diff --git a/README.md b/README.md
index 8475c25..5629b39 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,7 @@ Blog and experiment write-ups: [swinecoder101.github.io/hvec](https://swinecoder
 > connectors, seven codec families (`f32`, `int8`, `binary`, `binary-centred`, `pq<m>`, `mrl<k>`
 > and `mrl<k>+<codec>`), `bench recall` against the f32 ranking and against relevance judgments, a
 > BEIR importer, `bench run` across the matrix with a payload stage, `report`, an LLM judge and an
-> agent memory-loop benchmark. Four retrieval experiments are published. The answer-level
+> agent memory-loop benchmark. Ten retrieval experiments are published. The answer-level
 > experiments (two chat models) are scripted and waiting on API access. Expect APIs and results to
 > change without notice. Contributions and experiments are welcome; see [CONTRIBUTING.md](CONTRIBUTING.md).
 
diff --git a/ROADMAP.md b/ROADMAP.md
index 7734718..e10ddd2 100644
--- a/ROADMAP.md
+++ b/ROADMAP.md
@@ -181,6 +181,17 @@ decide how deep a hybrid re-score must go, and to decide where an adaptive code
   cached buffer is 5.1× faster than the original row scan. Every asymmetric codec costs about
   what f32 costs per vector. `binary-hamming` is 20× cheaper, and with an f32 re-score it reaches
   95% recall@10 on FiQA about 19× faster than exact f32: the first speed claim hvec makes.
+- [x] **Text-format experiment** (`experiments/2026-11-text-format`), with `ingest --whole-files`
+  (one chunk per file, layout kept). The same WikiBio records as prose, bullets, key-value, JSON
+  and a table row: format moves f32 nDCG@10 by up to 2×, and templated prose is last in every
+  context. The best format depends on the model (JSON on bge-small, a table row on MiniLM), not
+  the query type. There is no accuracy-for-speed trade-off: a `binary-hamming` scan plus 100
+  f32 re-scores keeps 0.96–0.99 of the best format's nDCG at 1/19 of the time. Noise ÷ band
+  predicts the depth on a held-out format within a factor of 1.16.
+- [ ] Embed one format and return another (match on key-value, hand the model prose): a payload
+  text per chunk, separate from the text embedded.
+- [ ] Per-query scores from `bench recall`, so format and codec comparisons carry confidence
+  intervals.
 - [ ] Choose the codec automatically at ingest from noise ÷ band, then measure the re-score
   depth for a target recall on a sample of self-queries. The routing experiment showed that the
   curve chooses the codec well but predicts the depth badly.
diff --git a/crates/hvec-bench/src/chunk.rs b/crates/hvec-bench/src/chunk.rs
index 53b1990..5290e98 100644
--- a/crates/hvec-bench/src/chunk.rs
+++ b/crates/hvec-bench/src/chunk.rs
@@ -42,6 +42,17 @@ pub fn chunk_text(text: &str, opts: ChunkOptions) -> Vec<String> {
     out
 }
 
+/// The whole of `text` as one chunk, line breaks and spacing kept; only the
+/// leading and trailing whitespace is trimmed. Empty input yields no chunks.
+///
+/// For corpora whose files are already one record each, where the layout is
+/// the thing under test: `chunk_text` would join every line with single spaces.
+#[must_use]
+pub fn chunk_whole(text: &str) -> Vec<String> {
+    let t = text.trim();
+    if t.is_empty() { Vec::new() } else { vec![t.to_owned()] }
+}
+
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -69,4 +80,11 @@ mod tests {
         let chunks = chunk_text("a b c d e", ChunkOptions { words: 2, overlap: 5 });
         assert_eq!(chunks, vec!["a b", "b c", "c d", "d e"]);
     }
+
+    #[test]
+    fn whole_keeps_layout_and_trims_the_ends() {
+        let text = "\n- name: ada\n- born:  1815\n\n";
+        assert_eq!(chunk_whole(text), vec!["- name: ada\n- born:  1815"]);
+        assert!(chunk_whole(" \n\t").is_empty());
+    }
 }
diff --git a/crates/hvec-bench/src/lib.rs b/crates/hvec-bench/src/lib.rs
index 002089c..b3f5481 100644
--- a/crates/hvec-bench/src/lib.rs
+++ b/crates/hvec-bench/src/lib.rs
@@ -14,7 +14,7 @@ pub mod record;
 pub mod report;
 pub mod rescore;
 
-pub use chunk::{ChunkOptions, chunk_text};
+pub use chunk::{ChunkOptions, chunk_text, chunk_whole};
 pub use judge::{JUDGE_PROMPT_VERSION, JUDGE_SYSTEM_PROMPT, Verdict, build_judge_prompt, parse_verdict};
 pub use payload::{Payload, PayloadStats};
 pub use prompt::{RAG_SYSTEM_PROMPT, build_user_prompt};
diff --git a/crates/hvec/src/cli.rs b/crates/hvec/src/cli.rs
index 79f72b3..77a6ac8 100644
--- a/crates/hvec/src/cli.rs
+++ b/crates/hvec/src/cli.rs
@@ -416,6 +416,10 @@ pub struct IngestArgs {
     /// Overlapping words between consecutive chunks.
     #[arg(long, default_value_t = 40)]
     pub overlap_words: usize,
+    /// Store each file as one chunk, verbatim (line breaks kept), instead of word windows.
+    /// For corpora of one record per file, where the layout is under test.
+    #[arg(long, conflicts_with_all = ["chunk_words", "overlap_words"])]
+    pub whole_files: bool,
     /// File extensions to include when walking directories.
     #[arg(long, value_delimiter = ',', default_value = "md,txt,markdown,rst")]
     pub ext: Vec<String>,
diff --git a/crates/hvec/src/commands/ingest.rs b/crates/hvec/src/commands/ingest.rs
index 240bb19..b287bb8 100644
--- a/crates/hvec/src/commands/ingest.rs
+++ b/crates/hvec/src/commands/ingest.rs
@@ -2,7 +2,7 @@ use std::path::{Path, PathBuf};
 use std::time::Instant;
 
 use anyhow::{Context as _, Result, bail};
-use hvec_bench::{ChunkOptions, chunk_text};
+use hvec_bench::{ChunkOptions, chunk_text, chunk_whole};
 use hvec_connect::Embedder;
 use hvec_core::{Metric, codec_by_name};
 use hvec_store::{NewChunk, NewCollection};
@@ -72,10 +72,10 @@ pub async fn run(config: &Option<PathBuf>, args: IngestArgs) -> Result<()> {
         codec = existing.codec()?;
     }
 
-    let opts = ChunkOptions {
+    let opts = (!args.whole_files).then_some(ChunkOptions {
         words: args.chunk_words,
         overlap: args.overlap_words,
-    };
+    });
     let batch = args.batch.max(1);
     let started = Instant::now();
     let mut total_chunks = 0usize;
@@ -189,11 +189,15 @@ pub async fn run(config: &Option<PathBuf>, args: IngestArgs) -> Result<()> {
     Ok(())
 }
 
-async fn read_chunks(file: &Path, opts: ChunkOptions) -> Result<Vec<String>> {
+/// Word windows under `opts`, or the whole file as one chunk when `opts` is `None`.
+async fn read_chunks(file: &Path, opts: Option<ChunkOptions>) -> Result<Vec<String>> {
     let text = tokio::fs::read_to_string(file)
         .await
         .with_context(|| format!("reading {}", file.display()))?;
-    Ok(chunk_text(&text, opts))
+    Ok(match opts {
+        Some(opts) => chunk_text(&text, opts),
+        None => chunk_whole(&text),
+    })
 }
 
 async fn embed_all(embedder: &dyn Embedder, pieces: &[String], batch: usize) -> Result<Vec<Vec<f32>>> {
diff --git a/crates/hvec/src/commands/shell.rs b/crates/hvec/src/commands/shell.rs
index adf8c20..1220416 100644
--- a/crates/hvec/src/commands/shell.rs
+++ b/crates/hvec/src/commands/shell.rs
@@ -294,6 +294,7 @@ impl Shell {
                 .unwrap_or_else(|| "f32".to_owned()),
             chunk_words: 200,
             overlap_words: 40,
+            whole_files: false,
             ext: ["md", "txt", "markdown", "rst"]
                 .iter()
                 .map(|s| (*s).to_owned())
diff --git a/crates/hvec/tests/pipeline.rs b/crates/hvec/tests/pipeline.rs
index 6dce31c..ddd7b58 100644
--- a/crates/hvec/tests/pipeline.rs
+++ b/crates/hvec/tests/pipeline.rs
@@ -1247,3 +1247,45 @@ fn bench_scan_times_paths_and_codecs_and_keeps_f32_exact() {
     assert!(codecs.iter().all(|c| c["ms_per_query"].as_f64().unwrap() >= 0.0));
     assert_eq!(r["bandwidth"].as_array().unwrap().len(), 2);
 }
+
+#[test]
+fn whole_files_stores_one_chunk_per_file() {
+    let env = Env::new();
+    // 450 words: three overlapping word windows by default, one chunk with --whole-files.
+    let long = (0..150)
+        .map(|i| format!("- field{i}: value{i}"))
+        .collect::<Vec<_>>()
+        .join("\n");
+    let path = env.write_doc("record.md", &long);
+    let path = path.to_str().unwrap();
+
+    let out = env.run(&["ingest", path, "--collection", "windows"]);
+    assert_ok(&out, "ingest windows");
+    assert!(
+        stdout(&out).contains("ingested 3 chunks from 1 files"),
+        "{}",
+        stdout(&out)
+    );
+
+    let out = env.run(&["ingest", path, "--collection", "whole", "--whole-files"]);
+    assert_ok(&out, "ingest whole");
+    assert!(
+        stdout(&out).contains("ingested 1 chunks from 1 files"),
+        "{}",
+        stdout(&out)
+    );
+
+    let out = env.run(&[
+        "ingest",
+        path,
+        "--collection",
+        "both",
+        "--whole-files",
+        "--chunk-words",
+        "50",
+    ]);
+    assert!(
+        !out.status.success(),
+        "--whole-files with --chunk-words should be refused"
+    );
+}
diff --git a/docs/_data/references.yml b/docs/_data/references.yml
index 3d9c23a..e6a9f84 100644
--- a/docs/_data/references.yml
+++ b/docs/_data/references.yml
@@ -314,6 +314,14 @@
   year: 2024
   url: https://arxiv.org/abs/2407.02883
   note: Code retrieval datasets in BEIR's layout. The source of hvec's CoSQA copy.
+- key: lebret2016wikibio
+  topic: Embeddings and RAG
+  authors: Rémi Lebret, David Grangier, Michael Auli
+  title: Neural Text Generation from Structured Data with Application to the Biography Domain
+  venue: Conference on Empirical Methods in Natural Language Processing (EMNLP)
+  year: 2016
+  url: https://arxiv.org/abs/1603.07771
+  note: WikiBio, Wikipedia infoboxes paired with each article's opening paragraph. The source of the text-format experiment's records.
 
 - key: morris2023vec2text
   topic: Embeddings and RAG
diff --git a/docs/_experiments/text-format.md b/docs/_experiments/text-format.md
new file mode 100644
index 0000000..1d19511
--- /dev/null
+++ b/docs/_experiments/text-format.md
@@ -0,0 +1,90 @@
+---
+title: "Text format: is there a best layout for a record?"
+date: 2026-11-05 21:00:00 +0000
+result: "With the facts held fixed, layout moves f32 nDCG@10 by up to 2×, and templated prose is last in every context. The best format depends on the embedding model (JSON on bge-small, a table row on MiniLM), not the query type. Its advantage survives compression: a binary-hamming scan plus 100 f32 re-scores keeps 0.96–0.99 of it at 1/19 of the time."
+references: [lebret2016wikibio, xiao2023bge, reimers2019sbert, charikar2002simhash, jegou2011pq]
+---
+
+*Full write-up, raw results and a one-command reproduction are in the repository:*
+[experiments/2026-11-text-format](https://github.com/SwineCoder101/hvec/tree/main/experiments/2026-11-text-format).
+Follow-up to [neighbour spacing]({{ "/experiments/neighbour-spacing/" | relative_url }}) and
+[zero-copy scan]({{ "/experiments/zero-copy-scan/" | relative_url }}).
+
+Every earlier experiment held the text fixed and changed the codec. This one holds the facts
+fixed and changes only their layout. The question: for a given embedding model and kind of
+query, which format and fast path give the best nDCG@10 within a time budget?
+
+**Format can only change speed indirectly.** The scan never sees text. With one record per chunk,
+every format has the same vector count and the same scan cost. Format can only move time through
+neighbour spacing, which sets how many f32 re-scores a compressed scan needs. It also changes how
+many tokens a retrieved record costs the chat model.
+
+**Hypothesis.**
+1. The best format depends on the query type. Field-labelled layouts (key-value, JSON, table)
+   win attribute lookups at f32. But they repeat the same keys in every record, which packs
+   neighbours closer, so they lose at a tight time budget.
+2. Format acts on compressed retrieval only through f32 nDCG and noise ÷ band. The depth curve
+   fitted on some formats predicts the depth on a held-out one.
+
+**Setup.** 20,000 WikiBio [1] biographies, each written seven ways.
+- **Five controlled formats** with identical facts, the cleaned infobox: templated prose, bullets,
+  key-value, JSON, and a two-row table.
+- **Two references:** the article's own opening paragraph, and a key-value header above it.
