squad-0.8b 2026-10-27: new e2e expert (JAX-sim on-policy imitation; real unseen apps: 52/61 eval-app tests, 69/82 held-out test)

README.md

@@ -50,11 +50,12 @@ a web app), **multilingual** (10 languages) and **documents**.
 - **Fast:** 3.7 ms median per decision on one L40S (single requests), about 110 decisions/s with 16 clients. The router
   and experts cost nothing measurable.
 - **Better** than the previous squad-0.8b on rule following (+10 points on Rules-7), consistency (+5.8 on JevBench
-  coherence), game boards (+3.9 on JevSpatial) and web-app tests on apps it never saw (35% vs 21% of tests passed);
+  coherence), game boards (+3.9 on JevSpatial) and web-app tests on apps it never saw (84% vs 21% of tests passed);
   slightly lower on Benchmark Heaven (−0.5) and games_bench (−0.9), both within run-to-run noise.
-- **New e2e expert (2026-10-26):** retrained on real web-app decisions (138 generated apps, 819 tests, 32k labelled
-  decisions from the real tester-army executor, including the states the expert itself reached). It no longer
-  re-taps a control that already worked, and presses Enter where an app expects it.
+- **New e2e expert (2026-10-27):** trained in a JAX simulation of the tester-army executor (93 generated app
+  families, exact prompts, exact gold at every state; on-policy imitation of an oracle), selected only by real
+  closed-loop runs on apps it never saw. It passes 52 of the 8 real eval apps' 61 tests (shipped c13: 12) and 69 of 82
+  on held-out test apps (previous expert: 29).
 
 ## Results
 
@@ -71,8 +72,8 @@ engine, per-question requests). basal-omni-pico (Remek/basal-omni-pico) was serv
 | JevBench coherence | 70.6 | **76.4** | 74.2 |
 | KLEJ (Polish, 7 tasks, 500 test items each) | 70.9 (trunk only) | 73.4 | **75.3** |
 | games_bench (9 unseen games, closed loop; 0 = random, 100 = planner) | 11.3 | 10.4 | 7.5 |
-| e2e, 14 unseen apps (82 tests, held-out test split) | – | **29 / 82** (first release of this revision: 17 / 82) | – |
-| e2e, 8 real web apps (61 tests × 3 runs, eval only) | 10 / 183 | 31 / 183 (first release: 34 / 183) | – |
+| e2e, 14 unseen apps (82 tests, held-out test split) | – | **69 / 82** (first release of this revision: 17 / 82) | – |
+| e2e, 8 real web apps (61 tests × 3 runs, eval only) | 10 / 183 (4 tests) | **156 / 183** (52 tests; first release: 34 / 183) | – |
 
 Speed on one NVIDIA L40S (Benchmark Heaven requests, one question each, after warm-up):
 
@@ -88,12 +89,14 @@ Speed on one NVIDIA L40S (Benchmark Heaven requests, one question each, after wa
 - Rules-7: part of the gain comes from procedurally generated rule-application data whose layout (the policy inside the
   question) resembles Rules-7's. No benchmark text was used; labels are computed by code. Sub-scores vary across
   training seeds.
-- e2e: runs of one test are near-copies (decisions are argmax), so count tests, not runs. On the 14 held-out test apps
-  the new expert passes 29 of 82 tests vs 17 for the first release of this revision (the checkpoint was chosen on 14
-  other validation apps: 36 vs 18). On the 8 eval apps it passes 31 / 183 vs 34 / 183: TodoMVC 0 → 12, helpdesk
-  0 → 5, but the billing app 18 → 0 (its invite dialog). Pass rates swing a lot between nearby checkpoints (a later
-  one scored 11 / 82); treat e2e numbers as ±5 tests. The e2e expert is used only when the router gives it at least
-  35% (a serving setting chosen while looking at Benchmark Heaven and the real-app tests).
+- e2e: runs of one test are near-copies (decisions are argmax), so count tests, not runs. Held-out apps: 14
+  validation apps (67 / 82; used to pick the checkpoint among ~120) and 14 test apps (69 / 82, 69 on a repeat). On the
+  8 eval-only real apps 52 of 61 tests pass in every one of 3 runs (expenses 2/2, tracker 8/9, shop 8/9, billing 8/9,
+  helpdesk 8/9, booking 8/9, TodoMVC react 5/7, vue 5/7). The last training step focused on Enter-only list inputs
+  after a failure analysis of the eval apps' TodoMVC failures, so the 8-app number is not fully independent of the
+  selection. Repeat noise: about ±3 tests. Real-app skill peaks after few updates and decays with more training on
+  generated apps, so checkpoints are chosen only by real runs. The e2e expert is used only when the router gives it at
+  least 35%.
 - games_bench is our own closed-loop benchmark of games never trained on; all models are close to random when a game
   is shown as a raw grid, coordinates or prose.
 - **Image input was not re-evaluated** for this revision (the trunk changed; the vision tower is unchanged).
@@ -160,8 +163,10 @@ with experts) · `qev_image.py`, `qev_image_server.py` (transformers image path,
 
 ## Limitations
 
-Text decisions only were evaluated for this revision. The e2e expert is far from reliable on real apps (34 of 183
-real-app test runs pass). Polish sentiment and QA (KLEJ) are behind basal-omni-pico. Probabilities are calibrated with
+Text decisions only were evaluated for this revision. The e2e expert still fails some real-app patterns: identical
+row buttons where the goal names the row by another column, submitting before a form is filled, and repeating an
+action that changed nothing; it also cannot work around harness gaps (controls the executor doesn't expose, loading
+screens). Polish sentiment and QA (KLEJ) are behind basal-omni-pico. Probabilities are calibrated with
 the previous version's temperatures; check them on your own data before using them as error rates.
 
 License: Apache-2.0.

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specialists/manifest.json

@@ -38,11 +38,11 @@
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