diff --git a/README.md b/README.md
index 6f3acd6..827272e 100644
--- a/README.md
+++ b/README.md
@@ -156,7 +156,7 @@ item can be highlighted in the original advisory.
 | 1 · Feasibility | Pinned ingestion, corpus statistics, label schema, offset-preserving preprocessing, pilot sample, annotation tooling | ✅ built |
 | 1 · Pilot | 100-document pilot (`pilot-2`: 22 hand-labelled, 78 from model drafts), [decision gate](docs/pilot_gate.md), guidelines v0.2 frozen, [acceptance targets](docs/targets.md) declared | ✅ done |
 | 2 · Baselines | Rules baseline, CRF + logistic-regression attributes and groups, evaluator with bootstrap CIs, evidence viewer | ✅ built · preliminary pilot dev scores |
-| 3 · Neural models | Frozen leakage-safe splits, annotation batches (01–03, 06: model-drafted), DistilBERT and DeBERTa-v3 with span / role / group heads, experiment runner | 🟨 trained on model drafts (run 3) · DeBERTa ≈ CRF on held-out agreement, no clear winner |
+| 3 · Neural models | Frozen leakage-safe splits, annotation batches (01–03, 06: model-drafted), DistilBERT and DeBERTa-v3 with span / role / group heads, experiment runner, CRF ablations and learning curve | 🟨 trained on model drafts (run 3) · DeBERTa ≈ CRF on held-out agreement, no clear winner |
 | 4 · Reliability | Calibration and review policy, formatting robustness (rules: 100%), challenge-set and error-catalogue tooling | 🟨 tooling ready · thresholds await labels |
 | 5 · Application | FastAPI API, review UI, SQLite (WAL) with append-only history, exports, Docker Compose stack, release manifests, tested rollback, CI image smoke test | ✅ built |
 | 6 · Production | Benchmark harness, ONNX/INT8 export (FP32 parity exact), local proxy benchmarks and load tests, monitoring; target-host numbers and hosted release pending | 🟨 harness and proxy results |
@@ -217,6 +217,12 @@ to three times as many agreeing conditions. Its held-out typed F1 is clearly hig
 clearly different, and its false-prerequisite rate is higher at every target (not clearly).
 The CRF therefore stays the default model; see [`reports/calibration/`](reports/calibration/README.md).
 
+**Ablations and learning curve (CRF).** On the 172 agreement records, removing the heading/list
+context features (−0.006 typed F1 [−0.027, +0.020]) or the part-of-speech features (+0.002
+[−0.025, +0.032]) makes no clear difference. Typed agreement rises from 0.054 with 30 training
+documents to 0.143 with 120, then stays flat (0.145 with all 239); span agreement keeps rising
+slowly. See [`reports/crf-ablations/`](reports/crf-ablations/README.md).
+
 95% project-group bootstrap intervals (1,000 resamples). Full reports, including scores against
 the 28 model-made dev records (which measure agreement with the drafting model, not accuracy),
 are in [`reports/pilot/`](reports/pilot/README.md). The paired difference from adding `batch-01` (+0.214 typed F1,
diff --git a/configs/experiments/crf-ablations.toml b/configs/experiments/crf-ablations.toml
new file mode 100644
index 0000000..314ed66
--- /dev/null
+++ b/configs/experiments/crf-ablations.toml
@@ -0,0 +1,82 @@
+# CRF ablations and learning curve, one factor at a time. The CRF is deterministic for a
+# fixed training set, so the full-data variants run once; each budget below the full train
+# split draws three seeded random subsets.
+#   bash scripts/experiments/crf_ablations.sh
+# Dev records only measure; nothing here is fitted or selected on them.
+
+[train]
+family = "crf"
+model_id = "crf-abl"
+use_pos = true
+use_context = true
+
+[[runs]]
+name = "full"
+
+[[runs]]
+name = "no-context"       # heading, list and lead-in features removed
+use_context = false
+
+[[runs]]
+name = "no-pos"           # spaCy part-of-speech features removed
+use_pos = false
+
+[[runs]]
+name = "docs30-seed0"
+max_train_documents = 30
+seed = 0
+
+[[runs]]
+name = "docs30-seed1"
+max_train_documents = 30
+seed = 1
+
+[[runs]]
+name = "docs30-seed2"
+max_train_documents = 30
+seed = 2
+
+[[runs]]
+name = "docs60-seed0"
+max_train_documents = 60
+seed = 0
+
+[[runs]]
+name = "docs60-seed1"
+max_train_documents = 60
+seed = 1
+
+[[runs]]
+name = "docs60-seed2"
+max_train_documents = 60
+seed = 2
+
+[[runs]]
+name = "docs120-seed0"
+max_train_documents = 120
+seed = 0
+
+[[runs]]
+name = "docs120-seed1"
+max_train_documents = 120
+seed = 1
+
+[[runs]]
+name = "docs120-seed2"
+max_train_documents = 120
+seed = 2
+
+[[runs]]
+name = "docs180-seed0"
+max_train_documents = 180
+seed = 0
+
+[[runs]]
+name = "docs180-seed1"
+max_train_documents = 180
+seed = 1
+
+[[runs]]
+name = "docs180-seed2"
+max_train_documents = 180
+seed = 2
diff --git a/docs/model_card.md b/docs/model_card.md
index a6c760d..f4dd875 100644
--- a/docs/model_card.md
+++ b/docs/model_card.md
@@ -134,6 +134,17 @@ The default model is therefore still the CRF. The live review policy stays
 `review-policy-0` (uncalibrated: every prediction is flagged), because thresholds fitted on
 model drafts measure agreement, not accuracy.
 
+## Ablations and learning curve
+
+Measured on the CRF, as agreement with the 172 model-drafted dev records
+([`reports/crf-ablations/`](../reports/crf-ablations/README.md)):
+- **Context and part-of-speech features:** removing either makes no clear difference (typed F1
+  −0.006 [−0.027, +0.020] and +0.002 [−0.025, +0.032]).
+- **Learning curve:** typed agreement rises from 0.054 with 30 training documents to 0.143 with
+  120, then stays flat at about 0.14. Span agreement keeps rising slowly.
+
+The group-objective ablation and transformer learning curves need a GPU and have not been run.
+
 ## Release process
 