+
+There are 500 lookup queries ("who was born on 25 april 1985 and played as guard / offensive
+tackle?") and 500 descriptive ones ("people who died in columbus , ohio"), the same for every
+format and judged from the infobox. Each record is one chunk, verbatim, using the new
+`ingest --whole-files`. The models are bge-small [2] and MiniLM [3], 384 dims. The fast path is
+a `binary-hamming` scan [4] (`pq48` [5] for comparison) with an f32 re-score, timed from measured
+per-vector costs on one thread. Apple M4 Pro.
+
+**Result.** Hypothesis 1 is refuted on both counts. Hypothesis 2 is confirmed.
+
+Exact f32 nDCG@10:
+
+| format | tokens | bge lookup | bge descriptive | MiniLM lookup | MiniLM descriptive |
+|---|---:|---:|---:|---:|---:|
+| templated prose (baseline) | 114 | 0.370 | 0.174 | 0.179 | 0.130 |
+| bullets | 75 | 0.402 | 0.180 | 0.336 | 0.161 |
+| key-value | 74 | 0.435 | 0.191 | 0.276 | 0.143 |
+| JSON | 114 | **0.441** | **0.224** | 0.240 | 0.142 |
+| table row | 119 | 0.415 | 0.211 | **0.353** | **0.181** |
+| Wikipedia paragraph (reference) | 74 | 0.181 | 0.113 | 0.098 | 0.091 |
+| key-value header + paragraph (reference) | 147 | 0.370 | 0.158 | 0.208 | 0.136 |
+
+Every structured layout beats prose in every context. The model, not the query type, decides
+which one wins: JSON on bge-small, the table row on MiniLM, where JSON is only fourth.
+
+The predicted speed penalty is too small to matter. Within a context, format changes the
+neighbour band by at most 1.3×, while the model changes it by about 2×. A `binary-hamming`
+scan plus 100 f32 re-scores takes 0.22 ms against about 4.1 ms for exact f32 over 20,000
+vectors.
+
+| context | best format | f32 nDCG | hybrid nDCG (share kept) | prose f32 nDCG | prose hybrid (share kept) |
+|---|---|---:|---:|---:|---:|
+| bge-small, lookup | JSON | 0.441 | 0.423 (0.959) | 0.370 | 0.335 (0.906) |
+| bge-small, descriptive | JSON | 0.224 | 0.219 (0.977) | 0.174 | 0.169 (0.976) |
+| MiniLM, lookup | table | 0.353 | 0.349 (0.987) | 0.179 | 0.170 (0.948) |
+| MiniLM, descriptive | table | 0.181 | 0.179 (0.992) | 0.130 | 0.126 (0.976) |
+
+The fast path on the best format beats exact f32 on prose in every context, at 1/19 of the time.
+
+With one format left out at a time, noise ÷ band ranks the re-score depth at Spearman 0.985. It
+predicts the held-out format's depth for 95% recall within a factor of 1.16, tighter than across
+corpora (1.25–1.41). Together with f32 nDCG, it predicts a hybrid's nDCG on the held-out format
+within 0.003–0.007.
+
+**Reading.** Choose the format for the model. Measure f32 nDCG on a sample in two or three
+layouts; the compressed cost follows from spacing. Key-value and bullets are about 35% cheaper in
+tokens than JSON and table, and within 0.02 of the best on lookups. Putting a key-value header
+above a prose document doubles its nDCG.
+
+Caveats:
+- With 500 queries per type, gaps under about 0.02 are ties.
+- The queries are templated, and the judgments match infobox values exactly, so absolute nDCG is
+  low.
+- The domain is biographies only.
+- Only retrieval is measured, not answers.
+- Hybrid times are computed from per-vector costs, with the candidates in memory.
+
+{% include references.html %}
diff --git a/experiments/2026-11-text-format/README.md b/experiments/2026-11-text-format/README.md
new file mode 100644
index 0000000..73c2344
--- /dev/null
+++ b/experiments/2026-11-text-format/README.md
@@ -0,0 +1,216 @@
+# Text format: is there a best layout for a record, given the model and the time budget?
+
+Every earlier experiment held the text fixed and changed the codec. Neighbour spacing found
+that the kind of content changes what a codec costs, but only through the score spacing of a
+corpus's neighbours. This experiment holds the content fixed and changes only its layout: the
+same facts written as prose, bullets, key-value pairs, JSON or a table row.
+
+**The question.** For a given context (an embedding model and a kind of query), which format and
+fast path (first-stage codec, re-score depth) give the best nDCG@10 within a fixed time per query?
+
+**Format can only change speed indirectly.** The scan never sees text. With one record per chunk,
+every format has the same number of vectors and so the same scan cost per codec. Format moves
+time in two ways:
+1. **Spacing.** A format that spreads neighbours further apart lowers a codec's noise ÷ band, so a
+   `binary-hamming` scan needs fewer f32 re-scores for the same recall
+   ([two-stage re-scoring](../2026-11-two-stage-rescoring/)).
+2. **Tokens.** What a retrieved record costs the chat model to read. Measured as size per record;
+   no model is called.
+
+**Hypothesis.**
+1. The best format depends on the query type. Templated, field-labelled layouts (key-value,
+   JSON, table) match attribute lookups best at f32. They also repeat the same keys in every
+   record, which should pack neighbours closer together. Their compressed scans would then need
+   deeper re-scores, so a format can win at f32 and lose at a tight time budget.
+2. Format acts on compressed retrieval only through two numbers: f32 nDCG (how well the query
+   matches the text) and noise ÷ band (how well the ranking survives the codec). If so, the
+   noise ÷ band curve fitted on some formats predicts the re-score depth on a held-out format
+   about as well as it did across corpora (within a factor of 1.25–1.41).
+
+**Result.** Hypothesis 1 is refuted on both counts. Hypothesis 2 is confirmed.
+- **Format matters more than any codec here.** With the facts held fixed, it moves exact f32
+  nDCG@10 by up to 2×: on MiniLM lookups, templated prose scores 0.179 and a table row 0.353.
+  Templated prose is the worst of the five controlled formats in all four contexts.
+- **The best format depends on the embedding model, not the query type.** On bge-small, JSON is
+  best for both query types (0.441 lookup, 0.224 descriptive), with key-value and table close
+  behind on lookups. On MiniLM, the table row is best for both (0.353, 0.181), and JSON is only
+  fourth (0.240 on lookups).
+- **There is no accuracy-for-speed trade-off.** Within a context, format changes the neighbour
+  band by at most 1.3×, against 1.7–2× between the two models. A `binary-hamming` scan plus 100 f32
+  re-scores (0.22 ms against 4.1 ms for exact f32, 19× less) keeps 0.96–0.99 of the winning
+  format's f32 nDCG, and 0.91–0.98 of prose's. So the fast path on the best format beats
+  exact f32 on prose in every context, at 1/19 of the time.
+- **Noise ÷ band predicts depth on a held-out format** within a factor of 1.16 at 95% recall
+  (Spearman 0.985). That is tighter than across corpora. Together with f32 nDCG, it predicts a
+  hybrid's nDCG on a held-out format within 0.003–0.007. In practice this means you can pick a
+  format by measuring f32 nDCG on a sample, and the codec cost follows from spacing.
+
+## Setup
+
+| | |
+|---|---|
+| Corpus | WikiBio [1] test split (`thoughtworks/wiki_bio` on Hugging Face, parquet, 72,831 biographies): infobox plus the article's opening paragraph. 20,000 records sampled with seed 2026 |
+| Cleaning | Image, caption, website and update-stamp fields dropped. Values over 25 words or empty or "unknown" dropped. At most 16 fields per record (median 12). PTB bracket tokens (`-lrb-`) restored. Text stays lowercased, as released |
+| Formats | Seven, below. One record per chunk, verbatim, line breaks kept (`ingest --whole-files`) |
+| Queries | 500 `lookup` and 500 `descriptive` queries from the infobox, the same for every format |
+| Embedding models | `bge-small-en-v1.5` and `all-MiniLM-L6-v2`, 384 dims, fastembed on CPU, no query instruction |
+| Measurements | `bench recall` (f32 nDCG@10, `neighbour_band`, `rank_noise` for `int8`, `binary`, `binary-hamming`, `pq48`). `bench rescore` (`binary-hamming` and `pq48` first stages, f32 second stage, depths 10–1000 and adaptive margins). `bench scan` (one thread, cost per vector) |
+| Time per query | Scan + mean re-score depth × the f32 cost per vector, on one thread. The candidates are assumed to be in memory, as in [zero-copy scan](../2026-11-zero-copy-scan/) |
+| Machine | Apple M4 Pro, 26 GB, release build |
+| Code | hvec commit `cb5ffd1` |
+| Command | `./run.sh` (about an hour, mostly embedding). Results in `results/`: `recall/`, `rescore/`, `scan/`, `stats.json`, `examples.md`, `summary.txt` |
+
+### Formats
+
+`results/examples.md` shows one record in all seven. The first five hold exactly the same facts
+(the cleaned infobox). Only the layout differs.
+
+| format | layout | approx. tokens / record |
+|---|---|---:|
+| `paragraph` | one templated sentence per field: "ruth born's birth place is bay city , michigan." | 114 |
+| `bullets` | `- birth place: bay city , michigan`, one field per line | 75 |
+| `kv` | `name: ruth born; birth date: 08 august 1925; ...` on one line | 74 |
+| `json` | `{"name": "ruth born", "birth_date": "08 august 1925", ...}` | 114 |
+| `table` | a two-row Markdown table, header then values | 119 |
+| `wiki` | the article's own opening paragraph, up to 150 words. Different facts: the natural-prose reference, not a controlled comparison | 74 |
+| `header-prose` | `kv` as a header, a blank line, then `wiki`. More facts than either | 147 |
+
+Token counts are a word-and-punctuation split from `stats.json`, not a model's tokenizer.
+
+### Queries
+
+- **`lookup`**: two of a record's attributes, for example "who was born on 25 april 1985 and
+  played as guard / offensive tackle?". Relevant to every record with both values, and kept only
+  when that is at most three records.
+- **`descriptive`**: one attribute value shared by 5 to 50 records, for example "people who died
+  in columbus , ohio". Relevant to all of them (5,860 judgments in all).
+
+The queries use their own phrasing ("was born in", "played as"), not the field labels the
+documents use ("birth place", "position"). That way no infobox format gets the query's words for
+free.
+
+## Results
+
+### Exact f32 nDCG@10 by format
+
+| format | tokens | bge lookup | bge descriptive | MiniLM lookup | MiniLM descriptive |
+|---|---:|---:|---:|---:|---:|
+| `paragraph` (baseline prose) | 114 | 0.370 | 0.174 | 0.179 | 0.130 |
+| `bullets` | 75 | 0.402 | 0.180 | 0.336 | 0.161 |
+| `kv` | 74 | 0.435 | 0.191 | 0.276 | 0.143 |
+| `json` | 114 | **0.441** | **0.224** | 0.240 | 0.142 |
+| `table` | 119 | 0.415 | 0.211 | **0.353** | **0.181** |
+| `wiki` (reference) | 74 | 0.181 | 0.113 | 0.098 | 0.091 |
+| `header-prose` (reference) | 147 | 0.370 | 0.158 | 0.208 | 0.136 |
+
+Every structured layout beats templated prose in every context, by 4% (bullets, bge
+descriptive) to 97% (table, MiniLM lookup). The model decides which one wins. JSON is best on
+bge-small and middling on MiniLM. The table row is best on MiniLM and within 0.03 of the top on
+bge-small.
+
+### Spacing and re-score depth
+
+| | bge-small | MiniLM |
+|---|---|---|
+| neighbour band, range across formats | 0.0082–0.0116 | 0.0157–0.0213 |
+| `binary-hamming` noise ÷ band | 4.47–5.39 | 2.12–2.80 |
+| `pq48` noise ÷ band | 1.08–1.41 | 0.87–1.18 |
+| f32 re-scores for 95% recall@10 behind `binary-hamming` | 476–966 | 225–441 |
+
+Within a context (one model and one query type), format changes the band by at most 1.3×. The
+model changes it by about 2×. The
+spacing penalty predicted for structured layouts appears once: JSON on bge-small has the tightest
+band (0.0082 on lookups) and the highest `binary-hamming` noise ÷ band (5.39). It is too small
+to matter, though. JSON still needs fewer re-scores for 95% recall than prose (760 against 966).
+
+### The fast path: `binary-hamming` scan, 100 f32 re-scores (0.22 ms against about 4.1 ms)
+
+| context | best format | its f32 nDCG | its hybrid nDCG (share kept) | `paragraph` f32 nDCG | `paragraph` hybrid (share kept) |
+|---|---|---:|---:|---:|---:|
+| bge-small, lookup | `json` | 0.441 | 0.423 (0.959) | 0.370 | 0.335 (0.906) |
+| bge-small, descriptive | `json` | 0.224 | 0.219 (0.977) | 0.174 | 0.169 (0.976) |
+| MiniLM, lookup | `table` | 0.353 | 0.349 (0.987) | 0.179 | 0.170 (0.948) |
+| MiniLM, descriptive | `table` | 0.181 | 0.179 (0.992) | 0.130 | 0.126 (0.976) |
+
+Scan cost does not depend on format. Across all 14 collections an exact f32 scan takes
+3.87–4.25 ms and a `binary-hamming` scan 0.19–0.22 ms (20,000 vectors, one thread). At 100
+candidates, agreement with the exact top 10 is only 0.64–0.87, but most of the neighbours lost
+are not relevant, so nDCG barely moves. In every context, the best format and operating point
+within 10% of an exact scan's time (0.41 ms) is the same as within 100%: JSON on bge-small and
+the table row on MiniLM.
