From 1aeb7af6df19feae879300b74e91c06e2b5d8483 Mon Sep 17 00:00:00 2001
From: Frank <frank@dankware>
Date: Tue, 20 Oct 2026 23:26:53 -0700
Subject: [PATCH] docs(probe): price the corpus chars/token ratio at run time
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The stored 3.28 chars/token (measured 2026-10-11) had drifted to 3.20 as the
repo's docs grew, so leg B sent ~201,900-token prompts and took a legitimate
400 from the engine's own window check — which reads as a capacity-contract
failure on a healthy endpoint. One ~4k-token request now prices the ratio, and
the leg sizes to 97 % of the deployed window.
---
 scripts/probe-frank-archon-kv.py | 21 ++++++++++++++++-----
 1 file changed, 16 insertions(+), 5 deletions(-)

diff --git a/scripts/probe-frank-archon-kv.py b/scripts/probe-frank-archon-kv.py
index a08b480..fdd5e82 100755
--- a/scripts/probe-frank-archon-kv.py
+++ b/scripts/probe-frank-archon-kv.py
@@ -147,10 +147,21 @@ def main():
     deep = corpus(160_000)
     wide = corpus(1_100_000)
 
-    # 3.28 chars/token is the measured ratio of this corpus through the
-    # froggeric template (646,000 chars -> 196,919 tokens at the 2026-10-11
-    # deploy); 98.5 % of the window leaves the response room under the ceiling.
-    chars = int(0.985 * maxlen * 3.28)
+    # The corpus's chars/token ratio is MEASURED here, not stored. A stored 3.28
+    # (646,000 chars -> 196,919 tokens, 2026-10-11) had drifted to 3.20 by
+    # 2026-10-20 (646,299 chars -> 201,876 tokens) as the repo's docs grew, and a
+    # stale constant sends leg B past --max-model-len, where the engine answers a
+    # perfectly legitimate 400 ("the request (201900 tokens) exceeds the available
+    # context size (200000)") and the leg reads as a capacity failure. One small
+    # request prices the ratio; 97 % of the window leaves the response room.
+    sample = prompt(wide, 4000, "CAL", "cal-marker")
+    cal, _, _ = post({"model": MODEL,
+                      "messages": [{"role": "user", "content": sample}],
+                      "max_tokens": 1, "temperature": 0.0,
+                      "chat_template_kwargs": {"enable_thinking": False}})
+    ratio = len(sample) / cal["usage"]["prompt_tokens"]
+    print(f"[cal] corpus ratio {ratio:.2f} chars/token")
+    chars = int(0.97 * maxlen * ratio)
     # Each marker's FIRST hyphen-separated word is unique, because show()
     # scores the hit on that word — a shared prefix would score every slot HIT
     # on any answer that says the word.
@@ -176,7 +187,7 @@ def main():
                 print(f"[B-{s}] FAILED after {time.time() - t0:.1f}s: {e!r}")
     print(f"[B] total {time.time() - t0:.1f}s")
     # D — concurrency decode: seqs streams, 512 tokens each, ~25 % depth.
-    dchars = int(0.25 * maxlen * 3.28)
+    dchars = int(0.25 * maxlen * ratio)
     dbodies = [chat(deep, dchars, f"D{i + 1}-9137", f"flint-marker-{i}", 512)
                for i in range(seqs)]
     print(f"[D] {seqs} concurrent streams x 512 tokens, "