Honest limits

Kept current, and deliberately specific. A limits page that only flatters is marketing; this one is part of the measurement.

• Answer selection can still pick a true-but-off-target sentence. The gate guarantees non-fabrication, not perfect relevance.
• Purely lexical retrieval has a synonym ceiling. "How many inches is the screen", where the line reads Screen 15.6" LCD and never says inches, is the open class. The offline expansion channel was built for exactly this and measured not to close it: its filter can only keep queries that copy a line — a genuine rephrasing shares no distinguishing word with its own line, so there is nothing lexical to keep it by. The code stays in the repository, off unless asked for, and closing the class needs a relevance judgement rather than a lexical one.
• Global, thematic questions are the competitor's home ground. "What are the main themes of this corpus" is answered well by community summaries; LMM has no equivalent, and the honest cost of building one the summary way is the fabrication channel it opens. The census (where) covers the countable half — which documents speak of X — exactly, in milliseconds.
• Per-question latency pays for verification. The exit gate's read-back is model calls; on a hosted engine a question runs seconds, not milliseconds. The floor is different: questions the graph can settle are answered in microseconds with zero calls.
• The judge can wobble. A model asked "does the evidence say this" does not always answer the same way twice at temperature zero. Mutual-coverage claims skip the jury entirely; the partial band still rides on it, and one conversational turn was measured flipping between runs.
• LMM does not OCR. A PDF without a text layer teaches it nothing, and learn() says so instead of reporting success.
• The sample is small. The published comparisons rest on a handful of documents and a few dozen questions. Every question, answer and scoring decision is in the repository; widen it and tell us what breaks.