+
+### Cross-format checks, leaving one format out at a time
+
+56 (format, model, query type, first stage) points.
+
+| target recall@10 | Spearman ρ (noise ÷ band, depth) | fitted depth | held-out format, mean factor error |
+|---|---:|---|---:|
+| 90% | 0.985 | 3.08·k·r^1.70 | 1.14 |
+| 95% | 0.985 | 4.74·k·r^1.83 | 1.16 |
+| 99% | 0.968 | 10.59·k·r^1.98 | 1.24 |
+
+The share of f32 nDCG a hybrid keeps, predicted from log noise ÷ band and fitted without the
+held-out format, has these errors:
+
+| N | error | f32 nDCG × the mean share kept |
+|---:|---:|---:|
+| 10 | 0.0067 | 0.0181 |
+| 30 | 0.0047 | 0.0117 |
+| 100 | 0.0026 | 0.0048 |
+
+## Discussion
+
+**The answer to the question is mostly "pick the format for the model".** The fast path costs
+every format about the same, so the format that wins at f32 also wins at a tenth of the time. You
+can choose a format by embedding a sample in two or three layouts and measuring f32 nDCG. The
+compressed cost then follows from noise ÷ band, which held on formats it was not fitted on.
+
+**Prose is the weakest layout for attribute queries, even with identical facts.** The templated
+sentences repeat the person's name in every sentence ("ruth born's ... is ..."), which may dilute
+the fields. That makes `paragraph` a pessimistic stand-in for prose. The real Wikipedia paragraph
+(`wiki`) is lower still, but it lacks many of the facts asked about.
+
+**A metadata header is a cheap fix for prose documents.** Putting the key-value fields above the
+Wikipedia paragraph doubles its nDCG (bge-small lookups 0.181 → 0.370, MiniLM 0.098 → 0.208). The
+record alone still scores higher, and the header costs the most tokens (147).
+
+**Tokens: key-value and bullets are about 35% cheaper than JSON and table** (74–75 against
+114–119 per record). On bge-small lookups, key-value is within 0.006 of JSON. On MiniLM lookups,
+bullets are within 0.017 of the table. For descriptive queries the cheaper format gives up more
+(0.033 and 0.020).
+
+**Why JSON on bge-small and a table on MiniLM?** Not tested. Finding out would take an ablation,
+for example JSON without its quotes and braces, or a table without its pipes.
+
+**The depth curve is steeper here than across corpora** (r^1.83 at 95% against r^1.29). Most of
+the ratio's range in this experiment comes from the model and the codec, not the format. So the
+check shows that the curve carries across formats, not that its exponent is universal.
+
+**Caveats.**
+- **No confidence intervals.** With 500 queries per type, treat gaps under about 0.02 nDCG as
+  ties: JSON against key-value on bge-small lookups, table against bullets on MiniLM lookups.
+  The per-query scores are not saved, so there is no bootstrap.
+- **Absolute nDCG is low** (0.09–0.44). Three causes: the queries are templated, the
+  judgments match infobox values exactly (so "new york city" and "new york city , new york ,
+  u.s." are separate categories), and no query instruction is used for bge-small. All seven
+  formats share these, but the ratios between formats may not hold for realistic queries.
+- **`wiki` and `header-prose` are not controlled comparisons.** Their facts differ from the
+  infobox's.
+- **One domain.** Biographies are short, field-heavy records. Long documents, code, or tables
+  with many rows may rank the formats differently.
+- **Retrieval only.** Whether a format helps a chat model answer needs a chat model and a judge,
+  as Experiments 1 and 3 do.
+- **Time is computed from per-vector costs**, not timed end to end, and assumes the f32
+  candidates are in memory.
+
+**Next.**
+- Embed one format and return another (for example, match on `kv` and hand the model `wiki`).
+  Retrieval and reading may want different layouts. `ingest` stores the text it embeds, so this
+  needs a separate payload text per chunk.
+- A second structured source, and realistic queries, to see whether "the model decides" holds.
+- Save per-query scores in `bench recall` so format comparisons can carry confidence intervals.
+
+## References
+
+1. Lebret, Grangier and Auli, "Neural Text Generation from Structured Data with Application to
+   the Biography Domain", EMNLP 2016. [arXiv:1603.07771](https://arxiv.org/abs/1603.07771)
diff --git a/experiments/2026-11-text-format/results/config.toml b/experiments/2026-11-text-format/results/config.toml
new file mode 100644
index 0000000..74c511e
--- /dev/null
+++ b/experiments/2026-11-text-format/results/config.toml
@@ -0,0 +1,17 @@
+db_path = "/Users/liam/dev/hvec/experiments/2026-11-text-format/work/hvec.db"
+default_chat = "anthropic"
+default_embedder = "bge-small"
+metric = "cosine"
+
+[chat.anthropic]
+provider = "anthropic"
+model = "claude-opus-5-5"
+api_key_env = "ANTHROPIC_API_KEY"
+
+[embed.bge-small]
+provider = "local"
+model = "BGESmallENV15"
+
+[embed.minilm]
+provider = "local"
+model = "AllMiniLML6V2"
diff --git a/experiments/2026-11-text-format/results/examples.md b/experiments/2026-11-text-format/results/examples.md
new file mode 100644
index 0000000..987eef9
--- /dev/null
+++ b/experiments/2026-11-text-format/results/examples.md
@@ -0,0 +1,62 @@
+# One record (wb05959) in every format
+
+## paragraph
+
+```
+ruth born. ruth born's throws is right. ruth born's birth date is 08 august 1925. ruth born's position is pitcher. ruth born's birth place is bay city , michigan. ruth born's bats is right. ruth born's team is all-american girls professional baseball league.
+```
+
+## bullets
+
+```
+- name: ruth born
+- throws: right
+- birth date: 08 august 1925
+- position: pitcher
+- birth place: bay city , michigan
+- bats: right
+- team: all-american girls professional baseball league
+```
+
+## kv
+
+```
+name: ruth born; throws: right; birth date: 08 august 1925; position: pitcher; birth place: bay city , michigan; bats: right; team: all-american girls professional baseball league
+```
+
+## json
+
+```
+{"name": "ruth born", "throws": "right", "birth_date": "08 august 1925", "position": "pitcher", "birth_place": "bay city , michigan", "bats": "right", "team": "all-american girls professional baseball league"}
+```
+
+## table
+
+```
+| name | throws | birth date | position | birth place | bats | team |
+|---|---|---|---|---|---|---|
+| ruth born | right | 08 august 1925 | pitcher | bay city , michigan | right | all-american girls professional baseball league |
+```
+
+## wiki
+
+```
+ruth born ( born august 8 , 1925 ) is a former pitcher who played in the all-american girls professional baseball league . listed at , 125 lb , she batted and threw right-handed . ruth born was one of the original south bend blue sox founding members of the all-american girls professional baseball league for its inaugural season in 1943 . a native of bay city , michigan , born grew up playing sandlot ball with her neighborhood kids at an early age , most of them boys , but did not start participating in an organized league until she was 12 years old , playing at school and in park softball leagues . at the age of 13 , she put on her first uniform with a moose lodge team .
+```
+
+## header-prose
+
+```
+name: ruth born; throws: right; birth date: 08 august 1925; position: pitcher; birth place: bay city , michigan; bats: right; team: all-american girls professional baseball league
+
+ruth born ( born august 8 , 1925 ) is a former pitcher who played in the all-american girls professional baseball league . listed at , 125 lb , she batted and threw right-handed . ruth born was one of the original south bend blue sox founding members of the all-american girls professional baseball league for its inaugural season in 1943 . a native of bay city , michigan , born grew up playing sandlot ball with her neighborhood kids at an early age , most of them boys , but did not start participating in an organized league until she was 12 years old , playing at school and in park softball leagues . at the age of 13 , she put on her first uniform with a moose lodge team .
+```
+
+## Queries
+
+- lookup: who was born on 3 february 1956 and played for medellín atlético nacional?
+- lookup: who was born on 25 april 1985 and played as guard / offensive tackle?
+- lookup: who was born on 04 october 1942 in hollymount , near claremorris , county mayo , ireland?
+- descriptive: people who died in columbus , ohio
+- descriptive: people who died in new york city , new york , u.s.
+- descriptive: people who played as pivot
diff --git a/experiments/2026-11-text-format/results/machine.txt b/experiments/2026-11-text-format/results/machine.txt
new file mode 100644
index 0000000..db304bd
--- /dev/null
+++ b/experiments/2026-11-text-format/results/machine.txt
@@ -0,0 +1,3 @@
+hvec cb5ffd1
+Apple M4 Pro
+memory: 26 GB
diff --git a/experiments/2026-11-text-format/results/recall/bullets-bge-small-descriptive.json b/experiments/2026-11-text-format/results/recall/bullets-bge-small-descriptive.json
new file mode 100644
index 0000000..6212395
--- /dev/null
+++ b/experiments/2026-11-text-format/results/recall/bullets-bge-small-descriptive.json
@@ -0,0 +1,112 @@
+[
+  {
+    "codec": "f32",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 1.0,
+    "top1_agreement": 1.0,
+    "mean_abs_score_error": 0.0,
+    "max_abs_score_error": 0.0,
+    "bytes_per_vector": 1536,
+    "compression_ratio": 1.0,
+    "encode_ms": 3,
+    "scan_ms": 2000,
+    "baseline_scan_ms": 2000,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.17969501434099042,
+    "doc_recall_at_k": 0.15695345841272806,
+    "boundary_gap": 0.0009827613830566406,
+    "neighbour_band": 0.01013973355293274,
+    "rank_noise": 0.0
+  },
+  {
+    "codec": "int8",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 0.9769999999999983,
+    "top1_agreement": 0.974,
+    "mean_abs_score_error": 0.0004987362382352352,
+    "max_abs_score_error": 0.0035058557987213135,
+    "bytes_per_vector": 392,
+    "compression_ratio": 3.9183674,
+    "encode_ms": 2,
+    "scan_ms": 2501,
+    "baseline_scan_ms": 2000,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.17882628925827088,
+    "doc_recall_at_k": 0.1561142729454467,
+    "boundary_gap": 0.0009827613830566406,
+    "neighbour_band": 0.01013973355293274,
+    "rank_noise": 0.0005157020294026582
+  },
+  {
+    "codec": "binary",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 0.40840000000000015,
+    "top1_agreement": 0.31,
+    "mean_abs_score_error": 0.2853380107817233,
+    "max_abs_score_error": 0.44112491607666016,
+    "bytes_per_vector": 48,
+    "compression_ratio": 32.0,
+    "encode_ms": 26,
+    "scan_ms": 2261,
+    "baseline_scan_ms": 2000,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.15168815299897204,
+    "doc_recall_at_k": 0.12595201442081372,
+    "boundary_gap": 0.0009827613830566406,
+    "neighbour_band": 0.01013973355293274,
+    "rank_noise": 0.021664264176572003
+  },
+  {
+    "codec": "binary-hamming",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 0.2842,
+    "top1_agreement": 0.2,
+    "mean_abs_score_error": 0.258451657642594,
+    "max_abs_score_error": 0.6197579503059387,
+    "bytes_per_vector": 48,
+    "compression_ratio": 32.0,
+    "encode_ms": 27,
+    "scan_ms": 0,
+    "baseline_scan_ms": 1999,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.12166128663735999,
+    "doc_recall_at_k": 0.0981435737515116,
+    "boundary_gap": 0.0009827613830566406,
+    "neighbour_band": 0.01013973355293274,
+    "rank_noise": 0.04885937247046683
+  },
+  {
+    "codec": "pq48",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 0.5419999999999995,
+    "top1_agreement": 0.452,
+    "mean_abs_score_error": 0.03525727904258966,
+    "max_abs_score_error": 0.147859126329422,
+    "bytes_per_vector": 48,
+    "compression_ratio": 32.0,
+    "encode_ms": 350,
+    "scan_ms": 1995,
+    "baseline_scan_ms": 1995,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.15724523921045508,
+    "doc_recall_at_k": 0.13266368133956502,
+    "boundary_gap": 0.0009827613830566406,
+    "neighbour_band": 0.01013973355293274,
+    "rank_noise": 0.012701630639716221
+  }
+]
diff --git a/experiments/2026-11-text-format/results/recall/bullets-bge-small-lookup.json b/experiments/2026-11-text-format/results/recall/bullets-bge-small-lookup.json
new file mode 100644
index 0000000..8da2fbc
--- /dev/null
+++ b/experiments/2026-11-text-format/results/recall/bullets-bge-small-lookup.json
@@ -0,0 +1,112 @@
+[
+  {
+    "codec": "f32",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 1.0,
+    "top1_agreement": 1.0,
+    "mean_abs_score_error": 0.0,
+    "max_abs_score_error": 0.0,
+    "bytes_per_vector": 1536,
+    "compression_ratio": 1.0,
+    "encode_ms": 9,
+    "scan_ms": 2002,
+    "baseline_scan_ms": 2003,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.40178147080319865,
+    "doc_recall_at_k": 0.49733333333333335,
+    "boundary_gap": 0.0010104477405548096,
+    "neighbour_band": 0.009650647640228271,
+    "rank_noise": 0.0
+  },
+  {
+    "codec": "int8",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 0.978199999999998,
+    "top1_agreement": 0.97,
+    "mean_abs_score_error": 0.000532852728164196,
+    "max_abs_score_error": 0.0036312341690063477,
+    "bytes_per_vector": 392,
+    "compression_ratio": 3.9183674,
+    "encode_ms": 2,
+    "scan_ms": 2500,
+    "baseline_scan_ms": 2000,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.4003450978888609,
+    "doc_recall_at_k": 0.49733333333333335,
+    "boundary_gap": 0.0010104477405548096,
+    "neighbour_band": 0.009650647640228271,
+    "rank_noise": 0.0005623826488021243
+  },
+  {
