Score 1.6-3.1B models: SmolLM3-3B, Qwen3.5-2B, LFM3-1.7B, granite-1B

README.md

@@ -20,9 +20,13 @@ configs:
     path: data/deeparithmark-1.jsonl
 ---
 
+# Deep Arithmark 1
 
-![image](https://cdn-uploads.huggingface.co/production/uploads/69c842686cf758859915159c/teBgMrvtiuqUW6ISAoPzx.png)
-
+**1,000 arithmetic word problems that need 2–5 dependent operations.** Same
+format as [ArithMark 3.0](https://huggingface.co/datasets/AxiomicLabs/Arithmark-3.0)
+— finish the sentence, pick one of four continuations, chance 25 % — but the
+wrong answers are no longer ±1 of the right one, so a magnitude guess does not
+work.
 
 > **Unofficial.** Deep Arithmark is an independent companion tier. It is not
 > affiliated with, endorsed by, or published by AxiomicLabs, the authors of
@@ -123,10 +127,21 @@ Zero-shot, `acc_norm`, bf16, batch 32, `--max-context 1024`. ArithMark 3.0
 numbers were produced on the same weights in the same run, not copied from a
 table. `soft` is the mean probability of the correct option after a softmax over
 the four scores; `d2`–`d5` are `acc_norm` per depth tier (250 items each, chance
-25 %). Δ is Deep Arithmark minus ArithMark 3.0.
+25 %). Δ is Deep Arithmark minus ArithMark 3.0. Every row is in one file,
+`benchmarks/deeparithmark_17models.json`.
+
+`LiquidAI/LFM3-1.7B-Base` ships remote modelling code written against
+transformers 4.x — it decorates its convolution with a hub-kernel helper that
+5.x removed — so the scorer stubs two deleted helpers and runs the checkpoint's
+own pure-torch path. Re-scoring it at batch 8 instead of 32 moved two items
+(31.90 % against 31.70 %), which is bf16 kernel noise, not a padding leak.
 
 | Model | Params | Deep | soft | d2 | d3 | d4 | d5 | ArithMark-3 | Δ |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| SmolLM3-3B-Base (2025-06) | 3.08 B | 36.50 % | 27.09 % | 57.6 | 35.6 | 26.0 | 26.8 | 75.70 % | −39.20 |
+| Qwen3.5-2B-Base (2026-02) | 1.88 B | 36.00 % | 27.01 % | 59.2 | 26.4 | 28.8 | 29.6 | 86.70 % | −50.70 |
+| LFM3-1.7B-Base (2026-10) | 1.73 B | 31.70 % | 26.42 % | 41.2 | 38.4 | 23.6 | 23.6 | **93.10 %** | **−61.40** |
+| granite-4.0-1b-base (2025-10) | 1.63 B | 29.90 % | 26.13 % | 39.2 | 29.2 | 28.4 | 22.8 | 84.20 % | −54.30 |
 | Qwen2.5-1.5B-Instruct | 1.54 B | 38.00 % | 27.23 % | 60.4 | 40.8 | 28.4 | 22.4 | — | — |
 | Qwen3.5-0.8B-Base | 752 M | 26.70 % | 25.48 % | 28.8 | 24.4 | 28.8 | 24.8 | 75.40 % | −48.70 |
 | Tev1-0.8B-experimental | 752 M | 23.10 % | 25.30 % | 20.8 | 24.4 | 23.6 | 23.6 | 71.40 % | −48.30 |
@@ -146,11 +161,14 @@ the four scores; `d2`–`d5` are `acc_norm` per depth tier (250 items each, chan
 architecture rather than two models. `gemma-3-270m` and `MobileLLM-R1-950M` are
 gated and have not been scored.
 
-**Three bands.** Below ~350 M nothing clears chance, and two models sit
+**Four bands.** Below ~350 M nothing clears chance, and two models sit
 significantly *below* it — nanowhale-100M (21.3 %, p = 0.007) and LFM2.5-350M
 (20.0 %, p = 0.0002) — meaning a stable losing strategy rather than absent
 arithmetic encoding. **Qwen3-0.6B-Base is the first sub-1B model to clear chance
-significantly: 29.20 %, p = 0.0027.** Above 1B the depth ladder is fully readable.
+significantly: 29.20 %, p = 0.0027.** The 1.6–3.1 B band clears it comfortably
+and then stalls at 29.9–36.5 % (p = 4.5e−4 to 8e−16). Only the post-trained
+Qwen2.5-1.5B-Instruct declines smoothly all the way down (60.4 → 40.8 → 28.4 →
+22.4); every *base* model from 1.6 B to 3.1 B is back at chance by depth 4.
 