 - **Thresholds.** Review thresholds and confidence cut-offs are chosen on the dev calibration
diff --git a/reports/crf-ablations/README.md b/reports/crf-ablations/README.md
new file mode 100644
index 0000000..afa9b05
--- /dev/null
+++ b/reports/crf-ablations/README.md
@@ -0,0 +1,91 @@
+# CRF ablations and learning curve (2026-11-07)
+
+How much each feature group and each extra training document contributes to the CRF behind
+the v0.1.0 preview (`crf-batches-01-02`). The runs are in
+[`configs/experiments/crf-ablations.toml`](../../configs/experiments/crf-ablations.toml) and are
+run by [`scripts/experiments/crf_ablations.sh`](../../scripts/experiments/crf_ablations.sh).
+
+- **Training data:** the train split of `pilot-2`, `batch-01` and `batch-02` (239 documents: 7
+  `manual`, 232 model drafts), the released model's training set.
+- **Scored on:** the 172 model-drafted dev records (`batch-03` + `batch-06`). These scores are
+  **agreement with the drafting model, not accuracy**. The CRF is not tuned or selected on
+  them; every run here is a measurement.
+- **Also scored on:** the 10 hand-labelled dev documents of `pilot-2`, for the ablations.
+- **Untouched:** the test split.
+- **Reproducible:** the full-data run reproduces the released model exactly. All 272 of its
+  predictions equal those of `crf-batches-01-02` in
+  [`reports/agreement/predictions/`](../agreement/predictions/), apart from the model id.
+
+95% project-group bootstrap intervals, 1,000 resamples.
+
+## Ablations
+
+One factor removed at a time, on all 239 training documents:
+- **`no-context`** drops the heading, list-level and lead-in features from the tagger and the
+  attribute classifiers, and the list and lead-in features from the group classifier.
+- **`no-pos`** drops the spaCy part-of-speech features.
+
+| Run | Typed F1 | Exact span F1 | Role macro-F1 | False-prerequisite rate | Training seconds |
+|---|---:|---:|---:|---:|---:|
+| full | 0.145 [0.104, 0.186] | 0.221 [0.169, 0.268] | 0.115 [0.066, 0.161] | 0.353 [0.268, 0.417] | 22 |
+| no-context | 0.139 [0.101, 0.178] | 0.203 [0.160, 0.242] | 0.114 [0.061, 0.172] | 0.377 [0.298, 0.436] | 19 |
+| no-pos | 0.147 [0.110, 0.188] | 0.217 [0.168, 0.259] | 0.121 [0.069, 0.177] | 0.331 [0.228, 0.410] | 8 |
+
+Paired differences against the full model (B − A):
+
+| B | Slice | Typed F1 | Exact span F1 | False-prerequisite rate (lower is better) |
+|---|---|---:|---:|---:|
+| no-context | agreement (172) | −0.006 [−0.027, +0.020] | −0.018 [−0.044, +0.011] | +0.024 [−0.030, +0.073] |
+| no-pos | agreement (172) | +0.002 [−0.025, +0.032] | −0.004 [−0.035, +0.031] | −0.022 [−0.089, +0.024] |
+| no-context | hand-labelled (10) | +0.030 [+0.000, +0.095] | +0.030 [+0.000, +0.095] | −0.048 [−0.500, +0.068] |
+| no-pos | hand-labelled (10) | +0.000 [+0.000, +0.000] | +0.000 [+0.000, +0.000] | +0.000 [+0.000, +0.000] |
+
+None of these differences is clear. Full reports: `compare_agreement_*.md` and
+`compare_manual_*.md`.
+
+## Learning curve
+
+Seeded random subsets of the 239 training documents, three subsets per budget. Mean ± SD
+across the three subsets. The CRF is deterministic for a fixed training set, so the
+239-document point is a single run.
+
+| Training documents | Typed F1 | Exact span F1 | Role macro-F1 | False-prerequisite rate |
+|---:|---:|---:|---:|---:|
+| 30 | 0.054 ± 0.006 | 0.099 ± 0.007 | 0.027 ± 0.009 | 0.465 ± 0.084 |
+| 60 | 0.094 ± 0.017 | 0.143 ± 0.007 | 0.061 ± 0.028 | 0.386 ± 0.055 |
+| 120 | 0.143 ± 0.008 | 0.194 ± 0.006 | 0.088 ± 0.023 | 0.375 ± 0.042 |
+| 180 | 0.140 ± 0.010 | 0.209 ± 0.008 | 0.108 ± 0.006 | 0.364 ± 0.034 |
+| 239 | 0.145 | 0.221 | 0.115 | 0.353 |
+
+Every run, with intervals, is in [`experiment.md`](experiment.md).
+
+## Reading
+
+- **Heading and list context adds nothing measurable to the CRF.** Removing it lowers span
+  agreement by 0.018, with an interval across zero. The group classifier, where list context
+  should matter most, matches no model-drafted group in any run (group F1 0.000).
+- **Part-of-speech features add nothing measurable either,** and they cost the most. Without
+  them training is about three times faster and needs no spaCy model. On the 10 hand-labelled
+  documents the two models' predictions score identically. This makes dropping spaCy from the
+  CRF a candidate simplification. It is not applied here: the released model stays as it is.
+- **Typed agreement stops rising at about 120 documents.** It climbs from 0.054 (30 documents)
+  to 0.143 (120), then stays flat (0.140 at 180, 0.145 at 239). Span agreement and role
+  macro-F1 keep rising slowly, and the false-prerequisite rate keeps falling slowly. More
+  model-drafted documents of the same kind are therefore unlikely to move the CRF's typed
+  agreement much. The limit is the attribute classifiers (category and role), not the number of
+  spans.
+- **The group-objective ablation does not apply to the CRF.** Its group classifier is separate
+  and predicts no matching group. That ablation is planned for the transformer
+  (`configs/experiments/ablations.toml`), which needs a GPU.
+
+Every number here is agreement with model drafts or a 10-document slice; neither is a release
+result.
+
+## Reproduce
+
+```bash
+bash scripts/experiments/crf_ablations.sh   # 15 trainings, a few minutes on a laptop
+```
+
+Bundles go to `artifacts/experiments/crf-ablations/` (not committed). The predictions of the
+three full-data runs are committed in `predictions/`.
diff --git a/reports/crf-ablations/compare_agreement_no-context.md b/reports/crf-ablations/compare_agreement_no-context.md
new file mode 100644
index 0000000..9b955ba
--- /dev/null
+++ b/reports/crf-ablations/compare_agreement_no-context.md
@@ -0,0 +1,25 @@
+# Agreement (172 model-drafted dev records): full -> no-context
+
+- Gold: `annotations/batches/batch-03.jsonl`, `annotations/batches/batch-06.jsonl` (dev split of `splits/pool-v1.json`; origin: all; 172 documents)
+- Baseline (A): `reports/crf-ablations/predictions/crf-abl-full.jsonl` (crf-abl-full)
+- Candidate (B): `reports/crf-ablations/predictions/crf-abl-no-context.jsonl` (crf-abl-no-context)
+- Bootstrap: 1000 paired resamples, project clusters, seed 0
+- Documents: 172
+
+Differences are B minus A with 95% paired bootstrap intervals. *Clear* means the whole interval lies on one side of zero.
+
+| Metric | A (crf-abl-full) | B (crf-abl-no-context) | B - A | 95% CI | Verdict |
+|---|---:|---:|---:|:---:|---|
+| Typed condition micro-F1 (span + category + role) | 0.145 | 0.139 | -0.006 | [-0.027, +0.020] | no clear difference |
+| Exact span F1 | 0.221 | 0.203 | -0.018 | [-0.044, +0.011] | no clear difference |
+| Exact span precision | 0.332 | 0.326 | -0.006 | [-0.048, +0.050] | no clear difference |
+| Exact span recall | 0.166 | 0.148 | -0.018 | [-0.040, +0.003] | no clear difference |
+| Role macro-F1 (roles with gold support) | 0.115 | 0.114 | -0.000 | [-0.011, +0.017] | no clear difference |
+| Category macro-F1 | 0.137 | 0.122 | -0.015 | [-0.038, +0.007] | no clear difference |
+| Group exact-match F1 | 0.000 | 0.000 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Complete-document exact match, documents with conditions | 0.065 | 0.045 | -0.019 | [-0.043, +0.000] | no clear difference |
+| Complete-document exact match, documents without conditions | 0.882 | 1.000 | +0.118 | [+0.000, +0.286] | no clear difference |
+| False prerequisite rate (lower is better) | 0.353 | 0.377 | +0.024 | [-0.030, +0.073] | no clear difference |
+| Reversed meaning rate (lower is better) | 0.074 | 0.064 | -0.010 | [-0.039, +0.009] | no clear difference |
+| Category accuracy on exactly matched spans | 0.797 | 0.807 | +0.010 | [-0.055, +0.098] | no clear difference |
+| Role accuracy on exactly matched spans | 0.859 | 0.860 | +0.000 | [-0.051, +0.044] | no clear difference |
diff --git a/reports/crf-ablations/compare_agreement_no-pos.md b/reports/crf-ablations/compare_agreement_no-pos.md
new file mode 100644
index 0000000..0717bfa
--- /dev/null
+++ b/reports/crf-ablations/compare_agreement_no-pos.md
@@ -0,0 +1,25 @@
+# Agreement (172 model-drafted dev records): full -> no-pos
+
+- Gold: `annotations/batches/batch-03.jsonl`, `annotations/batches/batch-06.jsonl` (dev split of `splits/pool-v1.json`; origin: all; 172 documents)
+- Baseline (A): `reports/crf-ablations/predictions/crf-abl-full.jsonl` (crf-abl-full)
+- Candidate (B): `reports/crf-ablations/predictions/crf-abl-no-pos.jsonl` (crf-abl-no-pos)
+- Bootstrap: 1000 paired resamples, project clusters, seed 0
+- Documents: 172
+
+Differences are B minus A with 95% paired bootstrap intervals. *Clear* means the whole interval lies on one side of zero.
+
+| Metric | A (crf-abl-full) | B (crf-abl-no-pos) | B - A | 95% CI | Verdict |
+|---|---:|---:|---:|:---:|---|
+| Typed condition micro-F1 (span + category + role) | 0.145 | 0.147 | +0.002 | [-0.025, +0.032] | no clear difference |
+| Exact span F1 | 0.221 | 0.217 | -0.004 | [-0.035, +0.031] | no clear difference |
+| Exact span precision | 0.332 | 0.333 | +0.002 | [-0.046, +0.070] | no clear difference |
+| Exact span recall | 0.166 | 0.161 | -0.005 | [-0.030, +0.022] | no clear difference |
+| Role macro-F1 (roles with gold support) | 0.115 | 0.121 | +0.006 | [-0.015, +0.035] | no clear difference |
+| Category macro-F1 | 0.137 | 0.140 | +0.003 | [-0.021, +0.031] | no clear difference |
+| Group exact-match F1 | 0.000 | 0.000 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Complete-document exact match, documents with conditions | 0.065 | 0.045 | -0.019 | [-0.041, +0.000] | no clear difference |
+| Complete-document exact match, documents without conditions | 0.882 | 1.000 | +0.118 | [+0.000, +0.286] | no clear difference |
+| False prerequisite rate (lower is better) | 0.353 | 0.331 | -0.022 | [-0.089, +0.024] | no clear difference |
+| Reversed meaning rate (lower is better) | 0.074 | 0.085 | +0.011 | [-0.037, +0.054] | no clear difference |
+| Category accuracy on exactly matched spans | 0.797 | 0.806 | +0.010 | [-0.058, +0.097] | no clear difference |
+| Role accuracy on exactly matched spans | 0.859 | 0.871 | +0.012 | [-0.009, +0.036] | no clear difference |
diff --git a/reports/crf-ablations/compare_manual_no-context.md b/reports/crf-ablations/compare_manual_no-context.md
new file mode 100644
index 0000000..3cf2fb8
--- /dev/null
+++ b/reports/crf-ablations/compare_manual_no-context.md
@@ -0,0 +1,25 @@
+# Hand-labelled dev (10): full -> no-context
+
+- Gold: `annotations/pilot/pilot-2.jsonl` (dev split of `splits/pool-v1.json`; origin: manual; 10 documents)
+- Baseline (A): `reports/crf-ablations/predictions/crf-abl-full.jsonl` (crf-abl-full)
+- Candidate (B): `reports/crf-ablations/predictions/crf-abl-no-context.jsonl` (crf-abl-no-context)
+- Bootstrap: 1000 paired resamples, project clusters, seed 0
+- Documents: 10
+
+Differences are B minus A with 95% paired bootstrap intervals. *Clear* means the whole interval lies on one side of zero.
+
+| Metric | A (crf-abl-full) | B (crf-abl-no-context) | B - A | 95% CI | Verdict |
+|---|---:|---:|---:|:---:|---|
+| Typed condition micro-F1 (span + category + role) | 0.370 | 0.400 | +0.030 | [+0.000, +0.095] | no clear difference |
+| Exact span F1 | 0.370 | 0.400 | +0.030 | [+0.000, +0.095] | no clear difference |
+| Exact span precision | 0.500 | 0.625 | +0.125 | [+0.000, +0.379] | no clear difference |
+| Exact span recall | 0.294 | 0.294 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Role macro-F1 (roles with gold support) | 0.200 | 0.217 | +0.017 | [+0.000, +0.066] | no clear difference |
+| Category macro-F1 | 0.237 | 0.232 | -0.004 | [-0.083, +0.047] | no clear difference |
+| Group exact-match F1 | 0.500 | 0.500 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Complete-document exact match, documents with conditions | 0.222 | 0.222 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Complete-document exact match, documents without conditions | 1.000 | 1.000 | +0.000 | [+0.000, +0.000] | no clear difference |
+| False prerequisite rate (lower is better) | 0.333 | 0.286 | -0.048 | [-0.500, +0.068] | no clear difference |