+    "codec": "binary",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 0.39820000000000044,
+    "top1_agreement": 0.41,
+    "mean_abs_score_error": 0.22838297591176332,
+    "max_abs_score_error": 0.40268176794052124,
+    "bytes_per_vector": 48,
+    "compression_ratio": 32.0,
+    "encode_ms": 27,
+    "scan_ms": 2312,
+    "baseline_scan_ms": 2003,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.3118627860087583,
+    "doc_recall_at_k": 0.4116666666666667,
+    "boundary_gap": 0.0010104477405548096,
+    "neighbour_band": 0.009650647640228271,
+    "rank_noise": 0.02312587601342484
+  },
+  {
+    "codec": "binary-hamming",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 0.2605999999999999,
+    "top1_agreement": 0.252,
+    "mean_abs_score_error": 0.21368755620237143,
+    "max_abs_score_error": 0.5942977070808411,
+    "bytes_per_vector": 48,
+    "compression_ratio": 32.0,
+    "encode_ms": 27,
+    "scan_ms": 0,
+    "baseline_scan_ms": 2000,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.23784049426639223,
+    "doc_recall_at_k": 0.3296666666666666,
+    "boundary_gap": 0.0010104477405548096,
+    "neighbour_band": 0.009650647640228271,
+    "rank_noise": 0.04810842078904391
+  },
+  {
+    "codec": "pq48",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 0.5301999999999993,
+    "top1_agreement": 0.5,
+    "mean_abs_score_error": 0.03072551447017491,
+    "max_abs_score_error": 0.14822229743003845,
+    "bytes_per_vector": 48,
+    "compression_ratio": 32.0,
+    "encode_ms": 372,
+    "scan_ms": 2000,
+    "baseline_scan_ms": 2000,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.33911710299556397,
+    "doc_recall_at_k": 0.41966666666666663,
+    "boundary_gap": 0.0010104477405548096,
+    "neighbour_band": 0.009650647640228271,
+    "rank_noise": 0.013291631288552421
+  }
+]
diff --git a/experiments/2026-11-text-format/results/recall/bullets-minilm-descriptive.json b/experiments/2026-11-text-format/results/recall/bullets-minilm-descriptive.json
new file mode 100644
index 0000000..2a27060
--- /dev/null
+++ b/experiments/2026-11-text-format/results/recall/bullets-minilm-descriptive.json
@@ -0,0 +1,112 @@
+[
+  {
+    "codec": "f32",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 1.0,
+    "top1_agreement": 1.0,
+    "mean_abs_score_error": 0.0,
+    "max_abs_score_error": 0.0,
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+    "compression_ratio": 1.0,
+    "encode_ms": 5,
+    "scan_ms": 2050,
+    "baseline_scan_ms": 2004,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.1613105898711521,
+    "doc_recall_at_k": 0.14068256993196904,
+    "boundary_gap": 0.0017380714416503906,
+    "neighbour_band": 0.0194818377494812,
+    "rank_noise": 0.0
+  },
+  {
+    "codec": "int8",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 0.9927999999999992,
+    "top1_agreement": 0.994,
+    "mean_abs_score_error": 0.000269356801425689,
+    "max_abs_score_error": 0.00196877121925354,
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+    "compression_ratio": 3.9183674,
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+    "baseline_scan_ms": 1844,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.16153744388643593,
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+    "boundary_gap": 0.0017380714416503906,
+    "neighbour_band": 0.0194818377494812,
+    "rank_noise": 0.0002853310502315725
+  },
+  {
+    "codec": "binary",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 0.5347999999999996,
+    "top1_agreement": 0.39,
+    "mean_abs_score_error": 0.04360414190521798,
+    "max_abs_score_error": 0.21623218059539795,
+    "bytes_per_vector": 48,
+    "compression_ratio": 32.0,
+    "encode_ms": 25,
+    "scan_ms": 2047,
+    "baseline_scan_ms": 1706,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.1362047839029237,
+    "doc_recall_at_k": 0.11573467870275221,
+    "boundary_gap": 0.0017380714416503906,
+    "neighbour_band": 0.0194818377494812,
+    "rank_noise": 0.025394864826891173
+  },
+  {
+    "codec": "binary-hamming",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
+    "recall_at_k": 0.40259999999999985,
+    "top1_agreement": 0.258,
+    "mean_abs_score_error": 0.04779376356004747,
+    "max_abs_score_error": 0.3231307864189148,
+    "bytes_per_vector": 48,
+    "compression_ratio": 32.0,
+    "encode_ms": 25,
+    "scan_ms": 0,
+    "baseline_scan_ms": 1553,
+    "labelled_queries": 500,
+    "ndcg_at_k": 0.11159863165280018,
+    "doc_recall_at_k": 0.09445272278756318,
+    "boundary_gap": 0.0017380714416503906,
+    "neighbour_band": 0.0194818377494812,
+    "rank_noise": 0.04512499398028013
+  },
+  {
+    "codec": "pq48",
+    "metric": "cosine",
+    "k": 10,
+    "vectors": 20000,
+    "queries": 500,
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new file mode 100644
index 0000000..325762d
--- /dev/null
+++ b/experiments/2026-11-text-format/results/recall/bullets-minilm-lookup.json
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new file mode 100644
index 0000000..a1dc674
--- /dev/null
+++ b/experiments/2026-11-text-format/results/recall/header-prose-bge-small-descriptive.json
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new file mode 100644
index 0000000..fc4a69b
--- /dev/null
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new file mode 100644
index 0000000..f630b7b
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new file mode 100644
index 0000000..8507736
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new file mode 100644
index 0000000..a5401ed
--- /dev/null
+++ b/experiments/2026-11-text-format/results/recall/json-bge-small-descriptive.json
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new file mode 100644
index 0000000..1e527c3
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+++ b/experiments/2026-11-text-format/results/recall/json-bge-small-lookup.json
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new file mode 100644
index 0000000..a8fe204
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new file mode 100644
index 0000000..1ffd956
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new file mode 100644
index 0000000..a6e306e
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new file mode 100644
index 0000000..97343c7
--- /dev/null
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index 0000000..ec17d79
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index 0000000..ffd166b
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index 0000000..fa0caf9
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new file mode 100644
index 0000000..226fb64
--- /dev/null
+++ b/experiments/2026-11-text-format/results/recall/paragraph-minilm-descriptive.json
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new file mode 100644
index 0000000..9fe39fa
--- /dev/null
+++ b/experiments/2026-11-text-format/results/recall/paragraph-minilm-lookup.json
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new file mode 100644
index 0000000..7adc4c8
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new file mode 100644
index 0000000..b35fb63
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new file mode 100644
index 0000000..783ca2d
--- /dev/null
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new file mode 100644
index 0000000..757b7f1
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new file mode 100644
index 0000000..b2c6bb4
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new file mode 100644
index 0000000..b09d6cb
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new file mode 100644
index 0000000..eaef822
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new file mode 100644
index 0000000..47b1150
--- /dev/null
+++ b/experiments/2026-11-text-format/results/recall/wiki-minilm-lookup.json
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new file mode 100644
index 0000000..6606188
--- /dev/null
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diff --git a/experiments/2026-11-text-format/results/rescore/bullets-bge-small-lookup.json b/experiments/2026-11-text-format/results/rescore/bullets-bge-small-lookup.json
new file mode 100644
index 0000000..2a36eaf
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/bullets-bge-small-lookup.json
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diff --git a/experiments/2026-11-text-format/results/rescore/bullets-minilm-descriptive.json b/experiments/2026-11-text-format/results/rescore/bullets-minilm-descriptive.json
new file mode 100644
index 0000000..e0b8380
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/bullets-minilm-descriptive.json
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diff --git a/experiments/2026-11-text-format/results/rescore/bullets-minilm-lookup.json b/experiments/2026-11-text-format/results/rescore/bullets-minilm-lookup.json
new file mode 100644
index 0000000..9999066
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/bullets-minilm-lookup.json
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diff --git a/experiments/2026-11-text-format/results/rescore/header-prose-bge-small-descriptive.json b/experiments/2026-11-text-format/results/rescore/header-prose-bge-small-descriptive.json
new file mode 100644
index 0000000..6473619
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/header-prose-bge-small-descriptive.json
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diff --git a/experiments/2026-11-text-format/results/rescore/header-prose-bge-small-lookup.json b/experiments/2026-11-text-format/results/rescore/header-prose-bge-small-lookup.json
new file mode 100644
index 0000000..a8a6e39
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/header-prose-bge-small-lookup.json
@@ -0,0 +1,664 @@
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diff --git a/experiments/2026-11-text-format/results/rescore/header-prose-minilm-descriptive.json b/experiments/2026-11-text-format/results/rescore/header-prose-minilm-descriptive.json
new file mode 100644
index 0000000..aa43959
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/header-prose-minilm-descriptive.json
@@ -0,0 +1,664 @@
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diff --git a/experiments/2026-11-text-format/results/rescore/header-prose-minilm-lookup.json b/experiments/2026-11-text-format/results/rescore/header-prose-minilm-lookup.json
new file mode 100644
index 0000000..5b66738
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/header-prose-minilm-lookup.json
@@ -0,0 +1,664 @@
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diff --git a/experiments/2026-11-text-format/results/rescore/json-bge-small-descriptive.json b/experiments/2026-11-text-format/results/rescore/json-bge-small-descriptive.json
new file mode 100644
index 0000000..651e06f
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/json-bge-small-descriptive.json
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new file mode 100644
index 0000000..5896f84
--- /dev/null
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diff --git a/experiments/2026-11-text-format/results/rescore/json-minilm-lookup.json b/experiments/2026-11-text-format/results/rescore/json-minilm-lookup.json
new file mode 100644
index 0000000..db49624
--- /dev/null
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diff --git a/experiments/2026-11-text-format/results/rescore/kv-bge-small-descriptive.json b/experiments/2026-11-text-format/results/rescore/kv-bge-small-descriptive.json
new file mode 100644
index 0000000..9b50788
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/kv-bge-small-descriptive.json
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diff --git a/experiments/2026-11-text-format/results/rescore/kv-bge-small-lookup.json b/experiments/2026-11-text-format/results/rescore/kv-bge-small-lookup.json
new file mode 100644
index 0000000..eec244e
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/kv-bge-small-lookup.json
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diff --git a/experiments/2026-11-text-format/results/rescore/kv-minilm-descriptive.json b/experiments/2026-11-text-format/results/rescore/kv-minilm-descriptive.json
new file mode 100644
index 0000000..e51b1e3
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/kv-minilm-descriptive.json
@@ -0,0 +1,664 @@
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diff --git a/experiments/2026-11-text-format/results/rescore/kv-minilm-lookup.json b/experiments/2026-11-text-format/results/rescore/kv-minilm-lookup.json
new file mode 100644
index 0000000..51f9361
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/kv-minilm-lookup.json
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diff --git a/experiments/2026-11-text-format/results/rescore/paragraph-bge-small-descriptive.json b/experiments/2026-11-text-format/results/rescore/paragraph-bge-small-descriptive.json
new file mode 100644
index 0000000..c0b6891
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/paragraph-bge-small-descriptive.json
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diff --git a/experiments/2026-11-text-format/results/rescore/paragraph-bge-small-lookup.json b/experiments/2026-11-text-format/results/rescore/paragraph-bge-small-lookup.json
new file mode 100644
index 0000000..e4c77b5
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/paragraph-bge-small-lookup.json
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diff --git a/experiments/2026-11-text-format/results/rescore/paragraph-minilm-descriptive.json b/experiments/2026-11-text-format/results/rescore/paragraph-minilm-descriptive.json
