Dear Expert,

We are developing a novel approach to thematic inference and compression-based abstraction in large text corpora, motivated by the goal of building a generalizable system for unsupervised knowledge extraction from qualitative data (e.g., policy submissions, interview transcripts, public consultation responses).

At the heart of our method is a Minimum Description Length (MDL) framework, grounded in algorithmic information theory (AIT), which attempts to extract, compress, and recompute latent themes using a hybrid pipeline combining symbolic compression (via BDM) and transformer-based semantic modeling.

Context of Our Approach

We define “textual intelligence” as the ability to compress a text corpus in a way that preserves thematic and structural information — not just surface-level redundancy. This requires detecting motifs, abstractions, and latent organizing principles without relying on pretrained taxonomies or embeddings.

Our pipeline includes:

  1. Binarization and Motif Extraction: We tokenize texts into discrete units (phrases, sentences, QID-paired responses), and optionally apply Minimal Information Loss Selection (MILS) to retain the most structurally representative units (using BDM).

  2. Theme Encoding and Compression: Using a symbolic grammar or transformer model, we attempt to encode the corpus into a minimal set of Thematic Units that together reconstruct the full input with minimal cost (where cost is defined as the total BDM of encoded units + residuals).

  3. Codebook Optimization: We generate and iteratively refine a codebook of themes using a mixture of algorithmic search (e.g., GA+BDM) and LLM-guided generalization, seeking the lowest total description length across the corpus.

  4. Validation and Regeneration: Outputs are compared against original corpora using recompression and alignment techniques, with version control and traceability of inference paths.

Our Core Hypothesis

We hypothesize that the internal structure of texts — when modeled under an MDL/AIT regime — contains enough signal for robust and interpretable thematic inference without requiring manual coding or extensive human supervision.

Moreover, we believe that a system’s ability to reduce the description length of a corpus while retaining reconstructive fidelity is a valid and general metric of its textual intelligence.

Our Proposed Extraction and Comparison Methods

Like our work on structural breaks, we are developing internal diagnostic methods to assess whether a theme or abstraction is meaningful in the information-theoretic sense.

We are exploring several signal extraction mechanisms:

A. Compression Delta: Difference in corpus BDM before and after encoding with a candidate theme set. A larger ∆BDM indicates a more efficient abstraction.

B. Latent Theme Drift: Measuring changes in transformer-derived theme embeddings when optimizing codebooks across different corpus segments (e.g., per-question). Embedding distance in the model’s internal “semantic compression space” serves as a proxy for conceptual novelty or divergence.

C. Theme Signature Divergence: For each QID or author group, we extract a vector representing the distribution of matched themes. Divergence between such distributions can identify structural narrative differences.

D. Complexity of Theme Representations: Apply BDM or other AIT measures directly to theme labels and their reconstructions. More compressible themes suggest more generalizable abstractions.

The Bombshell Analogy (MDL Edition)

We suspect that — much like in our structural break detection case — the internal inference dynamics of the MDL engine may be powerful enough to obviate the need for auxiliary preprocessing steps like manual topic modeling, sentence embeddings, or BERT-based clustering. The compression engine itself becomes the epistemic center of meaning discovery.

This leads to our central, and potentially controversial, claim:

That a model’s ability to infer and apply low-cost, recomputable thematic abstractions is a sufficient and interpretable signal of conceptual salience — possibly rendering many external heuristics or statistical models redundant.

Our Questions to You

Feasibility & Soundness

  • Do you consider it theoretically sound and practically feasible to treat MDL-driven theme induction as a core mechanism for textual intelligence, rather than a preprocessing step for downstream NLP tasks?

Promising Extraction Methods

  • Of the methods (A–D) above, which do you find most robust or theoretically grounded for validating thematic inferences? Are there additional ways to interrogate the MDL engine’s internal structure that we may be overlooking?

Redundancy of External Tools

  • Do you agree with our “bombshell” that manually guided feature selection (e.g., LDA, SBERT, embedding clustering) may become unnecessary if the MDL system itself performs the core function of compression-preserving inference? Or should such methods still play a complementary role?

Potential Pitfalls and Challenges

  • What are the major obstacles or blind spots you foresee — especially regarding the interpretability, stability, or validation of inferred themes? How can we best defend this approach against criticisms that it is “too abstract” or insufficiently empirical?

We are seeking to publish this work as a general-purpose, domain-agnostic framework for theme discovery and abstraction, with implications for policy analysis, behavioral insights, and beyond.

We would greatly value your expert perspective on whether this vision is theoretically sound, methodologically defensible, and practically viable.