 **The one to watch is Qwen3-0.6B's depth curve:** 37.6 → 32.0 → 28.8 → **18.4 %**,
 significant at both ends (depth 2 p = 1e-5, depth 5 p = 0.016). Being *below*
@@ -162,6 +180,24 @@ across depth, so no rank preference can produce that shape.
 Base scores **75.70 %** on ArithMark 3.0, the highest sub-1B result here, and
 24.10 % on this dataset — a **51.6-point** drop, against GPT-X3-150M's 24.4.
 
+**The extreme case is now LFM3-1.7B-Base.** It scores **93.10 %** on ArithMark
+3.0 — the highest figure in this table — and 31.70 % here: a **61.4-point** drop,
+larger than granite-4.0-350M's 51.6. It is also the only model whose depth-3
+bucket stays within three points of its depth-2 bucket with both above chance
+(41.2 → 38.4, p = 3e−6) before falling off a cliff at depth 4 (23.6). That skill is concentrated in two templates:
+`share_then_split` 93.2 % and `time_clock_end` 77.3 %, with `money_discount_fee`
+at 3.3 %. A 1.7 B model that nearly aces another benchmark is still a
+single-template specialist here.
+
+**Scaling 0.6 B → 3.1 B buys about seven points.** Qwen3-0.6B 29.2,
+granite-1B 29.9, LFM3-1.7B 31.7, Qwen3.5-2B 36.0, SmolLM3-3B 36.5: a 5× parameter
+increase for +7.3 points, while ArithMark 3.0 on the same runs spans 68.4 → 93.1.
+The aggregate also hides opposite failure shapes at the same level. Qwen3.5-2B
+has the best depth-2 bucket in the table (59.2 %, p = 2e−30) and collapses at
+depth 3 (26.4 %); SmolLM3-3B is weaker at depth 2 (57.6) yet still holds depth 3
+(35.6 %, p = 2e−4). Same ~36 %, different structure — which is why the curve is
+the reportable unit, not the single number.
+
 **A chance score is not always guessing.** GPT-X3-150M scores 49.60 % on
 ArithMark 3.0 and 25.20 % here, but its per-template accuracy spans 3.0 % to
 54.5 % (χ² = 63.2, df = 14, p < 1e-4): a small-number prior (picks the smallest
@@ -170,8 +206,9 @@ family. These cancel to chance in aggregate, so treat per-template rows as
 preference, not partial skill. Worked through in the design notes, §6.
 
 For a sub-1B leaderboard, report the per-depth curve rather than only the single
-number. At 1.5B-instruct the two solvable templates are `share_then_split`
-(86.4 %) and `time_clock_end` (85.9 %).
+number. The two solvable templates are stable across families: at 1.5B-instruct
+`share_then_split` (86.4 %) and `time_clock_end` (85.9 %), at 1.7 B `share_then_split`
+93.2 %, and at 2 B `time_clock_end` 90.6 %.
 
 ## Composition
 
@@ -259,3 +296,13 @@ copied code.
 **Trademarks.** "ArithMark" and "AxiomicLabs" remain the property of their owner.
 Their use here is descriptive, to state which benchmark this one extends. It does
 not imply affiliation or endorsement — see the notice at the top.
+
+## Files
+
+| File | What it is |
+| --- | --- |
+| `data/deeparithmark-1.jsonl` | The dataset — 1,000 items, one JSON object per line |
+| `deeparithmark.py` | Deterministic generator, plus `selftest` and the shortcut `audit` |
+| `bench_deeparithmark.py` | Scorer with per-depth and per-template breakdowns and an ArithMark 3.0 comparison |
+| `DEEPARITHMARK_DESIGN.md` | Design rationale, fairness audit, measurement discussion |
+| `benchmarks/deeparithmark_6models.json` | Raw results for the 2026 sub-1B sweep |