A few things that might help:

The Delta Lake suggestion is tempting but I don't think it actually solves your core problem - you still need to rewrite rows to populate a new column with non-default values. Their deletion vectors help with row-level updates, not column additions.

The separate table approach is probably your best bet, but I'd avoid row_number joins. If you can do a one-time rewrite to add a content hash or deterministic row ID, your annotation table becomes (row_id, score) and the join is trivial. Yes, it's a one-time cost, but it pays off every time you add a new annotation column.

If a rewrite is truly off the table, the overlay pattern works: keep annotations in separate parquet files keyed by (file_path, row_group_index, row_offset_within_group). DuckDB handles this reasonably well. The ergonomics aren't great but it's honest about the tradeoff you're making.

The parquet metadata idea is clever but metadata is size-limited and not designed for per-row data - you'd be fighting the format.

What query engine are you using? That might change the calculus.

If it's under a few terabytes, copy it to ClickHouse and store it in the native format, make your change, and dump it back out again. ClickHouse reads and writes Parquet very efficiently and supports efficient distribution of work across clusters. It's a rewrite but it's fast.