Prose Quality (Semantic Planning)

By default, Glossia embeds payload words into any grammatically valid slot. That is always correct, but it can read oddly — "the clock discovers the mountain" is grammatical yet nonsensical. Semantic planning biases word choice toward coherent pairings (an animate subject for a thinking verb, a place for a motion verb, and so on), so English output reads more naturally.

The guarantee: it never changes what you decode

Semantic planning only affects which cover words appear and how sentences are shaped. It never changes which payload words are emitted or their order. So an encoding decodes to exactly the same data whether or not this layer is active — decoding is just filtering the text against the payload wordlist, and the payload is untouched. You can always recover your data.

It's on by default for English. Languages without a semantic dataset are unaffected and behave exactly as before.

Prose quality (best-of-N)

The strongest quality lever is best-of-N: Glossia generates several realizations of the same payload and keeps the best-reading one. Selection is density-first — it never sacrifices how compactly your data is packed — and breaks ties on coherence. Because every candidate encodes the identical payload in order, choosing among them is free: the result always decodes the same.

In the web composer

The compose page has a Prose quality stepper (under the advanced options, next to the variant controls). Higher = more candidates sampled = better-reading prose, for the same message. 1 is the fastest (a single realization).

It composes with the Cover variant stepper cleanly:

  • Cover variant picks which family of wordings you're looking at.
  • Prose quality decides how hard to search that family for the best one.

Each cover variant is anchored to its own well-separated block of candidate seeds, so stepping the variant always gives a genuinely different wording, and changing the quality never makes the variant jump somewhere unrelated — at any sample size.

On the command line

# Sample 8 candidates, keep the densest / most coherent
glossia --dialect english-body --best-of 8  <your words...>

--best-of 1 (the default) is the classic single-pass behavior.

From the library / WASM

  • Rust: encode_into_language_best_of(...), or generate_text_best_of(...).
  • WASM: encode_best_of(...) and encode_raw_base_n_best_of(...).

All take an N (candidate count); N = 1 falls back to a single encoding.

What "coherent" means here

Each noun in the wordlist carries a coarse class — animate (people, animals), agentive (machines that can act, like an engine or sensor), thing, place, or abstract. Each verb declares what it expects of its subject and object. When Glossia fills a slot next to a payload verb, it prefers a cover word whose class fits — so you get "the author discovers the theory" rather than "the clock discovers the mountain."

This is a soft preference, never a hard rule: a forced payload word is always placed, even if no perfectly coherent frame exists. Coherence improves the prose; it never blocks your data.

Turning it off

Set the environment variable GLOSSIA_DISABLE_SEMANTICS=1 to force the classic part-of-speech-only planning (no semantic biasing, no coherence scoring). Useful for reproducing pre-semantic output or benchmarking.

Performance

Best-of-N is cheap. Generating a sentence is fast once Glossia has built its grammar tables — and that build happens once per process and is reused, so extra candidates cost only a fraction of a second each. In the web app the tables are built once when the page loads, so stepping the Prose quality control stays responsive.

Scope and accuracy

Semantic planning currently ships for English only. The word classifications are generated with WordNet assistance and hand-tuned overrides; they're good but not perfect, and because the layer is soft, an occasional mis-classification only slightly nudges word choice — it never affects correctness or decoding. See experiments/semantic_planner/ in the repository for the dataset and the tools that regenerate it.