new file mode 100644
index 0000000..8f0aba5
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/paragraph-minilm-descriptive.json
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diff --git a/experiments/2026-11-text-format/results/rescore/paragraph-minilm-lookup.json b/experiments/2026-11-text-format/results/rescore/paragraph-minilm-lookup.json
new file mode 100644
index 0000000..519ede5
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/paragraph-minilm-lookup.json
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diff --git a/experiments/2026-11-text-format/results/rescore/table-bge-small-descriptive.json b/experiments/2026-11-text-format/results/rescore/table-bge-small-descriptive.json
new file mode 100644
index 0000000..30f0c7c
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/table-bge-small-descriptive.json
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diff --git a/experiments/2026-11-text-format/results/rescore/table-bge-small-lookup.json b/experiments/2026-11-text-format/results/rescore/table-bge-small-lookup.json
new file mode 100644
index 0000000..0dee8a6
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/table-bge-small-lookup.json
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diff --git a/experiments/2026-11-text-format/results/rescore/table-minilm-descriptive.json b/experiments/2026-11-text-format/results/rescore/table-minilm-descriptive.json
new file mode 100644
index 0000000..105bb40
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/table-minilm-descriptive.json
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diff --git a/experiments/2026-11-text-format/results/rescore/table-minilm-lookup.json b/experiments/2026-11-text-format/results/rescore/table-minilm-lookup.json
new file mode 100644
index 0000000..7bd2b35
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/table-minilm-lookup.json
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diff --git a/experiments/2026-11-text-format/results/rescore/wiki-bge-small-descriptive.json b/experiments/2026-11-text-format/results/rescore/wiki-bge-small-descriptive.json
new file mode 100644
index 0000000..4bc268e
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/wiki-bge-small-descriptive.json
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diff --git a/experiments/2026-11-text-format/results/rescore/wiki-bge-small-lookup.json b/experiments/2026-11-text-format/results/rescore/wiki-bge-small-lookup.json
new file mode 100644
index 0000000..3832c93
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/wiki-bge-small-lookup.json
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diff --git a/experiments/2026-11-text-format/results/rescore/wiki-minilm-descriptive.json b/experiments/2026-11-text-format/results/rescore/wiki-minilm-descriptive.json
new file mode 100644
index 0000000..e99cf95
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/wiki-minilm-descriptive.json
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diff --git a/experiments/2026-11-text-format/results/rescore/wiki-minilm-lookup.json b/experiments/2026-11-text-format/results/rescore/wiki-minilm-lookup.json
new file mode 100644
index 0000000..b98ab16
--- /dev/null
+++ b/experiments/2026-11-text-format/results/rescore/wiki-minilm-lookup.json
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diff --git a/experiments/2026-11-text-format/results/scan/bullets-bge-small.json b/experiments/2026-11-text-format/results/scan/bullets-bge-small.json
new file mode 100644
index 0000000..c3c154e
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/bullets-bge-small.json
@@ -0,0 +1,59 @@
+{
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diff --git a/experiments/2026-11-text-format/results/scan/bullets-minilm.json b/experiments/2026-11-text-format/results/scan/bullets-minilm.json
new file mode 100644
index 0000000..8c0c09b
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/bullets-minilm.json
@@ -0,0 +1,59 @@
+{
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diff --git a/experiments/2026-11-text-format/results/scan/header-prose-bge-small.json b/experiments/2026-11-text-format/results/scan/header-prose-bge-small.json
new file mode 100644
index 0000000..2998612
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/header-prose-bge-small.json
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+{
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diff --git a/experiments/2026-11-text-format/results/scan/header-prose-minilm.json b/experiments/2026-11-text-format/results/scan/header-prose-minilm.json
new file mode 100644
index 0000000..154c637
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/header-prose-minilm.json
@@ -0,0 +1,59 @@
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diff --git a/experiments/2026-11-text-format/results/scan/json-bge-small.json b/experiments/2026-11-text-format/results/scan/json-bge-small.json
new file mode 100644
index 0000000..a1fab3a
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/json-bge-small.json
@@ -0,0 +1,59 @@
+{
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diff --git a/experiments/2026-11-text-format/results/scan/json-minilm.json b/experiments/2026-11-text-format/results/scan/json-minilm.json
new file mode 100644
index 0000000..c91f18f
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/json-minilm.json
@@ -0,0 +1,59 @@
+{
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diff --git a/experiments/2026-11-text-format/results/scan/kv-bge-small.json b/experiments/2026-11-text-format/results/scan/kv-bge-small.json
new file mode 100644
index 0000000..9652cb9
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/kv-bge-small.json
@@ -0,0 +1,59 @@
+{
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+  "codecs": [
+    {
+      "codec": "f32",
+      "bytes_per_vector": 1536,
+      "threads": 1,
+      "ms_per_query": 4.0628725,
+      "gb_per_s": 7.561152854292129,
+      "recall_at_k": 1.0
+    },
+    {
+      "codec": "binary-hamming",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 0.20218166,
+      "gb_per_s": 4.748205153721658,
+      "recall_at_k": 0.346
+    },
+    {
+      "codec": "pq48",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 3.99232584,
+      "gb_per_s": 0.24046133468905434,
+      "recall_at_k": 0.566
+    }
+  ],
+  "bandwidth": [
+    {
+      "threads": 1,
+      "gb_per_s": 89.83281590352883
+    }
+  ]
+}
diff --git a/experiments/2026-11-text-format/results/scan/kv-minilm.json b/experiments/2026-11-text-format/results/scan/kv-minilm.json
new file mode 100644
index 0000000..7d4bb00
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/kv-minilm.json
@@ -0,0 +1,59 @@
+{
+  "collection": "kv-minilm",
+  "vectors": 20000,
+  "dimension": 384,
+  "queries": 50,
+  "k": 10,
+  "repeat": 3,
+  "paths": [
+    {
+      "path": "cached buffer (Store::search)",
+      "ms_per_query": 4.03564166,
+      "note": "codes scanned in place in one allocation; text read for the hits only"
+    }
+  ],
+  "loads": [
+    {
+      "source": "sqlite (borrowed blobs, one copy)",
+      "ms": 10.900791,
+      "bytes": 30720000
+    },
+    {
+      "source": "flat code file (one read)",
+      "ms": 2.695208,
+      "bytes": 30720000
+    }
+  ],
+  "codecs": [
+    {
+      "codec": "f32",
+      "bytes_per_vector": 1536,
+      "threads": 1,
+      "ms_per_query": 4.05272334,
+      "gb_per_s": 7.580088109345259,
+      "recall_at_k": 1.0
+    },
+    {
+      "codec": "binary-hamming",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 0.20207418,
+      "gb_per_s": 4.750730647527557,
+      "recall_at_k": 0.396
+    },
+    {
+      "codec": "pq48",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 3.97777334,
+      "gb_per_s": 0.2413410513732288,
+      "recall_at_k": 0.63
+    }
+  ],
+  "bandwidth": [
+    {
+      "threads": 1,
+      "gb_per_s": 89.54191085352124
+    }
+  ]
+}
diff --git a/experiments/2026-11-text-format/results/scan/paragraph-bge-small.json b/experiments/2026-11-text-format/results/scan/paragraph-bge-small.json
new file mode 100644
index 0000000..5b86409
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/paragraph-bge-small.json
@@ -0,0 +1,59 @@
+{
+  "collection": "paragraph-bge-small",
+  "vectors": 20000,
+  "dimension": 384,
+  "queries": 50,
+  "k": 10,
+  "repeat": 3,
+  "paths": [
+    {
+      "path": "cached buffer (Store::search)",
+      "ms_per_query": 4.308935,
+      "note": "codes scanned in place in one allocation; text read for the hits only"
+    }
+  ],
+  "loads": [
+    {
+      "source": "sqlite (borrowed blobs, one copy)",
+      "ms": 14.478916,
+      "bytes": 30720000
+    },
+    {
+      "source": "flat code file (one read)",
+      "ms": 2.631709,
+      "bytes": 30720000
+    }
+  ],
+  "codecs": [
+    {
+      "codec": "f32",
+      "bytes_per_vector": 1536,
+      "threads": 1,
+      "ms_per_query": 4.18593834,
+      "gb_per_s": 7.338856310052575,
+      "recall_at_k": 1.0
+    },
+    {
+      "codec": "binary-hamming",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 0.20931584,
+      "gb_per_s": 4.586370529817523,
+      "recall_at_k": 0.346
+    },
+    {
+      "codec": "pq48",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 4.05231,
+      "gb_per_s": 0.23690191520392073,
+      "recall_at_k": 0.6
+    }
+  ],
+  "bandwidth": [
+    {
+      "threads": 1,
+      "gb_per_s": 85.92912551773942
+    }
+  ]
+}
diff --git a/experiments/2026-11-text-format/results/scan/paragraph-minilm.json b/experiments/2026-11-text-format/results/scan/paragraph-minilm.json
new file mode 100644
index 0000000..2b09379
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/paragraph-minilm.json
@@ -0,0 +1,59 @@
+{
+  "collection": "paragraph-minilm",
+  "vectors": 20000,
+  "dimension": 384,
+  "queries": 50,
+  "k": 10,
+  "repeat": 3,
+  "paths": [
+    {
+      "path": "cached buffer (Store::search)",
+      "ms_per_query": 4.249301679999999,
+      "note": "codes scanned in place in one allocation; text read for the hits only"
+    }
+  ],
+  "loads": [
+    {
+      "source": "sqlite (borrowed blobs, one copy)",
+      "ms": 14.368625,
+      "bytes": 30720000
+    },
+    {
+      "source": "flat code file (one read)",
+      "ms": 2.684916,
+      "bytes": 30720000
+    }
+  ],
+  "codecs": [
+    {
+      "codec": "f32",
+      "bytes_per_vector": 1536,
+      "threads": 1,
+      "ms_per_query": 4.24859,
+      "gb_per_s": 7.230634163334188,
+      "recall_at_k": 1.0
+    },
+    {
+      "codec": "binary-hamming",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 0.20095584,
+      "gb_per_s": 4.7771689541344005,
+      "recall_at_k": 0.45
+    },
+    {
+      "codec": "pq48",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 4.0619891599999995,
+      "gb_per_s": 0.23633741060992888,
+      "recall_at_k": 0.636
+    }
+  ],
+  "bandwidth": [
+    {
+      "threads": 1,
+      "gb_per_s": 84.47343434820235
+    }
+  ]
+}
diff --git a/experiments/2026-11-text-format/results/scan/table-bge-small.json b/experiments/2026-11-text-format/results/scan/table-bge-small.json
new file mode 100644
index 0000000..dfb637f
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/table-bge-small.json
@@ -0,0 +1,59 @@
+{
+  "collection": "table-bge-small",
+  "vectors": 20000,
+  "dimension": 384,
+  "queries": 50,
+  "k": 10,
+  "repeat": 3,
+  "paths": [
+    {
+      "path": "cached buffer (Store::search)",
+      "ms_per_query": 3.9332358199999997,
+      "note": "codes scanned in place in one allocation; text read for the hits only"
+    }
+  ],
+  "loads": [
+    {
+      "source": "sqlite (borrowed blobs, one copy)",
+      "ms": 11.884250000000002,
+      "bytes": 30720000
+    },
+    {
+      "source": "flat code file (one read)",
+      "ms": 5.896292,
+      "bytes": 30720000
+    }
+  ],
+  "codecs": [
+    {
+      "codec": "f32",
+      "bytes_per_vector": 1536,
+      "threads": 1,
+      "ms_per_query": 3.87095416,
+      "gb_per_s": 7.936027844876365,
+      "recall_at_k": 1.0
+    },
+    {
+      "codec": "binary-hamming",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 0.21519831999999997,
+      "gb_per_s": 4.4610013684121705,
+      "recall_at_k": 0.416
+    },
+    {
+      "codec": "pq48",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 3.8941666600000002,
+      "gb_per_s": 0.24652257692535431,
+      "recall_at_k": 0.6
+    }
+  ],
+  "bandwidth": [
+    {
+      "threads": 1,
+      "gb_per_s": 87.51428553730553
+    }
+  ]
+}
diff --git a/experiments/2026-11-text-format/results/scan/table-minilm.json b/experiments/2026-11-text-format/results/scan/table-minilm.json
new file mode 100644
index 0000000..bfcac4f
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/table-minilm.json
@@ -0,0 +1,59 @@
+{
+  "collection": "table-minilm",
+  "vectors": 20000,
+  "dimension": 384,
+  "queries": 50,
+  "k": 10,
+  "repeat": 3,
+  "paths": [
+    {
+      "path": "cached buffer (Store::search)",
+      "ms_per_query": 3.9845399999999995,
+      "note": "codes scanned in place in one allocation; text read for the hits only"
+    }
+  ],
+  "loads": [
+    {
+      "source": "sqlite (borrowed blobs, one copy)",
+      "ms": 11.925042,
+      "bytes": 30720000
+    },
+    {
+      "source": "flat code file (one read)",
+      "ms": 2.599334,
+      "bytes": 30720000
+    }
+  ],
+  "codecs": [
+    {
+      "codec": "f32",
+      "bytes_per_vector": 1536,
+      "threads": 1,
+      "ms_per_query": 3.9864966600000002,
+      "gb_per_s": 7.706014232556764,
+      "recall_at_k": 1.0
+    },
+    {
