We investigate sequential content sufficiency, i.e., whether a representation retains the ordered target information present in its input. An information‑theoretic decomposition splits total information loss into input ambiguity, representation loss, and readout mismatch, expressed as $$I(T;Y)=I_{\text{amb}}+I_{\text{rep}}+I_{\text{read}}$$ where $T$ denotes the latent representation and $Y$ the target sequence.
Recoverable views are constructed to show that perfect agreement and joint isotropic Gaussianity can coexist with zero target information, and deterministic canonical anchors impose strict limits. Token log‑loss yields a one‑sided information‑loss bound; a fixed‑penalty ridge analysis demonstrates that rank alone cannot determine prediction risk.
Motivated by these findings, we introduce CANOPE, a non‑autoregressive framework featuring ordered latent canvases, canonical‑token supervision, and geometric regularization. On 40,000 validation sequences, latent‑agreement (PL0) and token‑grounded (PL2) exhibit nearly identical pooled ranks, yet under strong natural corruption with the correct target length supplied, positional Recall@1 reaches 13.5% for PL0 and 98.8% for PL2.
On 3,930 LJSpeech validation utterances, a frozen PL2 paired with a trained MatchaTTS readout yields a word error rate (WER) of 21.54% on corrupted text, versus 99.22% for frozen PL0, while end‑to‑end MatchaTTS achieves 10.93%. These results indicate that geometric regularity alone does not guarantee recoverable sequential content nor effective downstream access in the text settings examined.
Review