+| Reversed meaning rate (lower is better) | 0.000 | 0.000 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Category accuracy on exactly matched spans | 1.000 | 1.000 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Role accuracy on exactly matched spans | 1.000 | 1.000 | +0.000 | [+0.000, +0.000] | no clear difference |
diff --git a/reports/crf-ablations/compare_manual_no-pos.md b/reports/crf-ablations/compare_manual_no-pos.md
new file mode 100644
index 0000000..a3eb728
--- /dev/null
+++ b/reports/crf-ablations/compare_manual_no-pos.md
@@ -0,0 +1,25 @@
+# Hand-labelled dev (10): full -> no-pos
+
+- Gold: `annotations/pilot/pilot-2.jsonl` (dev split of `splits/pool-v1.json`; origin: manual; 10 documents)
+- Baseline (A): `reports/crf-ablations/predictions/crf-abl-full.jsonl` (crf-abl-full)
+- Candidate (B): `reports/crf-ablations/predictions/crf-abl-no-pos.jsonl` (crf-abl-no-pos)
+- Bootstrap: 1000 paired resamples, project clusters, seed 0
+- Documents: 10
+
+Differences are B minus A with 95% paired bootstrap intervals. *Clear* means the whole interval lies on one side of zero.
+
+| Metric | A (crf-abl-full) | B (crf-abl-no-pos) | B - A | 95% CI | Verdict |
+|---|---:|---:|---:|:---:|---|
+| Typed condition micro-F1 (span + category + role) | 0.370 | 0.370 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Exact span F1 | 0.370 | 0.370 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Exact span precision | 0.500 | 0.500 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Exact span recall | 0.294 | 0.294 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Role macro-F1 (roles with gold support) | 0.200 | 0.200 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Category macro-F1 | 0.237 | 0.237 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Group exact-match F1 | 0.500 | 0.500 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Complete-document exact match, documents with conditions | 0.222 | 0.222 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Complete-document exact match, documents without conditions | 1.000 | 1.000 | +0.000 | [+0.000, +0.000] | no clear difference |
+| False prerequisite rate (lower is better) | 0.333 | 0.333 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Reversed meaning rate (lower is better) | 0.000 | 0.000 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Category accuracy on exactly matched spans | 1.000 | 1.000 | +0.000 | [+0.000, +0.000] | no clear difference |
+| Role accuracy on exactly matched spans | 1.000 | 1.000 | +0.000 | [+0.000, +0.000] | no clear difference |
diff --git a/reports/crf-ablations/experiment.md b/reports/crf-ablations/experiment.md
new file mode 100644
index 0000000..820911b
--- /dev/null
+++ b/reports/crf-ablations/experiment.md
@@ -0,0 +1,38 @@
+# Experiment: crf-ablations
+
+- Config: `configs/experiments/crf-ablations.toml`
+- Gold: 3 file(s): 239 train docs
+- Dev gold: `annotations/batches/batch-03.jsonl`, `annotations/batches/batch-06.jsonl`: 172 dev docs (this table)
+- Split: `splits/pool-v1.json` (41d7354e1a13)
+
+## Runs (dev split, 95% leakage-group bootstrap intervals)
+
+| Run | Docs | Typed F1 | Span F1 | Role macro-F1 | Group F1 | False prereq. | Best epoch | Seconds |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| crf-abl-full | 239 | 0.145 [0.104, 0.186] | 0.221 [0.169, 0.268] | 0.115 [0.066, 0.161] | 0.000 [0.000, 0.000] | 0.353 [0.268, 0.417] | n/a | 22 |
+| crf-abl-no-context | 239 | 0.139 [0.101, 0.178] | 0.203 [0.160, 0.242] | 0.114 [0.061, 0.172] | 0.000 [0.000, 0.000] | 0.377 [0.298, 0.436] | n/a | 19 |
+| crf-abl-no-pos | 239 | 0.147 [0.110, 0.188] | 0.217 [0.168, 0.259] | 0.121 [0.069, 0.177] | 0.000 [0.000, 0.000] | 0.331 [0.228, 0.410] | n/a | 8 |
+| crf-abl-docs30-seed0 | 30 | 0.049 [0.023, 0.080] | 0.091 [0.061, 0.125] | 0.020 [0.011, 0.030] | 0.000 [0.000, 0.000] | 0.556 [0.450, 0.638] | n/a | 2 |
+| crf-abl-docs30-seed1 | 30 | 0.061 [0.033, 0.091] | 0.101 [0.067, 0.136] | 0.038 [0.018, 0.064] | 0.000 [0.000, 0.000] | 0.447 [0.345, 0.533] | n/a | 3 |
+| crf-abl-docs30-seed2 | 30 | 0.052 [0.025, 0.083] | 0.104 [0.066, 0.143] | 0.024 [0.014, 0.036] | 0.000 [0.000, 0.000] | 0.391 [0.283, 0.471] | n/a | 3 |
+| crf-abl-docs60-seed0 | 60 | 0.080 [0.044, 0.120] | 0.144 [0.105, 0.185] | 0.034 [0.020, 0.047] | 0.000 [0.000, 0.000] | 0.423 [0.295, 0.527] | n/a | 4 |
+| crf-abl-docs60-seed1 | 60 | 0.113 [0.082, 0.149] | 0.149 [0.111, 0.186] | 0.060 [0.031, 0.102] | 0.000 [0.000, 0.000] | 0.413 [0.327, 0.480] | n/a | 5 |
+| crf-abl-docs60-seed2 | 60 | 0.089 [0.050, 0.128] | 0.136 [0.088, 0.180] | 0.090 [0.026, 0.152] | 0.000 [0.000, 0.000] | 0.323 [0.235, 0.412] | n/a | 6 |
+| crf-abl-docs120-seed0 | 120 | 0.134 [0.096, 0.170] | 0.190 [0.149, 0.228] | 0.067 [0.039, 0.107] | 0.000 [0.000, 0.000] | 0.401 [0.303, 0.476] | n/a | 9 |
+| crf-abl-docs120-seed1 | 120 | 0.147 [0.113, 0.186] | 0.201 [0.151, 0.248] | 0.084 [0.045, 0.125] | 0.000 [0.000, 0.000] | 0.397 [0.296, 0.459] | n/a | 12 |
+| crf-abl-docs120-seed2 | 120 | 0.148 [0.107, 0.187] | 0.191 [0.141, 0.239] | 0.113 [0.060, 0.159] | 0.000 [0.000, 0.000] | 0.326 [0.255, 0.388] | n/a | 10 |
+| crf-abl-docs180-seed0 | 180 | 0.132 [0.098, 0.169] | 0.205 [0.166, 0.245] | 0.101 [0.051, 0.147] | 0.000 [0.000, 0.000] | 0.364 [0.274, 0.428] | n/a | 14 |
+| crf-abl-docs180-seed1 | 180 | 0.138 [0.100, 0.177] | 0.203 [0.163, 0.243] | 0.113 [0.061, 0.159] | 0.000 [0.000, 0.000] | 0.398 [0.310, 0.457] | n/a | 15 |
+| crf-abl-docs180-seed2 | 180 | 0.151 [0.107, 0.201] | 0.218 [0.174, 0.267] | 0.110 [0.065, 0.157] | 0.000 [0.000, 0.000] | 0.331 [0.243, 0.404] | n/a | 13 |
+
+## Across seeds (typed F1)
+
+| Variant | Seeds | Mean | SD |
+|---|---:|---:|---:|
+| crf-abl-full | 1 | 0.145 | 0.000 |
+| crf-abl-no-context | 1 | 0.139 | 0.000 |
+| crf-abl-no-pos | 1 | 0.147 | 0.000 |
+| crf-abl-docs30 | 3 | 0.054 | 0.006 |
+| crf-abl-docs60 | 3 | 0.094 | 0.017 |
+| crf-abl-docs120 | 3 | 0.143 | 0.008 |
+| crf-abl-docs180 | 3 | 0.140 | 0.010 |
diff --git a/reports/crf-ablations/predictions/crf-abl-full.jsonl b/reports/crf-ablations/predictions/crf-abl-full.jsonl
new file mode 100644
index 0000000..1eb7f08
--- /dev/null
+++ b/reports/crf-ablations/predictions/crf-abl-full.jsonl
@@ -0,0 +1,272 @@
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diff --git a/reports/crf-ablations/predictions/crf-abl-no-context.jsonl b/reports/crf-ablations/predictions/crf-abl-no-context.jsonl
new file mode 100644
index 0000000..fc01d79
--- /dev/null
+++ b/reports/crf-ablations/predictions/crf-abl-no-context.jsonl
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diff --git a/reports/crf-ablations/predictions/crf-abl-no-pos.jsonl b/reports/crf-ablations/predictions/crf-abl-no-pos.jsonl
new file mode 100644
index 0000000..7233e61
--- /dev/null
+++ b/reports/crf-ablations/predictions/crf-abl-no-pos.jsonl
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+{"advisory_id":"GHSA-9w2p-rh8c-v9g5","content_hash":"f31ca4835b47fce2a8cfe494680c8f2efc06040a7721572927b17ba7e178577a","preprocessing_version":"1","model_id":"crf-abl-no-pos","document_status":"HAS_CONDITIONS","conditions":[{"id":"c1","span":[30,37],"category":"PLATFORM","role":"REQUIRED","source":"model","confidence":0.7967},{"id":"c2","span":[300,360],"category":"ACCESS","role":"REQUIRED","source":"model","confidence":0.4275}],"groups":[],"review_flags":["low_confidence:c2"]}
+{"advisory_id":"GHSA-ph9x-4vc9-m39g","content_hash":"e3d6541dc5249f8461a6d71971524e19f2d8cdaebe4665a645b1730d0073fb5f","preprocessing_version":"1","model_id":"crf-abl-no-pos","document_status":"HAS_CONDITIONS","conditions":[{"id":"c1","span":[139,192],"category":"USAGE","role":"REQUIRED","source":"model","confidence":0.3092},{"id":"c2","span":[635,706],"category":"ACCESS","role":"REQUIRED","source":"model","confidence":0.3186}],"groups":[],"review_flags":["low_confidence:c1","low_confidence:c2"]}
+{"advisory_id":"GHSA-h8gc-pgj2-vjm3","content_hash":"bab3a04046c0d42cac32d860dd718988626b8af914c04029091181234fd05769","preprocessing_version":"1","model_id":"crf-abl-no-pos","document_status":"HAS_CONDITIONS","conditions":[{"id":"c1","span":[203,212],"category":"CONFIGURATION","role":"REQUIRED","source":"model","confidence":0.4676}],"groups":[],"review_flags":["low_confidence:c1"]}
+{"advisory_id":"GHSA-5x64-925v-h4gv","content_hash":"7b81397f423e1e6b6492140401d6905a60136b343335fa46abaa6f608beba688","preprocessing_version":"1","model_id":"crf-abl-no-pos","document_status":"NO_EXPLICIT_CONDITION","conditions":[],"groups":[],"review_flags":[]}
+{"advisory_id":"GHSA-pwv6-vv43-88gr","content_hash":"042b8ebfd5b9f81a14904e79ecb0489c2824407eb20b5cda101f9e2769ada955","preprocessing_version":"1","model_id":"crf-abl-no-pos","document_status":"HAS_CONDITIONS","conditions":[{"id":"c1","span":[81,112],"category":"USAGE","role":"REQUIRED","source":"model","confidence":0.6583}],"groups":[],"review_flags":[]}
+{"advisory_id":"GHSA-2hwx-mjrm-v3g8","content_hash":"513780c9afa71541645090cad0233f883ece8ac31016dc117d729d140be2549c","preprocessing_version":"1","model_id":"crf-abl-no-pos","document_status":"NO_EXPLICIT_CONDITION","conditions":[],"groups":[],"review_flags":[]}
diff --git a/scripts/experiments/crf_ablations.sh b/scripts/experiments/crf_ablations.sh
new file mode 100755
index 0000000..d4bcae2
--- /dev/null
+++ b/scripts/experiments/crf_ablations.sh
@@ -0,0 +1,41 @@
+#!/usr/bin/env bash
+# CRF ablations (no heading/list context, no part of speech) and learning curve.
+#
+#   bash scripts/experiments/crf_ablations.sh
+#
+# Trains every run of configs/experiments/crf-ablations.toml on the train split of pilot-2,
+# batch-01 and batch-02 (the training set of the released crf-batches-01-02). It scores each
+# run on the 172 model-drafted dev records (batch-03 + batch-06), then compares the full model
+# with each ablation, pairwise, on those records and on the 10 hand-labelled dev documents.
+# Nothing is fitted or selected on dev, and the test split is not touched. Scores against
+# model drafts are agreement with the drafting model, not accuracy.
+set -euo pipefail
+[ -f pyproject.toml ] && [ -d annotations ] || { echo "run from the repository root" >&2; exit 1; }
+
+TM=(uv run --no-sync triggermap)
+TRAIN=(annotations/pilot/pilot-2.jsonl annotations/batches/batch-01.jsonl annotations/batches/batch-02.jsonl)
+DEV=(annotations/batches/batch-03.jsonl annotations/batches/batch-06.jsonl)
+SAMPLES=(annotations/pilot/pilot_manifest.json annotations/batches/batch-03.json annotations/batches/batch-06.json)
+RUNS=artifacts/experiments/crf-ablations
+OUT=reports/crf-ablations
+mkdir -p "$OUT/predictions"
+
+"${TM[@]}" experiment --config configs/experiments/crf-ablations.toml --gold "${TRAIN[@]}" \
+  --dev-gold "${DEV[@]}" --out-root "$RUNS" --report "$OUT/experiment.md"
+
+for run in full no-context no-pos; do
+  "${TM[@]}" predict --model crf --model-dir "$RUNS/crf-abl-$run" --sample "${SAMPLES[@]}" \
+    --out "$OUT/predictions/crf-abl-$run.jsonl"
+done
+
+for run in no-context no-pos; do
+  a="$OUT/predictions/crf-abl-full.jsonl"
+  b="$OUT/predictions/crf-abl-$run.jsonl"
+  "${TM[@]}" compare "${DEV[0]}" "$a" "$b" --add-gold "${DEV[1]}" --split dev \
+    --title "Agreement (172 model-drafted dev records): full -> $run" \
+    --out "$OUT/compare_agreement_$run.md" >/dev/null
+  "${TM[@]}" compare annotations/pilot/pilot-2.jsonl "$a" "$b" --split dev --gold-origin manual \
+    --title "Hand-labelled dev (10): full -> $run" \
+    --out "$OUT/compare_manual_$run.md" >/dev/null
+done
+ls "$OUT"
diff --git a/src/triggermap/cli.py b/src/triggermap/cli.py
index 730f32e..739aea4 100644
--- a/src/triggermap/cli.py
+++ b/src/triggermap/cli.py
@@ -501,6 +501,8 @@ def _train(args: argparse.Namespace) -> int:
         args.out,
         model_id=args.model_id,
         use_pos=not args.no_pos,
+        use_context=not args.no_context,
+        max_train_documents=args.max_train_documents,
         seed=args.seed or 0,
     )
     print(json.dumps(model.metadata["stats"], indent=2))
@@ -523,8 +525,8 @@ def _experiment(args: argparse.Namespace) -> int:
         return 0
 