+      "codec": "binary-hamming",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 0.2051525,
+      "gb_per_s": 4.679445778140652,
+      "recall_at_k": 0.466
+    },
+    {
+      "codec": "pq48",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 3.9615424999999997,
+      "gb_per_s": 0.24232985005209462,
+      "recall_at_k": 0.69
+    }
+  ],
+  "bandwidth": [
+    {
+      "threads": 1,
+      "gb_per_s": 87.54759396588443
+    }
+  ]
+}
diff --git a/experiments/2026-11-text-format/results/scan/wiki-bge-small.json b/experiments/2026-11-text-format/results/scan/wiki-bge-small.json
new file mode 100644
index 0000000..642d579
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/wiki-bge-small.json
@@ -0,0 +1,59 @@
+{
+  "collection": "wiki-bge-small",
+  "vectors": 20000,
+  "dimension": 384,
+  "queries": 50,
+  "k": 10,
+  "repeat": 3,
+  "paths": [
+    {
+      "path": "cached buffer (Store::search)",
+      "ms_per_query": 4.089700000000001,
+      "note": "codes scanned in place in one allocation; text read for the hits only"
+    }
+  ],
+  "loads": [
+    {
+      "source": "sqlite (borrowed blobs, one copy)",
+      "ms": 12.255749999999999,
+      "bytes": 30720000
+    },
+    {
+      "source": "flat code file (one read)",
+      "ms": 3.171333,
+      "bytes": 30720000
+    }
+  ],
+  "codecs": [
+    {
+      "codec": "f32",
+      "bytes_per_vector": 1536,
+      "threads": 1,
+      "ms_per_query": 4.08832416,
+      "gb_per_s": 7.514081270894136,
+      "recall_at_k": 1.0
+    },
+    {
+      "codec": "binary-hamming",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 0.20190416,
+      "gb_per_s": 4.7547311556136345,
+      "recall_at_k": 0.32
+    },
+    {
+      "codec": "pq48",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 3.9898149999999997,
+      "gb_per_s": 0.24061265998548806,
+      "recall_at_k": 0.592
+    }
+  ],
+  "bandwidth": [
+    {
+      "threads": 1,
+      "gb_per_s": 89.405232646052
+    }
+  ]
+}
diff --git a/experiments/2026-11-text-format/results/scan/wiki-minilm.json b/experiments/2026-11-text-format/results/scan/wiki-minilm.json
new file mode 100644
index 0000000..50cbab4
--- /dev/null
+++ b/experiments/2026-11-text-format/results/scan/wiki-minilm.json
@@ -0,0 +1,59 @@
+{
+  "collection": "wiki-minilm",
+  "vectors": 20000,
+  "dimension": 384,
+  "queries": 50,
+  "k": 10,
+  "repeat": 3,
+  "paths": [
+    {
+      "path": "cached buffer (Store::search)",
+      "ms_per_query": 4.033472499999999,
+      "note": "codes scanned in place in one allocation; text read for the hits only"
+    }
+  ],
+  "loads": [
+    {
+      "source": "sqlite (borrowed blobs, one copy)",
+      "ms": 12.746458,
+      "bytes": 30720000
+    },
+    {
+      "source": "flat code file (one read)",
+      "ms": 2.7811250000000003,
+      "bytes": 30720000
+    }
+  ],
+  "codecs": [
+    {
+      "codec": "f32",
+      "bytes_per_vector": 1536,
+      "threads": 1,
+      "ms_per_query": 4.0176016599999995,
+      "gb_per_s": 7.64635287411744,
+      "recall_at_k": 1.0
+    },
+    {
+      "codec": "binary-hamming",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 0.20049583999999998,
+      "gb_per_s": 4.78812926991403,
+      "recall_at_k": 0.422
+    },
+    {
+      "codec": "pq48",
+      "bytes_per_vector": 48,
+      "threads": 1,
+      "ms_per_query": 3.9473649999999996,
+      "gb_per_s": 0.24320021077351603,
+      "recall_at_k": 0.652
+    }
+  ],
+  "bandwidth": [
+    {
+      "threads": 1,
+      "gb_per_s": 86.39816051584862
+    }
+  ]
+}
diff --git a/experiments/2026-11-text-format/results/stats.json b/experiments/2026-11-text-format/results/stats.json
new file mode 100644
index 0000000..37e9ea5
--- /dev/null
+++ b/experiments/2026-11-text-format/results/stats.json
@@ -0,0 +1,57 @@
+{
+  "records": 20000,
+  "lookup_queries": 500,
+  "descriptive_queries": 500,
+  "seed": 2026,
+  "formats": {
+    "paragraph": {
+      "mean_bytes": 503.7568,
+      "mean_words": 79.5741,
+      "mean_tokens": 114.0377,
+      "p99_tokens": 231,
+      "over_400_tokens": 1
+    },
+    "bullets": {
+      "mean_bytes": 334.27645,
+      "mean_words": 60.1161,
+      "mean_tokens": 74.5787,
+      "p99_tokens": 155,
+      "over_400_tokens": 0
+    },
+    "kv": {
+      "mean_bytes": 322.8965,
+      "mean_words": 49.73615,
+      "mean_tokens": 73.5787,
+      "p99_tokens": 154,
+      "over_400_tokens": 0
+    },
+    "json": {
+      "mean_bytes": 366.6407,
+      "mean_words": 46.3079,
+      "mean_tokens": 113.895,
+      "p99_tokens": 216,
+      "over_400_tokens": 0
+    },
+    "table": {
+      "mean_bytes": 392.1762,
+      "mean_words": 73.49555,
+      "mean_tokens": 119.0985,
+      "p99_tokens": 221,
+      "over_400_tokens": 0
+    },
+    "wiki": {
+      "mean_bytes": 376.23385,
+      "mean_words": 71.19245,
+      "mean_tokens": 73.91285,
+      "p99_tokens": 158,
+      "over_400_tokens": 0
+    },
+    "header-prose": {
+      "mean_bytes": 701.13035,
+      "mean_words": 120.9286,
+      "mean_tokens": 147.49155,
+      "p99_tokens": 283,
+      "over_400_tokens": 0
+    }
+  }
+}
diff --git a/experiments/2026-11-text-format/results/summary.txt b/experiments/2026-11-text-format/results/summary.txt
new file mode 100644
index 0000000..60b08a2
--- /dev/null
+++ b/experiments/2026-11-text-format/results/summary.txt
@@ -0,0 +1,72 @@
+== bge-small, descriptive queries
+format        tokens f32 nDCG    band binary-hamming n/b  N@95%           pq48 n/b  N@95%
+paragraph        114   0.1735  0.0110              4.579    754              1.247     78
+bullets           75   0.1797  0.0101              4.819    825              1.253     74
+kv                74   0.1907  0.0099              4.709    722              1.205     69
+json             114   0.2238  0.0093              4.782    505              1.079     62
+table            119   0.2114  0.0095              4.679    476              1.171     68
+wiki              74   0.1131  0.0116              4.467    611              1.261     70
+header-prose     147   0.1575  0.0108              4.759    606              1.277     69
+best nDCG@10 within a time budget (exact f32 scan ≈ 4.10 ms/query, one thread)
+    10% (0.41 ms): json 0.2227 (binary-hamming 3.0σ, 0.28 ms); table 0.2128 (binary-hamming 3.0σ, 0.29 ms); kv 0.1906 (binary-hamming 3.0σ, 0.33 ms)
+    25% (1.03 ms): json 0.2227 (binary-hamming 3.0σ, 0.28 ms); table 0.2128 (binary-hamming 3.0σ, 0.29 ms); kv 0.1906 (binary-hamming 3.0σ, 0.33 ms)
+    50% (2.05 ms): json 0.2227 (binary-hamming 3.0σ, 0.28 ms); table 0.2128 (binary-hamming 3.0σ, 0.29 ms); kv 0.1906 (binary-hamming 3.0σ, 0.33 ms)
+   100% (4.10 ms): json 0.2227 (binary-hamming 3.0σ, 0.28 ms); table 0.2128 (binary-hamming 3.0σ, 0.29 ms); kv 0.1908 (pq48 N=200, 4.03 ms)
+
+== bge-small, lookup queries
+format        tokens f32 nDCG    band binary-hamming n/b  N@95%           pq48 n/b  N@95%
+paragraph        114   0.3700  0.0100              4.915    966              1.409     98
+bullets           75   0.4018  0.0097              4.985    860              1.377     85
+kv                74   0.4353  0.0095              4.947    947              1.337     78
+json             114   0.4407  0.0082              5.386    760              1.298     75
+table            119   0.4154  0.0095              4.683    712              1.228     71
+wiki              74   0.1810  0.0108              4.530    804              1.313     81
+header-prose     147   0.3702  0.0106              4.675    835              1.369     88
+best nDCG@10 within a time budget (exact f32 scan ≈ 4.10 ms/query, one thread)
+    10% (0.41 ms): json 0.4395 (binary-hamming 3.0σ, 0.30 ms); kv 0.4320 (binary-hamming N=1000, 0.41 ms); table 0.4156 (binary-hamming N=1000, 0.41 ms)
+    25% (1.03 ms): json 0.4411 (binary-hamming N=1000, 0.43 ms); kv 0.4320 (binary-hamming N=1000, 0.41 ms); table 0.4156 (binary-hamming N=1000, 0.41 ms)
+    50% (2.05 ms): json 0.4411 (binary-hamming N=1000, 0.43 ms); kv 0.4320 (binary-hamming N=1000, 0.41 ms); table 0.4156 (binary-hamming N=1000, 0.41 ms)
+   100% (4.10 ms): json 0.4411 (binary-hamming N=1000, 0.43 ms); kv 0.4353 (exact f32, 4.06 ms); table 0.4156 (binary-hamming N=1000, 0.41 ms)
+
+== minilm, descriptive queries
+format        tokens f32 nDCG    band binary-hamming n/b  N@95%           pq48 n/b  N@95%
+paragraph        114   0.1295  0.0213              2.285    246              1.005     40
+bullets           75   0.1613  0.0195              2.316    315              0.981     42
+kv                74   0.1433  0.0198              2.326    312              0.953     44
+json             114   0.1422  0.0201              2.119    291              0.873     36
+table            119   0.1808  0.0181              2.255    228              0.931     42
+wiki              74   0.0909  0.0211              2.326    270              1.047     45
+header-prose     147   0.1359  0.0212              2.309    225              1.028     44
+best nDCG@10 within a time budget (exact f32 scan ≈ 4.04 ms/query, one thread)
+    10% (0.40 ms): table 0.1811 (binary-hamming N=200, 0.25 ms); bullets 0.1604 (binary-hamming N=1000, 0.39 ms); kv 0.1421 (binary-hamming N=500, 0.30 ms)
+    25% (1.01 ms): table 0.1811 (binary-hamming N=200, 0.25 ms); bullets 0.1604 (binary-hamming N=1000, 0.39 ms); kv 0.1424 (binary-hamming N=1000, 0.40 ms)
+    50% (2.02 ms): table 0.1811 (binary-hamming N=200, 0.25 ms); bullets 0.1604 (binary-hamming N=1000, 0.39 ms); kv 0.1424 (binary-hamming N=1000, 0.40 ms)
+   100% (4.04 ms): table 0.1811 (binary-hamming N=200, 0.25 ms); bullets 0.1613 (exact f32, 3.91 ms); kv 0.1433 (pq48 N=200, 4.02 ms)
+
+== minilm, lookup queries
+format        tokens f32 nDCG    band binary-hamming n/b  N@95%           pq48 n/b  N@95%
+paragraph        114   0.1793  0.0169              2.801    367              1.169     52
+bullets           75   0.3359  0.0160              2.770    407              1.137     47
+kv                74   0.2755  0.0164              2.742    441              1.094     51
+json             114   0.2401  0.0165              2.573    317              1.033     44
+table            119   0.3533  0.0157              2.647    398              1.057     45
+wiki              74   0.0980  0.0185              2.672    365              1.179     49
+header-prose     147   0.2082  0.0185              2.606    330              1.122     46
+best nDCG@10 within a time budget (exact f32 scan ≈ 4.04 ms/query, one thread)
+    10% (0.40 ms): table 0.3536 (binary-hamming N=500, 0.30 ms); bullets 0.3372 (binary-hamming 2.0σ, 0.23 ms); kv 0.2768 (binary-hamming N=500, 0.30 ms)
+    25% (1.01 ms): table 0.3536 (binary-hamming N=500, 0.30 ms); bullets 0.3372 (binary-hamming 2.0σ, 0.23 ms); kv 0.2768 (binary-hamming N=500, 0.30 ms)
+    50% (2.02 ms): table 0.3536 (binary-hamming N=500, 0.30 ms); bullets 0.3372 (binary-hamming 2.0σ, 0.23 ms); kv 0.2768 (binary-hamming N=500, 0.30 ms)
+   100% (4.04 ms): table 0.3536 (binary-hamming N=500, 0.30 ms); bullets 0.3372 (binary-hamming 2.0σ, 0.23 ms); kv 0.2768 (binary-hamming N=500, 0.30 ms)
+
+Cross-format checks, leaving one format out at a time
+  depth for 90% recall: Spearman ρ(noise/band, depth) = 0.985, depth ≈ 3.08·k·r^1.70, held-out format within a factor of 1.14 on average (56 points)
+  depth for 95% recall: Spearman ρ(noise/band, depth) = 0.985, depth ≈ 4.74·k·r^1.83, held-out format within a factor of 1.16 on average (56 points)
+  depth for 99% recall: Spearman ρ(noise/band, depth) = 0.968, depth ≈ 10.59·k·r^1.98, held-out format within a factor of 1.24 on average (56 points)
+  hybrid nDCG at N=10: predicted from f32 nDCG and noise/band within 0.0067 on a held-out format (f32 nDCG times the mean retained share: 0.0181)
+  hybrid nDCG at N=20: predicted from f32 nDCG and noise/band within 0.0060 on a held-out format (f32 nDCG times the mean retained share: 0.0143)
+  hybrid nDCG at N=30: predicted from f32 nDCG and noise/band within 0.0047 on a held-out format (f32 nDCG times the mean retained share: 0.0117)
+  hybrid nDCG at N=50: predicted from f32 nDCG and noise/band within 0.0032 on a held-out format (f32 nDCG times the mean retained share: 0.0081)
+  hybrid nDCG at N=100: predicted from f32 nDCG and noise/band within 0.0026 on a held-out format (f32 nDCG times the mean retained share: 0.0048)
+  hybrid nDCG at N=200: predicted from f32 nDCG and noise/band within 0.0016 on a held-out format (f32 nDCG times the mean retained share: 0.0025)
+  hybrid nDCG at N=500: predicted from f32 nDCG and noise/band within 0.0008 on a held-out format (f32 nDCG times the mean retained share: 0.0010)
+  hybrid nDCG at N=1000: predicted from f32 nDCG and noise/band within 0.0005 on a held-out format (f32 nDCG times the mean retained share: 0.0006)
diff --git a/experiments/2026-11-text-format/run.sh b/experiments/2026-11-text-format/run.sh
new file mode 100755
index 0000000..0e2d9b1
--- /dev/null
+++ b/experiments/2026-11-text-format/run.sh
@@ -0,0 +1,88 @@
+#!/usr/bin/env bash
+# Text format: the same WikiBio records written as prose, bullets, key-value, JSON, a table row
+# and a key-value header above prose, plus each record's own Wikipedia paragraph. For each format,
+# query type (lookup, descriptive) and embedding model: exact f32 nDCG@10, the neighbour band and
+# each codec's rank noise, the re-score depth and nDCG behind a binary-hamming or pq48 scan, and
+# the per-vector scan cost, so summarise.py can draw accuracy against time per query.
