Kev-4B

Kev-4B is a decision model: one document (the state) and a set of typed questions in, a probability distribution per question out, in one forward pass. No text generation. It is a LoRA adapter (r=16, 33.8M trainable parameters) plus a pointer head on Qwen/Qwen3.5-4B-Base (revision 1001bb4d), serving TypeSafe's public /v1/systemone contract.

The recommended Kev. The best accuracy per byte: out of domain 0.797 on the development partition and 0.837 on the locked test, Brier 0.255 on the test, held-out rule pairs 0.77–0.78. This checkpoint is the decision-v7 recipe (trial q35-4b-s23/00-trial-0, seed 2, selected on development accuracy) followed by a 9-minute delta fine-tune (--init_from, lr 2e-5, one epoch) on 1,425 additional records — date-bearing policy cases rendered with explicit day counts, and evidence-free cases with uniform targets — mixed with 2,000 replayed training records. Against the pre-delta checkpoint on the locked test: +1.0 pp [−0.1, +2.1], Brier 0.266 → 0.255, deadline 0.65 → 0.75.

• Hub: jaredpalmer/kev-4b (this repo; trial night2-4b-du/00-trial-0). The pre-delta checkpoint is at revision v7-base; the Qwen3 generation at qwen3 (its card).
• Code, suites, every trial with hashes and paired bootstraps: github.com/jaredpalmer/kev — PLAN_Qwen35.md (the port and this experiment), PLAN.md, runs/leaderboard.md

Results (same frozen items for every row)

	Kev-4B (Qwen3)	Kev-4B before the delta (v7-base)	Kev-4B	Kev-4B, calibrated (T = 2.0)	Jev
in-distribution accuracy (decision-v7 dev, 1,204 records)	0.854	0.877	0.872	0.872	0.845
out-of-domain accuracy (transfer-v4 dev, 764 records)	0.790	0.794	0.797	0.797	0.857
out-of-domain Brier	0.328	0.316	0.299	0.265	0.211
out-of-domain ECE	0.102	0.130	0.122	0.048	0.049
confident errors out of domain (p ≥ 0.9 and wrong)	8.2%	8.2%	6.9%	3.2%	3.7%
coverage at ≤ 5% error (share of decisions automatable)	0.31	0.54	0.54	0.58	0.70
held-out policy structures, both siblings correct	0.73	0.78	0.78	0.78	0.86
unknowable items answered at ≥ 0.9 (lower is better; transfer-v9)	0.44	0.19	0.00	0.00	0.09
locked test, out-of-domain accuracy / Brier	0.806 / 0.294	0.832 / 0.266	0.837 / 0.255	–	–
locked test, in-distribution accuracy	0.856	0.870	0.871	–	–

Per-source out-of-domain accuracy (Kev-4B / Jev): QNLI 0.91 / 0.93, SciQ 0.97 / 0.99, TweetEval-offensive 0.74 / 0.81, PAWS 0.74 / 0.79, MMLU 0.70 / 0.90, Emotion 0.56 / 0.59, deadline (3-level date arithmetic) 0.60 / 0.93 — 0.85 with the date_facts preprocessor (below), (A or B) and C 0.91 / 0.91, (A and B) or not C 0.88 / 0.97, if A then not B else C 1.00 / 0.78.

Calibrated row. A single temperature T = 2.0 fitted on the in-distribution development rows and applied out of domain (scripts/temperature_groups.py). It leaves accuracy and the confidence ranking unchanged and brings ECE and confident errors to Jev's level; it is applied only when you ask for it (KEV_TEMPERATURE=2.0 in kev.serve). Per-(type, option-count) temperatures were tested and are worse out of domain.

date_facts preprocessor. Kev, like every Kev before it, cannot subtract dates reliably (the untrained base can; LoRA training erodes it). It can use a stated day count. KEV_DATE_FACTS=1 appends one sentence per pair of absolute dates found in the state ("June 26, 2026 is 8 days before July 4, 2026"); this checkpoint was trained on such renderings, so with it deadline goes from 0.60 to 0.85 and overall out-of-domain accuracy from 0.797 to 0.820. It is preprocessing, reported separately, never folded into the model's own numbers.

What the delta cost. MMLU-Pro fell 0.500 → 0.490 and scienthoon's ECE rose 0.086 → 0.116; coverage at ≤ 5% error was unchanged (0.54 development, 0.67 → 0.68 locked test) and confident errors fell (8.2% → 6.9%). The pre-registered criteria for the delta (PLAN.md, "Tonight's autoresearch") were met for dates and for the unknowable-confidence behaviour; the coverage criterion asked for +5 pp and got 0; the locked read decided promotion.

Newer evaluation columns (transfer-v9 development, Kev-4B / Jev): MMLU-Pro (10-way) 0.490 / 0.840; state buried among unrelated records 0.68 / 0.70; unknowable share at ≥ 0.9 confidence 0.00 / 0.09 (intact controls 0.94).

