Paired tool-use trajectories and one-shot per-step probes of Bielik-PL-11B-v3.0-Instruct and Bielik-11B-v3.0-Instruct, compared with a step-composition prediction

https://github.com/stw2/tokenizer-science-tax @ df809d2de201ff885d619d31aa541a944f68703c

Reference checked 2026-09-14 13:17 UTC. No code was executed or scientific result verified.

• experiments/E10-agentic-compounding/DESIGN.md
• experiments/E10-agentic-compounding/scripts
• experiments/E10-agentic-compounding/configs

Prediction

Unchanged from the locked design: the end-to-end gap exceeds the predicted gap; the gap is larger with English instructions; the APT4 model's English token cost and overflow or budget exhaustion are higher.

Protocol

01 builds 100 task instances from 20 templates and freezes the manifest. 03 runs every instance with English and with Polish instructions through a ReAct tool loop with a persistent Python sandbox, one model at a time: at most 8 steps, 700 generated tokens per step, a FINAL answer ends a trajectory only in a turn without code. 02 poses every instance one-shot as a per-step probe. 04 grades trajectories with golden-gated graders (numeric tolerance, hidden tests); a write-and-debug solution is rebuilt from the executed code when no solution.py was written. 05 computes the end-to-end gap pooled, per language and per family with a paired bootstrap, the language interaction, the English token ratio and non-finalization rates, the step-composition prediction and Holm-adjusted p for H2 and H3. 06 writes flat headline values.

Dataset

100 synthetic task instances generated by 20 parametric templates (numpy PCG64, seed 20260707 plus a template hash and the instance index), each with English and hand-translated Polish instructions; no public benchmark items.

Split

None: both models run every instance with both instruction languages.

Access needs

Both Bielik 11B repositories on Hugging Face are gated; the two MLX bf16 conversions are restricted materials. Task instances rebuild from the public generators.

Configurations

Tool loop: at most 8 steps, 700 generated tokens per step, context window 32,768 tokens, observations cut at 2,000 characters, 20 s per step; one-shot probes: 900 generated tokens; greedy decoding; system prompts calib-2.

Metric

End-to-end success gap (original minus APT4 model) pooled, per instruction language and per family with 95% paired bootstrap intervals; the language interaction DD; the English prompt-side token ratio and overflow or budget-exhaustion rates; the predicted gap and D = observed minus predicted gap.

Seeds

20260707 (instance generation base seed and bootstrap)

Interpretation rule

H1: confirmed if D, the end-to-end gap minus the predicted gap, is above 0 with a 95% interval excluding 0; refuted if the interval's upper bound is below 0.02; partial otherwise. The interval comes from a paired bootstrap over instances (2000 resamples) with the prediction recomputed in each resample. H2: DD above 0 predicted; Holm-adjusted with H3. H3: English prompt-side token ratio above 1.10 and a higher English overflow or budget-exhaustion rate for the APT4 model; Holm-adjusted with H2. Trajectories that end without an accepted FINAL answer count as failures.

Resources

Apple silicon, 128 GB unified memory, MLX bf16, one 11B model resident at a time; at most about 4.5 million generated tokens for trajectories and 0.2 million for probes.

Prior work

arXiv:2604.10799v1 compares the pair on static benchmarks only.