     from triggermap.corpus.splits import load_split
-    from triggermap.models.transformer.train import TrainConfig, train
 
+    family = config["train"].get("family", "transformer")
     split = load_split(args.split_manifest)
     examples = _gold_examples(args.gold, args.processed, args.split_manifest, origins=args.origin)
     dev = _gold_examples(
@@ -537,16 +539,12 @@ def _experiment(args: argparse.Namespace) -> int:
     if not examples or not dev:
         raise SystemExit("experiments need gold documents in both the train and dev splits")
     results = []
-    from triggermap.models.transformer.extractor import TransformerExtractor
-
     for run in runs:
         bundle = args.out_root / run.name
-        if (bundle / "metadata.json").exists():  # resume: finished runs are not retrained
-            extractor = TransformerExtractor.load(bundle)
-            print(f"{run.name}: reusing existing bundle")
+        if family == "crf":
+            extractor: Any = _experiment_crf(run.settings, examples, bundle)
         else:
-            settings = {**run.settings, "split_assignment_sha256": split["assignment_sha256"]}
-            extractor = train(TrainConfig(**settings), examples, dev, bundle)
+            extractor = _experiment_transformer(run.settings, examples, dev, bundle, split)
         intervals = evaluate_run(extractor, dev, split["group_of"], args.samples)
         results.append(RunResult(run, intervals, extractor.metadata))
         print(f"{run.name}: dev typed F1 {intervals['typed_f1'].estimate:.3f}")
@@ -561,7 +559,9 @@ def _experiment(args: argparse.Namespace) -> int:
                 "Gold": f"{len(args.gold)} file(s): {len(examples)} train docs",
                 "Dev gold": ", ".join(f"`{p}`" for p in (args.dev_gold or args.gold))
                 + (f", subset `{args.dev_subset}`" if args.dev_subset else "")
-                + f": {len(dev)} dev docs (early stopping and this table)",
+                + f": {len(dev)} dev docs ("
+                + ("this table" if family == "crf" else "early stopping and this table")
+                + ")",
                 "Split": f"`{args.split_manifest}` ({split['assignment_sha256'][:12]})",
             },
         ),
@@ -571,6 +571,39 @@ def _experiment(args: argparse.Namespace) -> int:
     return 0
 