External suites (same items as their published Jev numbers): SemIf's authored 144 — 0.896 before the delta (live Jev 0.965; SemIf's untrained Qwen3.5-4B 0.813); scienthoon's 900 tickets — queue 0.918, angry 0.790, ECE 0.116 (Jev 0.897, 0.914, 0.105). On ekzhang's 1,000-question MMLU-Pro sample the pre-delta checkpoint scores 0.468 (Jev 0.829).

How it was built

• Base model: Qwen3.5-4B-Base, a hybrid of 24 Gated DeltaNet (linear attention) layers and 8 full-attention layers. Because the recurrent layers cannot honour a block-causal mask, questions run as separate causal rows that continue from the shared state (kev/model.py: forward_rows_batch); isolation is exact by construction (together vs alone within 1e-5) and on attention-only models this form is bit-identical to the packed one.
• Recipe: decision-v7, two epochs, LoRA r=16 (attention, MLP and DeltaNet projections), lr 5e-5 — the same data and settings as every other Kev, so the Qwen3 → Qwen3.5 difference is the base (PLAN_Qwen35.md §10: locked test +7.3 pp [+2.8, +11.7] over Kev-8B).
• Delta: kev.train --init_from jaredpalmer/kev-4b@v7-base --data evals/night2/dates_unknowable.jsonl --replay 2000 --lr 2e-5 --epochs 1. The 1,425 new records are generated (no public dataset): 900 date-bearing policy cases, a third rendered plainly, a third with a relational day-count sentence, a third with a date_facts field; 255 cases with the deciding sentence removed and a uniform soft target over the options, plus their 270 intact controls. Record hashes are in evals/night2/manifest.json; the source checkpoint's hashes are in training_config.json.
• Why a delta and not a retrain: it is a controlled change (one fixed checkpoint, one data addition, 9 minutes), and the results section shows exactly what it moved.

Known limits

• Use Kev-9B when accuracy and calibration matter more than memory: 0.852 vs 0.837 out of domain on the locked test, Brier 0.237 vs 0.255.

• Slow on a Mac. The DeltaNet kernels have no MPS implementation; PyTorch falls back to reference code. A five-question request that takes 0.17 s on the Qwen3 Kev-4B takes 0.78 s here in bf16 on an M5. On CUDA with flash-linear-attention installed it is fast. Use jaredpalmer/kev-4b@qwen3 for low latency on Apple Silicon until an MLX path exists.

• Requires transformers >= 5.17 (the qwen3_5 architecture) and peft >= 0.21.

• Knowledge (MMLU 0.70 vs Jev 0.90; MMLU-Pro 0.490 vs 0.840), TweetEval (0.74 vs 0.81) and noisy-label Emotion (0.56 vs 0.59) are the remaining gap; knowledge is set by the base (the untrained Qwen3.5-4B scores the same).

• Date arithmetic without the preprocessor: deadline 0.60 (Jev 0.93). With KEV_DATE_FACTS=1: 0.85.

• Raw probabilities are over-confident out of domain (ECE 0.122); KEV_TEMPERATURE=2.0 fixes most of it (ECE 0.048) without changing any answer. Coverage at a 5% error budget is 0.54–0.68 against Jev's 0.70.

• 4B bf16 needs ~9 GB of GPU memory for serving; training took 56 min on one H100 (peak 24.6 GB).

Training

Frozen suite evals/v7/decision-v7: 10,000 public records (1,000 per source), 896 policy minimal-pair records over nine template families, 1,680 records from 60 randomly generated rule structures in four rendering styles. Two epochs, LoRA r=16 α=32 on q/k/v/o_proj, gate/up/down_proj, in_proj_qkv/z/a/b, out_proj; pointer head from scratch; cross-entropy on the option distribution; lr 5e-5 (OneCycle), effective batch 8, bf16 autocast with fp32 master weights, gradient checkpointing; option permutation, none-of-the-above insertion, distractors, none minimal pairs on 25% of Choice records. Then the delta described above (one epoch, lr 2e-5, 3,937 records seen, 9 minutes on one H100). No Jev outputs were used for training.

Evaluation protocol

Development partitions select models; the locked test partition is read at most once per candidate (runs/locked/kev-4b-night2-du-ungated/; the pre-delta read is runs/locked/kev-4b-q35/). Every number carries suite hash, code hashes and git commit in result.json. Untrained-base baselines use zero-shot letter logits on the same items (scripts/base_mmlu_probe.py).

Use

uv run --extra serve python -m kev.serve --run jaredpalmer/kev-4b --port 8008      # KEV_DTYPE=bf16 on a Mac; slow on MPS, see limits
KEV_TEMPERATURE=2.0 KEV_DATE_FACTS=1 uv run --extra serve python -m kev.serve --run jaredpalmer/kev-4b --port 8008   # calibrated probabilities + date preprocessing

Any TypeSafe-compatible client works: TypeSafeClient(api_key="local", base_url="http://127.0.0.1:8008", model="kev-latest").

License

Apache-2.0 for the adapter and head; the Qwen3.5 base is Apache-2.0; datasets carry their own licenses.