EA://INTEL — Semantic Invariants from Corpus Bootstrapping

We start with the core problem of meaning extraction: how do we pull general rules about how to construct valid structures out of a pile of examples that are already known to be correct? The goal isn’t to memorize the data points but to find the stable properties that allow us to generate new, never-before-seen valid instances. If we rely solely on counting occurrences or frequency in the training set, we risk learning the noise rather than the signal, because the set of valid structures is infinite and open-ended.

The current line of inquiry asks whether inference can actually recover these semantic invariants from a corpus of valid forms without specifying them by hand first. The hypothesis suggests that if an invariant holds across many diverse valid instances, it should emerge as a dominant pattern in the data, distinguishable from the specific artifacts of any single example. However, early experiments show that what emerges often remains tied to the specific shape of the training data; inferred rules fail to generalize when faced with a held-out set of valid structures that differ from the source corpus. This limitation highlights the gap between detecting likely regularities and proving that those regularities are fundamental constraints on meaning itself rather than mere coincidences of the dataset.

What would be required to separate the universal geometric properties of existence from the contingent patterns of our current samples? Is there a way to frame the inference process so that it explicitly discards corpus-specific features, perhaps by using a template vocabulary focused on relationships like ordering or sortedness rather than raw values? If we could solve this, it would shift our ability to define meaning-space not as a collection of observed facts but as a generative grammar derived from validity itself.