 
+def _experiment_transformer(
+    settings: dict[str, Any],
+    examples: Sequence[Any],
+    dev: Sequence[Any],
+    bundle: Path,
+    split: dict[str, Any],
+) -> Any:
+    from triggermap.models.transformer.extractor import TransformerExtractor
+    from triggermap.models.transformer.train import TrainConfig, train
+
+    if (bundle / "metadata.json").exists():  # resume: finished runs are not retrained
+        print(f"{bundle.name}: reusing existing bundle")
+        return TransformerExtractor.load(bundle)
+    settings = {**settings, "split_assignment_sha256": split["assignment_sha256"]}
+    return train(TrainConfig(**settings), examples, dev, bundle)
+
+
+def _experiment_crf(settings: dict[str, Any], examples: Sequence[Any], bundle: Path) -> Any:
+    import time
+
+    from triggermap.models.crf.model import CrfExtractor
+    from triggermap.models.experiment import crf_settings
+
+    if (bundle / "metadata.json").exists():  # resume: finished runs are not retrained
+        print(f"{bundle.name}: reusing existing bundle")
+        return CrfExtractor.load(bundle)
+    started = time.monotonic()
+    model = CrfExtractor.train(examples, bundle, **crf_settings(settings))
+    # Recorded for the report only; the bundle's own metadata stays deterministic.
+    model.metadata = {**model.metadata, "train_seconds": round(time.monotonic() - started)}
+    return model
+
+
 def _calibrate(args: argparse.Namespace) -> int:
     from triggermap.corpus.splits import load_split, select
     from triggermap.eval.calibration import (
@@ -1240,6 +1273,14 @@ def build_parser() -> argparse.ArgumentParser:
     train.add_argument("--split-manifest", type=Path, default=DEFAULT_SPLIT)
     train.add_argument("--model-id", default="crf-1")
     train.add_argument("--no-pos", action="store_true", help="skip spaCy part-of-speech features")
+    train.add_argument(
+        "--no-context", action="store_true", help="CRF ablation: no heading/list context features"
+    )
+    train.add_argument(
+        "--max-train-documents",
+        type=int,
+        help="CRF learning curve: train on a random subset of this size (seeded by --seed)",
+    )
     train.add_argument("--seed", type=int, help="overrides the config seed")
     train.add_argument(
         "--origin",
@@ -1389,7 +1430,9 @@ def build_parser() -> argparse.ArgumentParser:
     challenge.add_argument("--out", type=Path)
     challenge.set_defaults(handler=_challenge)
 
