine, and evaluate research hypotheses, impacting ML, NLP, chemistry, and physics.
• Product and Business Innovation: Agent Ideate (Kanumolu et al., 2 Jul 2025) automates the interpretation of patent data for novel business idea extraction, outperforming standalone LLMs by leveraging multi-agent orchestration, external search, and modular validation tools.
• Explainable AI and Interpretability: Exemplar synthetization (Barbalau et al., 2020) uses generative models (VAEs, GANs) and evolutionary search to produce exemplars across data types for model explanation without internal access, while frameworks like MACE (Yang et al., 2022) provide model-agnostic counterfactuals even for non-differentiable models.
• Creativity and Group Ideation: Brainwriting frameworks (Shaer et al., 22 Feb 2024) and LLM-assisted hourglass pipelines (Li et al., 2 Mar 2025) harness LLMs for both idea expansion and evaluation, with feedback loops supporting divergence and convergence phases in group and educational settings.
• Adaptive Testing: The MAAT framework (Bi et al., 2021) formulates question selection for computerized adaptive tests so as to maximize both information gain (quality) and knowledge concept coverage (diversity), regardless of the test-taker model.

6. Emerging Challenges and Future Directions

While model-agnostic ideation frameworks provide significant flexibility and generality, several open challenges and avenues have been identified:

• Evaluation Bottlenecks: Frameworks that incorporate an LLM-as-a-judge (Kanumolu et al., 2 Jul 2025) or use proprietary LLMs for idea scoring (Bystroński et al., 18 Jul 2025) may face limitations related to evaluation objectivity, scalability, or domain mismatch. Future work includes developing lightweight, domain-specific, or human-in-the-loop evaluators.
• Exploration Strategy Enhancement: Latent space ideation pipelines (Bystroński et al., 18 Jul 2025) have so far used conservative exploration strategies. More ambitious approaches such as swarm-based search or adaptive exploration tuned by user feedback are indicated as promising paths.
• Collaborative and Multimodal Ideation: Much current research targets individual, text-based workflows (Li et al., 2 Mar 2025), leaving room for richer, multimodal, or synchronous group-centric designs.
• Ethical Considerations: Frameworks such as SCI-IDEA (Keya et al., 25 Mar 2025) emphasize the need for attribution transparency, misuse mitigation, and balanced integration of human and AI creativity, especially as LLM-powered ideation systems become more prevalent.
• Unified Taxonomies and Process Models: The emergence of the Hourglass Ideation Framework (Li et al., 2 Mar 2025) and similar structural templates suggests a trend towards formal process models that can index, compare, or integrate systems and studies, facilitating knowledge transfer and methodological rigor.

7. Representative Models and Formulas

A selection of representative mathematical and algorithmic formulations from the literature:

• Exploratory merit function (1302.6789):

  $F(S) = P[d = d^* | E(S)]$

• Latent space interpolation/perturbation (Bystroński et al., 18 Jul 2025):

  $e_\text{new} = \lambda e_i + (1-\lambda) e_j$

  $$e_\text{new} = e_i + \epsilon, \quad \epsilon \sim \mathcal{N}(0, \sigma^2 I)$$

• SIPA workflow stages (Scholbeck et al., 2019):

  Stage	Description
  Sampling	Select data points or features to perturb
  Intervention	Modify data (e.g., permute, set to fixed value, add noise)
  Prediction	Compute model output on intervened data
  Aggregation	Summarize results (e.g., mean/variance, fit surrogate, compare losses)

• MAAT importance weighted knowledge coverage (Bi et al., 2021):

  $$\operatorname{IWKC}(Q_T) = \frac{ \sum_k w_k \cdot \operatorname{IncCov}(k, Q_T) }{ \sum_k w_k }$$

  where $$\operatorname{IncCov}(k, Q_T) = \frac{ \operatorname{cnt}(k, Q_T) }{ \operatorname{cnt}(k, Q_T) + 1 }$$

• SCI-IDEA novelty and surprise (Keya et al., 25 Mar 2025):

  $$\text{Novelty}(c_i) = 1 - \max_{c_j \in \mathcal{C}} \text{Cosine\_similarity}(c_i, c_j)$$

  $\text{Surprise}(c_i) = -\log p(c_i | \mathcal{C})$

• IRIS MCTS Upper Confidence Bound (Garikaparthi et al., 23 Apr 2025):

  $$\operatorname{UCT}(n) = \frac{ Q(n) }{ N(n) } + c \sqrt{ \frac{ \ln( N(n_p) ) }{ N(n) } }$$

Summary Table: Notable Model-Agnostic Ideation Frameworks

Framework	Domain	Key Mechanisms/Features
Exploratory Model Building	Hypothesis generation	Probabilistic recombination; context assembly; merit-based search
SIPA	Interpretability (ML)	Unified perturbation–prediction–aggregation stages
SCI-IDEA	Scientific ideation	Prompting, novelty/surprise metrics, embedding-based Aha detection
IRIS	Scientific ideation	Multi-agent, MCTS, human-in-the-loop, query-based retrieval
Agent Ideate	Product innovation	Multi-agent, LLM+tool, modular, LLM-as-a-Judge evaluation
MAMS	Video captioning	Module/token selection, adaptive attention, architecture-agnostic
MAAT	Adaptive testing	Active learning-inspired, quality/diversity/importance modules
LLM Hourglass Framework	General ideation	Preparation–divergence–convergence pipeline, model/task-agnostic

Model-agnostic ideation frameworks are transforming creative AI, hypothesis generation, product and scientific innovation, and model interpretability by enabling systematic, modular, and adaptive processes untethered from the constraints of any single model architecture. These frameworks facilitate not only creativity and flexibility in computational systems but also support rigorous evaluation, reproducibility, and cross-domain innovation.