+# Retrieval only, no chat model, no API key. One record per chunk (`ingest --whole-files`), so
+# every format has the same number of vectors.
+# Needs: a built `hvec` (cargo build --release), curl, and `uv` (to read WikiBio's parquet file).
+# Downloads 41 MB on first run.
+set -euo pipefail
+HERE="$(cd "$(dirname "$0")" && pwd)"
+ROOT="$HERE/../.."
+HVEC="${HVEC:-$ROOT/target/release/hvec}"
+WORK="${WORK:-$HERE/work}"
+RESULTS="${RESULTS:-$HERE/results}"
+K="${K:-10}"
+RECORDS="${RECORDS:-20000}"
+QUERIES="${QUERIES:-500}"
+SEED="${SEED:-2026}"
+FORMATS="${FORMATS:-paragraph bullets kv json table wiki header-prose}"
+MODELS="${MODELS:-bge-small minilm}"
+QTYPES="${QTYPES:-lookup descriptive}"
+CODECS="${CODECS:-int8,binary,binary-hamming,pq48}"
+FIRST="${FIRST:-binary-hamming,pq48}"
+DEPTHS="${DEPTHS:-10,20,30,50,100,200,500,1000}"
+mkdir -p "$WORK/dl" "$RESULTS/recall" "$RESULTS/rescore" "$RESULTS/scan"
+
+PARQUET="$WORK/dl/wikibio-test.parquet"
+[ -f "$PARQUET" ] || curl -sfL -o "$PARQUET" \
+  https://huggingface.co/datasets/thoughtworks/wiki_bio/resolve/main/data/test-00000-of-00001.parquet
+[ -f "$WORK/beir/stats.json" ] || (cd "$WORK" && uv run -q --no-project --with pyarrow python -I \
+  "$HERE/wikibio_to_formats.py" "$PARQUET" "$WORK/beir" "$RECORDS" "$QUERIES" "$SEED")
+cp "$WORK/beir/stats.json" "$WORK/beir/examples.md" "$RESULTS/"
+
+export HVEC_CONFIG="$WORK/config.toml"
+cat > "$HVEC_CONFIG" <<CFG
+db_path = "$WORK/hvec.db"
+default_chat = "anthropic"
+default_embedder = "bge-small"
+metric = "cosine"
+
+[chat.anthropic]
+provider = "anthropic"
+model = "claude-opus-5-5"
+api_key_env = "ANTHROPIC_API_KEY"
+
+[embed.bge-small]
+provider = "local"
+model = "BGESmallENV15"
+
+[embed.minilm]
+provider = "local"
+model = "AllMiniLML6V2"
+CFG
+
+{
+  echo "hvec $(git -C "$ROOT" rev-parse --short HEAD)$(git -C "$ROOT" diff --quiet || echo '+dirty')"
+  sysctl -n machdep.cpu.brand_string 2>/dev/null || true
+  sysctl -n hw.memsize 2>/dev/null | awk '{printf "memory: %.0f GB\n", $1/1e9}' || true
+} > "$RESULTS/machine.txt"
+
+for f in $FORMATS; do
+  for q in $QTYPES; do  # the question sets are the same for every format; the corpus is not
+    [ -f "$WORK/questions-$q.jsonl" ] && [ -d "$WORK/corpus-$f" ] && continue
+    "$HVEC" questions import-beir "$WORK/beir/$f" --split "$q" --out "$WORK/questions-$q.jsonl" \
+      --corpus-out "$WORK/corpus-$f"
+  done
+  for m in $MODELS; do
+    col="$f-$m"
+    if ! "$HVEC" collections list | grep -q "^$col "; then
+      "$HVEC" ingest "$WORK/corpus-$f" --collection "$col" --embedder "$m" --ext txt --whole-files --batch 64
+    fi
+    for q in $QTYPES; do
+      "$HVEC" bench recall --collection "$col" -k "$K" --codecs "$CODECS" \
+        --questions "$WORK/questions-$q.jsonl" --json > "$RESULTS/recall/$col-$q.json"
+      "$HVEC" bench rescore --collection "$col" -k "$K" --codecs "$FIRST" --rescore f32 \
+        --depths "$DEPTHS" --questions "$WORK/questions-$q.jsonl" --json > "$RESULTS/rescore/$col-$q.json"
+    done
+    # Per-vector scan cost, one thread. It does not depend on the text, only on the codec and the
+    # vector count, which is the same for every format; measured per collection as a check.
+    "$HVEC" bench scan --collection "$col" --codecs "f32,${FIRST}" --threads 1 --skip-sqlite \
+      --bandwidth-mib 256 --json > "$RESULTS/scan/$col.json"
+    echo "== $col done"
+  done
+done
+cp "$HVEC_CONFIG" "$RESULTS/config.toml"
+python3 -I "$HERE/summarise.py" "$RESULTS" "$K" | tee "$RESULTS/summary.txt"
diff --git a/experiments/2026-11-text-format/summarise.py b/experiments/2026-11-text-format/summarise.py
new file mode 100644
index 0000000..4f70134
--- /dev/null
+++ b/experiments/2026-11-text-format/summarise.py
@@ -0,0 +1,198 @@
+"""Summarise the text-format run: accuracy against time per query, per format and context.
+
+    python3 -I summarise.py <results-dir> <k>
+
+Reads recall/<format>-<model>-<qtype>.json (bench recall), rescore/<...>.json (bench rescore),
+scan/<format>-<model>.json (bench scan) and stats.json (record sizes). A context is an
+(embedding model, query type) pair. For each context it prints:
+
+1. Per format: tokens per record, exact f32 nDCG@k, the neighbour band, each first stage's noise
+   over band and the depth an f32 re-score needs for 95% recall@k behind it.
+2. The frontier: every operating point (exact f32; a first-stage scan alone; a scan plus an f32
+   re-score of N candidates, fixed or adaptive), timed as scan + N x the f32 cost per vector on
+   one thread, and the best format at budgets of 10, 25, 50 and 100% of an exact f32 scan.
+
+Then the two cross-format checks:
+- depth: does noise / band predict the re-score depth on a format it was not fitted on?
+- accuracy: does (f32 nDCG, noise / band) predict a hybrid's nDCG on a held-out format?
+"""
+
+import json
+import math
+import sys
+from collections import defaultdict
+from pathlib import Path
+
+QTYPES = ("lookup", "descriptive")
+BUDGETS = (0.10, 0.25, 0.50, 1.00)
+
+
+def ranks(xs):
+    order = sorted(range(len(xs)), key=lambda i: xs[i])
+    r = [0.0] * len(xs)
+    i = 0
+    while i < len(order):
+        j = i
+        while j + 1 < len(order) and xs[order[j + 1]] == xs[order[i]]:
+            j += 1
+        for t in range(i, j + 1):
+            r[order[t]] = (i + j) / 2 + 1
+        i = j + 1
+    return r
+
+
+def pearson(a, b):
+    n = len(a)
+    ma, mb = sum(a) / n, sum(b) / n
+    cov = sum((x - ma) * (y - mb) for x, y in zip(a, b))
+    va = sum((x - ma) ** 2 for x in a) ** 0.5
+    vb = sum((y - mb) ** 2 for y in b) ** 0.5
+    return cov / (va * vb) if va and vb else float("nan")
+
+
+def fit(xs, ys):
+    """Least squares y = a + b x."""
+    n = len(xs)
+    mx, my = sum(xs) / n, sum(ys) / n
+    sxx = sum((x - mx) ** 2 for x in xs)
+    b = sum((x - mx) * (y - my) for x, y in zip(xs, ys)) / sxx if sxx else 0.0
+    return my - b * mx, b
+
+
+def split_name(stem, formats):
+    """`<format>-<model>[-<qtype>]` -> (format, model, qtype or None). Formats may contain '-'."""
+    qtype = None
+    for q in QTYPES:
+        if stem.endswith("-" + q):
+            qtype, stem = q, stem[: -len(q) - 1]
+    fmt = max((f for f in formats if stem.startswith(f + "-")), key=len)
+    return fmt, stem[len(fmt) + 1:], qtype
+
+
+def policy_name(p):
+    return {"none": "scan only", "fixed": f"N={p.get('depth')}", "margin": f"{p.get('sigmas')}σ"}[p["kind"]]
+
+
+def main(results: Path, k: int) -> None:
+    stats = json.loads((results / "stats.json").read_text())
+    formats = list(stats["formats"])
+
+    cost = {}  # (format, model) -> {codec: ms per query, "f32_ns": ns per vector, "vectors": n}
+    for p in sorted((results / "scan").glob("*.json")):
+        r = json.loads(p.read_text())
+        fmt, model, _ = split_name(p.stem, formats)
+        c = {x["codec"]: x["ms_per_query"] for x in r["codecs"] if x["threads"] == 1}
+        c["f32_ns"] = c["f32"] * 1e6 / r["vectors"]
+        c["vectors"] = r["vectors"]
+        cost[(fmt, model)] = c
+
+    rows = defaultdict(dict)  # (model, qtype) -> format -> row
+    profiles = []  # one per (format, model, qtype, first stage), for the cross-format checks
+    for p in sorted((results / "recall").glob("*.json")):
+        fmt, model, qtype = split_name(p.stem, formats)
+        if (fmt, model) not in cost:
+            continue
+        c = cost[(fmt, model)]
+        recall = {r["codec"]: r for r in json.loads(p.read_text())}
+        resc = json.loads((results / "rescore" / p.name).read_text())
+        f32 = recall["f32"]
+        row = {
+            "tokens": stats["formats"][fmt]["mean_tokens"],
+            "ndcg": f32["ndcg_at_k"],
+            "band": f32["neighbour_band"],
+            "ratio": {n: r["rank_noise"] / f32["neighbour_band"] for n, r in recall.items() if n != "f32"},
+            "depth95": {},
+            "points": [("exact f32", c["f32"], f32["ndcg_at_k"])],
+        }
+        for pr in resc["profiles"]:
+            d = dict((t, n) for t, n in pr["depth_for_recall"])
+            row["depth95"][pr["first"]] = d.get(0.95)
+            profiles.append({"format": fmt, "model": model, "qtype": qtype, "first": pr["first"],
+                             "ratio": pr["rank_noise"] / pr["neighbour_band"], "depth": d, "f32_ndcg": f32["ndcg_at_k"],
+                             "cells": {}})
+        by_first = {pp["first"]: pp for pp in profiles[-len(resc["profiles"]):]} if resc["profiles"] else {}
+        for cell in resc["cells"]:
+            if cell["first"] not in c:
+                continue
+            ms = c[cell["first"]] + cell["mean_depth"] * c["f32_ns"] / 1e6
+            row["points"].append((f"{cell['first']} {policy_name(cell['policy'])}", ms, cell["ndcg_at_k"]))
+            if cell["policy"]["kind"] == "fixed" and cell["first"] in by_first:
+                by_first[cell["first"]]["cells"][cell["policy"]["depth"]] = cell["ndcg_at_k"]
+        row["f32_ms"] = c["f32"]
+        rows[(model, qtype)][fmt] = row
+
+    firsts = sorted({p["first"] for p in profiles})
+    for (model, qtype), fr in sorted(rows.items()):
+        print(f"== {model}, {qtype} queries")
+        head = f"{'format':<13} {'tokens':>6} {'f32 nDCG':>8} {'band':>7}"
+        for f in firsts:
+            head += f" {f + ' n/b':>18} {'N@95%':>6}"
+        print(head)
+        for fmt in formats:
+            if fmt not in fr:
+                continue
+            r = fr[fmt]
+            line = f"{fmt:<13} {r['tokens']:>6.0f} {r['ndcg']:>8.4f} {r['band']:>7.4f}"
+            for f in firsts:
+                line += f" {r['ratio'].get(f, float('nan')):>18.3f} {r['depth95'].get(f) or '-':>6}"
+            print(line)
+        f32_ms = sum(r["f32_ms"] for r in fr.values()) / len(fr)
+        print(f"best nDCG@{k} within a time budget (exact f32 scan ≈ {f32_ms:.2f} ms/query, one thread)")
+        for b in BUDGETS:
+            limit = b * f32_ms
+            best = []
+            for fmt, r in fr.items():
+                pts = [p for p in r["points"] if p[1] <= limit]
+                if pts:
+                    name, ms, nd = max(pts, key=lambda p: p[2])
+                    best.append((nd, fmt, name, ms))
+            best.sort(reverse=True)
+            cells = "; ".join(f"{fmt} {nd:.4f} ({name}, {ms:.2f} ms)" for nd, fmt, name, ms in best[:3])
+            print(f"  {100 * b:>4.0f}% ({limit:.2f} ms): {cells or 'nothing fits'}")
+        print()
+
+    if len({p["format"] for p in profiles}) < 3:
+        return
+    print("Cross-format checks, leaving one format out at a time")
+    for t in (0.9, 0.95, 0.99):
+        pts = [p for p in profiles if p["depth"].get(t)]
+        xs = [math.log(p["ratio"]) for p in pts]
+        ys = [math.log(max(p["depth"][t], k) / k) for p in pts]
+        errs = []
+        for fmt in formats:
+            tr = [(x, y) for x, y, p in zip(xs, ys, pts) if p["format"] != fmt]
+            te = [(x, y) for x, y, p in zip(xs, ys, pts) if p["format"] == fmt]
+            if len(tr) < 3 or not te:
+                continue
+            a, b = fit(*zip(*tr))
+            errs += [abs(a + b * x - y) for x, y in te]
+        a, b = fit(xs, ys)
+        print(f"  depth for {t:.0%} recall: Spearman ρ(noise/band, depth) = {pearson(ranks(xs), ranks(ys)):.3f}, "
+              f"depth ≈ {math.exp(a):.2f}·k·r^{b:.2f}, held-out format within a factor of "
+              f"{math.exp(sum(errs) / len(errs)):.2f} on average ({len(pts)} points)")
+    depths = sorted({d for p in profiles for d in p["cells"]})
+    for d in depths:
+        pts = [p for p in profiles if d in p["cells"] and p["f32_ndcg"]]
+        if len(pts) < 4:
+            continue
+        # The share of exact f32's nDCG a hybrid keeps, against log noise/band.