-    experiment = commands.add_parser("experiment", help="run a grid of transformer trainings")
+    experiment = commands.add_parser(
+        "experiment", help="run a grid of trainings (transformer or CRF)"
+    )
     experiment.add_argument("--config", type=Path, required=True)
     experiment.add_argument("--gold", type=Path, nargs="+", help="gold JSONL file(s)")
     _dev_gold_argument(experiment)
diff --git a/src/triggermap/models/crf/features.py b/src/triggermap/models/crf/features.py
index 633e98a..4a9f7ac 100644
--- a/src/triggermap/models/crf/features.py
+++ b/src/triggermap/models/crf/features.py
@@ -106,9 +106,11 @@ def token_features(
     tokens: Sequence[Token],
     ctx: SentenceContext,
     pos: Sequence[str] | None,
+    use_context: bool = True,
 ) -> list[Features]:
+    """Per-token features; ``use_context=False`` drops heading/list context (an ablation)."""
     lowered = [t.text.lower() for t in tokens]
-    context = _context_features(ctx)
+    context = _context_features(ctx) if use_context else {}
     last_cue: tuple[int, str] | None = None
     out: list[Features] = []
     for i, tok in enumerate(tokens):
@@ -160,7 +162,12 @@ def token_features(
 
 
 def span_features(
-    tokens: Sequence[Token], first: int, last: int, ctx: SentenceContext, in_code: bool
+    tokens: Sequence[Token],
+    first: int,
+    last: int,
+    ctx: SentenceContext,
+    in_code: bool,
+    use_context: bool = True,
 ) -> Features:
     """Features for the attribute classifiers; ``first``..``last`` inclusive token indices."""
     lowered = [t.text.lower() for t in tokens]
@@ -182,17 +189,28 @@ def span_features(
         feats["neg_before"] = 1.0
     if in_code:
         feats["in_code"] = 1.0
-    feats.update(_context_features(ctx))
+    if use_context:
+        feats.update(_context_features(ctx))
     return feats
 
 
 def pair_features(
-    text: str, a: tuple[int, int], b: tuple[int, int], same_list: bool, lead: str
+    text: str,
+    a: tuple[int, int],
+    b: tuple[int, int],
+    same_list: bool,
+    lead: str,
+    use_context: bool = True,
 ) -> Features:
-    """Features for whether two conditions (character spans, a before b) share a group."""
+    """Features for whether two conditions (character spans, a before b) share a group.
+
+    ``use_context=False`` drops the list and lead-in features (the context ablation).
+    """
     between = text[a[1] : b[0]].lower()
     words = re.findall(r"[a-z/]+", between)
-    feats: Features = {"gap": _bucket(len(words)), "same_list": float(same_list)}
+    feats: Features = {"gap": _bucket(len(words))}
+    if use_context:
+        feats["same_list"] = float(same_list)
     for word in words[:6]:
         feats[f"between={word}"] = 1.0
     for marker in ("and", "or", "and/or", "either", "both", "but", "while", "also"):
@@ -200,7 +218,7 @@ def pair_features(
             feats[f"has={marker}"] = 1.0
     if ";" in between:
         feats["semicolon"] = 1.0
-    lead_low = lead.lower()
+    lead_low = lead.lower() if use_context else ""
     for marker in ("all of", "any of", "one of", "either", "following", "both", "combine"):
         if marker in lead_low:
             feats[f"lead={marker}"] = 1.0
diff --git a/src/triggermap/models/crf/model.py b/src/triggermap/models/crf/model.py
index 6e179fd..fd90167 100644
--- a/src/triggermap/models/crf/model.py
+++ b/src/triggermap/models/crf/model.py
@@ -13,6 +13,7 @@
 from __future__ import annotations
 
 import json
+import random
 from collections import Counter
 from collections.abc import Sequence
 from dataclasses import dataclass
@@ -92,7 +93,7 @@ class _Unit:
     features: list[Features]
 
 
-def _units(doc: PreprocessedDocument, use_pos: bool) -> list[_Unit]:
+def _units(doc: PreprocessedDocument, use_pos: bool, use_context: bool) -> list[_Unit]:
     units = []
     for index, sentence in enumerate(doc.sentences):
         tokens = tokenize(doc.text, sentence.start, sentence.end)
@@ -100,7 +101,8 @@ def _units(doc: PreprocessedDocument, use_pos: bool) -> list[_Unit]:
             continue
         ctx = sentence_context(doc, sentence)
         pos = pos_tags(doc.text, tokens, sentence.start) if use_pos else None
-        units.append(_Unit(index, tokens, ctx, token_features(doc, tokens, ctx, pos)))
+        feats = token_features(doc, tokens, ctx, pos, use_context)
+        units.append(_Unit(index, tokens, ctx, feats))
     return units
 
 
@@ -123,7 +125,7 @@ def _scope(doc: PreprocessedDocument, span: tuple[int, int]) -> tuple[str, int,
 
 
 def _pairs(
-    doc: PreprocessedDocument, spans: Sequence[tuple[int, int]]
+    doc: PreprocessedDocument, spans: Sequence[tuple[int, int]], use_context: bool
 ) -> list[tuple[int, int, Features]]:
     """Consecutive condition pairs that share a sentence or list, with their features."""
     out: list[tuple[int, int, Features]] = []
@@ -131,7 +133,8 @@ def _pairs(
     for i in range(len(spans) - 1):
         a, b = scopes[i], scopes[i + 1]
         if a[0] != "none" and a[:2] == b[:2]:
-            feats = pair_features(doc.text, spans[i], spans[i + 1], a[0] == "list", a[2])
+            same_list = a[0] == "list"
+            feats = pair_features(doc.text, spans[i], spans[i + 1], same_list, a[2], use_context)
             out.append((i, i + 1, feats))
     return out
 
@@ -149,6 +152,8 @@ def __init__(
         self.metadata = metadata
         self.model_id = str(metadata["model_id"])
         self.use_pos = bool(metadata["use_pos"])
+        # Bundles from before the context ablation always used context features.
+        self.use_context = bool(metadata.get("use_context", True))
         self.category = category
         self.role = role
         self.group = group
@@ -165,12 +170,23 @@ def train(
         *,
         model_id: str = "crf-1",
         use_pos: bool = True,
+        use_context: bool = True,
+        max_train_documents: int | None = None,
         c1: float = 0.1,
         c2: float = 0.1,
         max_iterations: int = 200,
         seed: int = 0,
     ) -> CrfExtractor:
+        """Train a bundle into ``out_dir``.
+
+        ``use_context=False`` (ablation) drops heading/list context features;
+        ``max_train_documents`` (learning curves) trains on a seeded random subset.
+        """
         out_dir.mkdir(parents=True, exist_ok=True)
+        if max_train_documents is not None:
+            examples = random.Random(seed).sample(
+                list(examples), min(max_train_documents, len(examples))
+            )
         trainer = pycrfsuite.Trainer(verbose=False)
         span_x: list[Features] = []
         category_y: list[str] = []
@@ -184,7 +200,7 @@ def train(
                 raise ValueError(f"{record.advisory_id}: annotation is for different text")
             conditions = sorted(record.conditions, key=lambda c: c.span)
             placed: set[str] = set()
-            for unit in _units(doc, use_pos):
+            for unit in _units(doc, use_pos, use_context):
                 sentence = doc.sentences[unit.sentence_index]
                 inside = [
                     c
@@ -203,7 +219,9 @@ def train(
                     if cond.span[0] in starts and cond.span[1] in ends:
                         first, last = starts[cond.span[0]], ends[cond.span[1]]
                         in_code = doc.canonical.in_code(cond.span[0])
-                        span_x.append(span_features(unit.tokens, first, last, unit.ctx, in_code))
+                        span_x.append(
+                            span_features(unit.tokens, first, last, unit.ctx, in_code, use_context)
+                        )
                         category_y.append(cond.category.value)
                         role_y.append(cond.role.value)
             stats["gold_spans"] += len(conditions)
@@ -211,7 +229,7 @@ def train(
 
             group_of = {m: g.operator.value for g in record.groups for m in g.members}
             group_index = {m: i for i, g in enumerate(record.groups) for m in g.members}
-            for i, j, feats in _pairs(doc, [c.span for c in conditions]):
+            for i, j, feats in _pairs(doc, [c.span for c in conditions], use_context):
                 a, b = conditions[i].id, conditions[j].id
                 same = a in group_index and group_index.get(a) == group_index.get(b)
                 pair_x.append(feats)
@@ -240,7 +258,14 @@ def train(
             "feature_version": FEATURE_VERSION,
             "preprocessing_version": PREPROCESSING_VERSION,
             "use_pos": use_pos,
-            "hyperparameters": {"c1": c1, "c2": c2, "max_iterations": max_iterations, "seed": seed},
+            "use_context": use_context,
+            "hyperparameters": {
+                "c1": c1,
+                "c2": c2,
+                "max_iterations": max_iterations,
+                "seed": seed,
+                "max_train_documents": max_train_documents,
+            },
             "training_documents": len(records),
             "training_hash": training_hash(records),
             "training_origins": training_origins(records),
@@ -271,7 +296,7 @@ def load(cls, directory: Path) -> CrfExtractor:
 
     def extract(self, doc: PreprocessedDocument, advisory_id: str | None = None) -> Extraction:
         found: list[tuple[tuple[int, int], Features, float]] = []
-        for unit in _units(doc, self.use_pos):
+        for unit in _units(doc, self.use_pos, self.use_context):
             tags = self._tagger.tag(unit.features)
             ranges = spans_from_tags(unit.tokens, tags)
             pieces = [p for r in ranges for p in split_at_lines(doc.text, unit.tokens, *r)]
@@ -279,7 +304,9 @@ def extract(self, doc: PreprocessedDocument, advisory_id: str | None = None) ->
                 marginals = [self._marginal(tags[i], i) for i in range(first, stop)]
                 span = (unit.tokens[first].start, unit.tokens[stop - 1].end)
                 in_code = doc.canonical.in_code(span[0])
-                feats = span_features(unit.tokens, first, stop - 1, unit.ctx, in_code)
+                feats = span_features(
+                    unit.tokens, first, stop - 1, unit.ctx, in_code, self.use_context
+                )
                 found.append((span, feats, min(marginals)))
 
         categories = self.category.predict([f for _, f, _ in found])
@@ -322,7 +349,7 @@ def _marginal(self, tag: str, index: int) -> float:
             return 0.0
 
     def _groups(self, doc: PreprocessedDocument, conditions: Sequence[Condition]) -> list[Group]:
-        pairs = _pairs(doc, [c.span for c in conditions])
+        pairs = _pairs(doc, [c.span for c in conditions], self.use_context)
         labels = self.group.predict([feats for _, _, feats in pairs])
         chains: list[tuple[list[int], set[str]]] = []
         for (i, j, _), (label, _) in zip(pairs, labels, strict=True):
diff --git a/src/triggermap/models/experiment.py b/src/triggermap/models/experiment.py
index 0db0e83..3471961 100644
--- a/src/triggermap/models/experiment.py
+++ b/src/triggermap/models/experiment.py
@@ -41,6 +41,21 @@ def plan(config: Mapping[str, Any]) -> list[PlannedRun]:
     return runs
 
 
+CRF_SETTINGS = frozenset(
+    {"model_id", "use_pos", "use_context", "max_train_documents", "c1", "c2"}
+    | {"max_iterations", "seed"}
+)
+
+
+def crf_settings(settings: Mapping[str, Any]) -> dict[str, Any]:
+    """Keyword arguments for ``CrfExtractor.train`` from a ``family = "crf"`` run."""
+    rest = {k: v for k, v in settings.items() if k != "family"}
+    unknown = sorted(set(rest) - CRF_SETTINGS)
+    if unknown:  # typos in a config must fail, as TrainConfig does for transformers
+        raise ValueError(f"unknown CRF settings: {', '.join(unknown)}")
+    return rest
+
+
 @dataclass(frozen=True)
 class RunResult:
     run: PlannedRun
@@ -68,6 +83,10 @@ def evaluate_run(
 ]
 