+        xs = [math.log(p["ratio"]) for p in pts]
+        ys = [p["cells"][d] / p["f32_ndcg"] for p in pts]
+        errs, naive = [], []
+        for fmt in formats:
+            tr = [(x, y) for x, y, p in zip(xs, ys, pts) if p["format"] != fmt]
+            te = [(x, y, p) for x, y, p in zip(xs, ys, pts) if p["format"] == fmt]
+            if len(tr) < 3 or not te:
+                continue
+            a, b = fit(*zip(*tr))
+            mean = sum(y for _, y in tr) / len(tr)
+            for x, y, p in te:
+                errs.append(abs((a + b * x - y) * p["f32_ndcg"]))
+                naive.append(abs((mean - y) * p["f32_ndcg"]))
+        if errs:
+            print(f"  hybrid nDCG at N={d}: predicted from f32 nDCG and noise/band within {sum(errs) / len(errs):.4f} "
+                  f"on a held-out format (f32 nDCG times the mean retained share: {sum(naive) / len(naive):.4f})")
+
+
+if __name__ == "__main__":
+    main(Path(sys.argv[1]), int(sys.argv[2]) if len(sys.argv) > 2 else 10)
diff --git a/experiments/2026-11-text-format/wikibio_to_formats.py b/experiments/2026-11-text-format/wikibio_to_formats.py
new file mode 100644
index 0000000..69a121e
--- /dev/null
+++ b/experiments/2026-11-text-format/wikibio_to_formats.py
@@ -0,0 +1,230 @@
+"""Write one WikiBio sample as several BEIR datasets, the same records in a different layout each.
+
+    python3 -I wikibio_to_formats.py <test.parquet> <out-dir> [records] [queries-per-type] [seed]
+
+Every format but `wiki` and `header-prose` holds exactly the same facts: the cleaned infobox
+fields of each record. `wiki` is the record's own Wikipedia paragraph, which holds different
+facts (it is the natural-prose reference), and `header-prose` is the infobox as a key-value
+header above that paragraph.
+
+The queries and judgments are the same for every format, from the infobox:
+- `lookup`: two of a record's attributes ("who was born in X and played as Y?"), relevant
+  to every record with both values, kept only when that is at most 3 records.
+- `descriptive`: one attribute value shared by 5 to 50 records ("people who worked as X"),
+  relevant to all of them.
+
+Writes `<out>/<format>/{corpus.jsonl,queries.jsonl,qrels/lookup.tsv,qrels/descriptive.tsv}`,
+`<out>/stats.json` (size per record per format) and `<out>/examples.md` (one record in every
+format). Needs pyarrow.
+"""
+
+import json
+import random
+import re
+import sys
+from collections import defaultdict
+from pathlib import Path
+
+import pyarrow.parquet as pq
+
+FORMATS = ["paragraph", "bullets", "kv", "json", "table", "wiki", "header-prose"]
+
+# Fields with no retrievable content (images, layout, update stamps).
+DROP = {
+    "article_title", "image", "image_size", "imagesize", "caption", "alt", "website", "signature",
+    "pcupdate", "ntupdate", "image_caption", "logo", "landscape", "module", "footnotes",
+}
+MAX_FIELDS = 16
+MAX_VALUE_WORDS = 25
+MAX_WIKI_WORDS = 150
+JUNK_VALUES = {"", "unknown", "none", "n/a", "--", "-", "?"}
+
+# Query phrasing. Deliberately not the field labels the documents use, so no format gets the
+# query's own words for free.
+PHRASES = {
+    "birth_date": "was born on {}",
+    "birth_place": "was born in {}",
+    "death_date": "died on {}",
+    "death_place": "died in {}",
+    "position": "played as {}",
+    "occupation": "worked as {}",
+    "nationality": "had {} nationality",
+    "party": "belonged to the {} party",
+    "alma_mater": "studied at {}",
+    "genre": "worked in {}",
+    "religion": "followed {}",
+    "spouse": "was married to {}",
+    "clubs": "played for {}",
+    "residence": "lived in {}",
+}
+DESCRIPTIVE_FIELDS = ["occupation", "position", "nationality", "party", "genre", "religion",
+                      "birth_place", "death_place", "alma_mater", "residence"]
+
+PTB = {"-lrb-": "(", "-rrb-": ")", "-lsb-": "[", "-rsb-": "]", "-lcb-": "{", "-rcb-": "}",
+       "``": '"', "''": '"'}
+TOKEN = re.compile(r"\w+|[^\w\s]")
+
+
+def clean(s: str) -> str:
+    for k, v in PTB.items():
+        s = s.replace(k, v)
+    return " ".join(s.split())
+
+
+def record(row):
+    t = row["input_text"]["table"]
+    fields = {}
+    for h, v in zip(t["column_header"], t["content"]):
+        h = h.strip()
+        v = clean(v)
+        if h in DROP or h.endswith("image") or h.endswith("caption") or h in fields:
+            continue
+        if v.lower() in JUNK_VALUES or len(v.split()) > MAX_VALUE_WORDS:
+            continue
+        fields[h] = v
+    name = fields.pop("name", None)
+    if not name and "article_title" in t["column_header"]:
+        title = clean(t["content"][t["column_header"].index("article_title")])
+        name = re.sub(r"\s*\(.*?\)\s*$", "", title)
+    if not name:
+        return None
+    fields = dict(list(fields.items())[: MAX_FIELDS - 1])
+    if len(fields) < 3:
+        return None
+    words, wiki = 0, []
+    for sent in row["target_text"].split("\n"):
+        sent = clean(sent)
+        if not sent:
+            continue
+        if wiki and words + len(sent.split()) > MAX_WIKI_WORDS:
+            break
+        wiki.append(sent)
+        words += len(sent.split())
+    return {"name": name, "fields": fields, "wiki": " ".join(wiki)}
+
+
+def label(h: str) -> str:
+    return h.replace("_", " ")
+
+
+def render(r, fmt: str) -> str:
+    name, f = r["name"], r["fields"]
+    items = [("name", name)] + list(f.items())
+    kv = "; ".join(f"{label(h)}: {v}" for h, v in items)
+    if fmt == "paragraph":
+        return " ".join([f"{name}."] + [f"{name}'s {label(h)} is {v}." for h, v in f.items()])
+    if fmt == "bullets":
+        return "\n".join(f"- {label(h)}: {v}" for h, v in items)
+    if fmt == "kv":
+        return kv
+    if fmt == "json":
+        return json.dumps(dict(items), ensure_ascii=False)
+    if fmt == "table":
+        head = "| " + " | ".join(label(h) for h, _ in items) + " |"
+        rule = "|" + "---|" * len(items)
+        row = "| " + " | ".join(v.replace("|", "/") for _, v in items) + " |"
+        return "\n".join([head, rule, row])
+    if fmt == "wiki":
+        return r["wiki"]
+    if fmt == "header-prose":
+        return kv + "\n\n" + r["wiki"]
+    raise ValueError(fmt)
+
+
+def make_queries(recs, ids, n, rng):
+    by_value = defaultdict(set)
+    for i, r in zip(ids, recs):
+        for h, v in r["fields"].items():
+            if h in PHRASES:
+                by_value[(h, v)].add(i)
+
+    lookup, seen = [], set()
+    order = list(range(len(recs)))
+    rng.shuffle(order)
+    for j in order:
+        if len(lookup) >= n:
+            break
+        cand = [h for h in recs[j]["fields"] if h in PHRASES]
+        if len(cand) < 2:
+            continue
+        a, b = sorted(rng.sample(cand, 2), key=list(PHRASES).index)
+        va, vb = recs[j]["fields"][a], recs[j]["fields"][b]
+        rel = by_value[(a, va)] & by_value[(b, vb)]
+        if len(rel) > 3 or (a, va, b, vb) in seen:
+            continue
+        seen.add((a, va, b, vb))
+        if (a, b) in (("birth_date", "birth_place"), ("death_date", "death_place")):
+            text = f"who {PHRASES[a].format(va)} in {vb}?"
+        else:
+            text = f"who {PHRASES[a].format(va)} and {PHRASES[b].format(vb)}?"
+        lookup.append((text, sorted(rel)))
+
+    cats = [(h, v) for (h, v), s in by_value.items() if h in DESCRIPTIVE_FIELDS and 5 <= len(s) <= 50]
+    cats.sort()
+    rng.shuffle(cats)
+    descriptive = [(f"people who {PHRASES[h].format(v)}", sorted(by_value[(h, v)])) for h, v in cats[:n]]
+    return lookup, descriptive
+
+
+def main(src: Path, out: Path, n_records: int, n_queries: int, seed: int) -> None:
+    rng = random.Random(seed)
+    rows = pq.read_table(src).to_pylist()
+    order = list(range(len(rows)))
+    rng.shuffle(order)
+    recs, ids = [], []
+    for i in order:
+        r = record(rows[i])
+        if r:
+            recs.append(r)
+            ids.append(f"wb{i:05d}")
+        if len(recs) >= n_records:
+            break
+    lookup, descriptive = make_queries(recs, ids, n_queries, rng)
+
+    queries = [{"_id": f"L{q:04d}", "text": t} for q, (t, _) in enumerate(lookup)]
+    queries += [{"_id": f"D{q:04d}", "text": t} for q, (t, _) in enumerate(descriptive)]
+    qrels = {
+        "lookup": [(f"L{q:04d}", d) for q, (_, rel) in enumerate(lookup) for d in rel],
+        "descriptive": [(f"D{q:04d}", d) for q, (_, rel) in enumerate(descriptive) for d in rel],
+    }
+
+    stats = {"records": len(recs), "lookup_queries": len(lookup), "descriptive_queries": len(descriptive),
+             "seed": seed, "formats": {}}
+    for fmt in FORMATS:
+        d = out / fmt
+        (d / "qrels").mkdir(parents=True, exist_ok=True)
+        texts = [render(r, fmt) for r in recs]
+        with open(d / "corpus.jsonl", "w") as fh:
+            for i, t in zip(ids, texts):
+                fh.write(json.dumps({"_id": i, "title": "", "text": t}, ensure_ascii=False) + "\n")
+        with open(d / "queries.jsonl", "w") as fh:
+            for q in queries:
+                fh.write(json.dumps(q, ensure_ascii=False) + "\n")
+        for split, rows_ in qrels.items():
+            with open(d / "qrels" / f"{split}.tsv", "w") as fh:
+                fh.write("query-id\tcorpus-id\tscore\n")
+                fh.writelines(f"{q}\t{c}\t1\n" for q, c in rows_)
+        toks = sorted(len(TOKEN.findall(t)) for t in texts)
+        stats["formats"][fmt] = {
+            "mean_bytes": sum(len(t.encode()) for t in texts) / len(texts),
+            "mean_words": sum(len(t.split()) for t in texts) / len(texts),
+            "mean_tokens": sum(toks) / len(toks),
+            "p99_tokens": toks[int(0.99 * (len(toks) - 1))],
+            "over_400_tokens": sum(t > 400 for t in toks),
+        }
+    (out / "stats.json").write_text(json.dumps(stats, indent=2) + "\n")
+
+    ex = recs[0]
+    md = [f"# One record ({ids[0]}) in every format\n"]
+    for fmt in FORMATS:
+        md += [f"## {fmt}\n", "```", render(ex, fmt), "```\n"]
+    md += ["## Queries\n"] + [f"- lookup: {t}" for t, _ in lookup[:3]] + [f"- descriptive: {t}" for t, _ in descriptive[:3]]
+    (out / "examples.md").write_text("\n".join(md) + "\n")
+    print(f"{len(recs)} records, {len(lookup)} lookup and {len(descriptive)} descriptive queries, "
+          f"{len(FORMATS)} formats in {out}")
+
+
+if __name__ == "__main__":
+    a = sys.argv[1:]
+    main(Path(a[0]), Path(a[1]), int(a[2]) if len(a) > 2 else 20000,
+         int(a[3]) if len(a) > 3 else 500, int(a[4]) if len(a) > 4 else 2026)