 
+def _cell(value: Any) -> str:
+    return "n/a" if value is None else str(value)
+
+
 def render_report(title: str, results: Sequence[RunResult], context: Mapping[str, str]) -> str:
     lines = [f"# {title}", "", *(f"- {k}: {v}" for k, v in context.items()), ""]
     lines += [
@@ -85,7 +104,7 @@ def render_report(title: str, results: Sequence[RunResult], context: Mapping[str
         lines.append(
             f"| {result.run.name} | {meta['training_documents']} | "
             + " | ".join(cells)
-            + f" | {meta.get('best_epoch')} | {meta.get('train_seconds')} |"
+            + f" | {_cell(meta.get('best_epoch'))} | {_cell(meta.get('train_seconds'))} |"
         )
     groups: dict[str, list[float]] = {}
     for result in results:
diff --git a/tests/synthetic.py b/tests/synthetic.py
index 078282c..ad521c5 100644
--- a/tests/synthetic.py
+++ b/tests/synthetic.py
@@ -43,7 +43,10 @@
 Spec = tuple[str, Category, Role]
 
 _GHSA_ALPHABET = "23456789cfghjmpqrvwx"
-_STREAMS = {"train": 0, "test": 1, "x": 2, "t": 3, "pos": 4, "cli": 5, "org": 6}
+_STREAMS = {
+    "train": 0, "test": 1, "x": 2, "t": 3, "pos": 4, "cli": 5, "org": 6,
+    "ctx": 7, "lc": 8, "exp": 9,
+}  # fmt: skip
 
 
 def synthetic_id(prefix: str, index: int) -> str:
diff --git a/tests/test_crf.py b/tests/test_crf.py
index 2716c2c..c5f7ad1 100644
--- a/tests/test_crf.py
+++ b/tests/test_crf.py
@@ -161,3 +161,90 @@ def test_tagged_ranges_are_split_at_line_breaks() -> None:
         text[tokens[a].start : tokens[b - 1].end] for a, b in split_at_lines(text, tokens, 0, 7)
     ]
     assert pieces == ["uses the legacy API", "with debug enabled"]
+
+
+def test_context_ablation_drops_context_features(tmp_path: Path) -> None:
+    from triggermap.models.crf.features import sentence_context, token_features
+
+    data = make_dataset(12, TRAIN_VOCAB, seed=7, prefix="ctx")
+    _, pre, rec = data[0]
+    sentence = pre.sentences[0]
+    tokens = tokenize(pre.text, sentence.start, sentence.end)
+    ctx = sentence_context(pre, sentence)
+    assert "block" in token_features(pre, tokens, ctx, None)[0]
+    assert "block" not in token_features(pre, tokens, ctx, None, use_context=False)[0]
+
+    model = CrfExtractor.train(
+        [(p, r) for _, p, r in data], tmp_path / "b", use_pos=False, use_context=False
+    )
+    assert model.metadata["use_context"] is False
+    reloaded = CrfExtractor.load(model.directory)
+    assert reloaded.use_context is False
+    assert reloaded.extract(pre, rec.advisory_id) == model.extract(pre, rec.advisory_id)
+
+    # Bundles written before the ablation existed have no use_context key: context was on.
+    meta = json.loads((model.directory / "metadata.json").read_text())
+    del meta["use_context"]
+    (model.directory / "metadata.json").write_text(json.dumps(meta))
+    assert CrfExtractor.load(model.directory).use_context is True
+
+
+def test_training_budget_is_a_seeded_subset(tmp_path: Path) -> None:
+    examples = [(p, r) for _, p, r in make_dataset(20, TRAIN_VOCAB, seed=8, prefix="lc")]
+
+    def hash_of(seed: int, name: str) -> str:
+        model = CrfExtractor.train(
+            examples, tmp_path / name, use_pos=False, max_train_documents=8, seed=seed
+        )
+        assert model.metadata["training_documents"] == 8
+        assert model.metadata["hyperparameters"]["max_train_documents"] == 8
+        return str(model.metadata["training_hash"])
+
+    assert hash_of(0, "a") == hash_of(0, "b")
+    assert hash_of(0, "a") != hash_of(1, "c")
+
+
+def test_crf_settings_reject_unknown_keys() -> None:
+    from triggermap.models.experiment import crf_settings
+
+    settings = {"family": "crf", "model_id": "m", "use_context": False}
+    assert crf_settings(settings) == {"model_id": "m", "use_context": False}
+    with pytest.raises(ValueError, match="use_contxt"):
+        crf_settings({"model_id": "m", "use_contxt": False})
+
+
+def test_crf_experiment_cli(tmp_path: Path) -> None:
+    from tests.factories import write_split_manifest
+    from triggermap.cli import main
+
+    data = make_dataset(24, TRAIN_VOCAB, seed=9, prefix="exp")
+    processed = tmp_path / "processed"
+    processed.mkdir()
+    (processed / "advisories.jsonl").write_text(
+        "".join(doc.model_dump_json() + "\n" for doc, _, _ in data)
+    )
+    gold = tmp_path / "gold.jsonl"
+    gold.write_text("".join(rec.model_dump_json() + "\n" for _, _, rec in data))
+    split = write_split_manifest(
+        tmp_path / "split.json",
+        {rec.advisory_id: ("train" if i < 16 else "dev") for i, (_, _, rec) in enumerate(data)},
+    )
+    config = tmp_path / "crf.toml"
+    config.write_text(
+        '[train]\nfamily = "crf"\nmodel_id = "c"\nuse_pos = false\n\n'
+        '[[runs]]\nname = "full"\n\n[[runs]]\nname = "no-context"\nuse_context = false\n\n'
+        '[[runs]]\nname = "docs8-seed0"\nmax_train_documents = 8\n'
+    )
+    report = tmp_path / "report.md"
+    args = ["experiment", "--config", str(config), "--gold", str(gold), "--samples", "20"]
+    args += ["--out-root", str(tmp_path / "runs"), "--report", str(report)]
+    args += ["--processed", str(processed), "--split-manifest", str(split)]
+    assert main(args) == 0
+    text = report.read_text()
+    assert "| c-full | 16 |" in text and "| c-docs8-seed0 | 8 |" in text
+    assert (
+        json.loads((tmp_path / "runs/c-no-context/metadata.json").read_text())["use_context"]
+        is False
+    )
+    assert "train_seconds" not in (tmp_path / "runs/c-full/metadata.json").read_text()
+    assert main(args) == 0  # resume reuses the bundles
diff --git a/tests/test_transformer.py b/tests/test_transformer.py
index da0e835..b4d3001 100644
--- a/tests/test_transformer.py
+++ b/tests/test_transformer.py
@@ -178,17 +178,21 @@ def test_ablations_train(base: Path, tmp_path: Path) -> None:
 def test_committed_experiment_configs_are_valid() -> None:
     import tomllib
 
-    from triggermap.models.experiment import plan
+    from triggermap.models.experiment import crf_settings, plan
 
     root = Path(__file__).resolve().parents[1] / "configs/experiments"
     counts = {}
     for path in sorted(root.glob("*.toml")):
-        runs = plan(tomllib.loads(path.read_text()))
+        config = tomllib.loads(path.read_text())
+        runs = plan(config)
         for run in runs:
-            TrainConfig(**run.settings)  # extra="forbid": typos fail here
+            if config["train"].get("family") == "crf":
+                crf_settings(run.settings)  # unknown keys fail here
+            else:
+                TrainConfig(**run.settings)  # extra="forbid": typos fail here
         assert len({r.name for r in runs}) == len(runs)
         counts[path.stem] = len(runs)
-    assert counts == {"ablations": 15, "deberta": 12, "distilbert": 12}
+    assert counts == {"ablations": 15, "crf-ablations": 15, "deberta": 12, "distilbert": 12}
 
 
 def test_bio_weighting_modes() -> None: