cloudlabworks/intelligence-ladder

Intelligence Ladder

Per-trial results and frozen task files for a seven-rung, behaviourally graded Python coding benchmark. It measures how far up a ladder of harder task families a model climbs under one fixed protocol, and how consistently, for hosted API models and a 4B model on an owned edge NPU node.

Dataset summary

• 1309 trials across 8 models: 28 tasks (7 rungs x 4) x (3 productive + 3 trap repeats) = 168 per model; qwen-plus has 133 (stopped during R6, R7 not run).
• Grading is behavioural only: hidden asserts run under pytest, planted mutants, existing and feature test suites. No LLM judge, no text similarity.
• Traps: an impossible variant of each task whose only correct answer is UNKNOWN. Traps are reported separately and never rescue productive failures.
• Solved task: at least 2 of 3 productive repeats pass.

Rung	Family
R1	Simple functions
R2	Edge cases / Unicode / nonmutation
R3	Three-file package repairs
R4	Tool-mediated debugging (simulated read tool, exact call accounting)
R5	Bounded two-turn revisions (revised spec must reject the old behaviour)
R6	Implementation plus self-authored tests that kill at least 4 of 5 planted mutants
R7	Multi-file package feature (three required source files changed; existing and feature tests green)

Configurations

results (data/results.jsonl)

One row per trial.

Field	Type	Meaning
id	string	`-<code
task, rung, repeat, trap	string, int, int, bool	Task id (e.g. R4-C), rung 1–7, repeat 1–3, trap flag
model	string	Result label; see data/models.json for model ref, route and hardware class
freeze_sha256	string	sha256 of the freeze manifest the trial ran under (both shipped in frozen/)
score.pass	bool	Trial verdict
score.*	mixed	Structural fields by rung: returncode, kind (trap), tool_exact, actual_calls, initial/final/obsolete_rejected (R5), behavior/tests_on_reference/mutants/mutants_killed/mutation_kill_rate (R6), existing/feature/changed_paths (R7), error (harness rejection message only)
wall_s	float	Trial wall time, seconds, including transport and scoring
turn_metrics[]	list	Per model turn: wall_s, ttft_s, tok_s, inference_s (RK1), output_tokens, usage.{prompt,completion,total,reasoning,cached}_tokens where the route reports them, metric_source
evaluation_repair	string	Present on 3 rows re-scored from the saved answer with the frozen evaluator (no new model call)

score has a rung-dependent shape; load with json if your tooling needs a fixed schema.

leaderboard (data/leaderboard.jsonl, same rows as results/leaderboard.csv)

One row per model, generated by reproduce.py --write from data/results.jsonl: tasks solved with cluster-bootstrap 95% CI, productive pass rate with CI, eligible families, ladder height, trap abstention, coverage, token totals, median and p90 trial wall time, median TTFT and median tok/s (non-reasoning turns).

Frozen files

frozen/ holds byte-identical copies of the task files (rungs/R1–R7.json, which include the grading asserts, reference solutions for R6 and package files), the prompt builder, both evaluators, the protocol and the freeze manifests. reproduce.py checks every shipped file against the manifests. Reserved held-out variants and the site-specific runner are withheld; their hashes stay in the manifest so a later release can prove they were not changed (see frozen/WITHHELD.md).

Models

Label	Model ref	Route	Hardware class
astra	openai/gpt-6-astra	OpenRouter, streaming	hosted API
deepseek-v4-flash	deepseek/deepseek-v4-flash	OpenRouter, streaming	hosted API
glm-4.7-flash	z-ai/glm-4.7-flash	OpenRouter, streaming	hosted API
gpt-5-nano	openai/gpt-5-nano	OpenRouter, streaming	hosted API
qwen3.7-flash	qwen/qwen3.7-flash	OpenRouter, streaming	hosted API
alibaba/qwen-flash	qwen-flash	Alibaba Cloud Model Studio, buffered, thinking off	hosted API
alibaba/qwen-plus	qwen-plus	Alibaba Cloud Model Studio, buffered, thinking off	hosted API
RK1-Qwen3-4B	Qwen3-4B-Instruct-2507, RKLLM W8A8, context 2048	local NPU via RKLLM runtime 1.3.1	edge SoC: Turing RK1, Rockchip RK3588, 3-core NPU, 32 GB

Output caps: 512 tokens on R1–R5 and 2048 on R6–R7 for hosted routes; RK1 kept its native 512-token output and 2048-token context on every rung.

Intended uses

• Compare where on the ladder different models stop under a fixed, small output cap.
• Compare an edge NPU model with hosted models on identical tasks and grading.
• Study trap abstention and reasoning-token exhaustion under a fixed cap.
• Template for adding a model with the public runner (harness/run_model.py).

Limitations and biases

• 28 tasks and 3 repeats at temperature 0: intervals are wide and repeats are not independent.
• The task files are now public. Any model trained after this release may have seen them; treat new results as contaminated unless checked against the withheld variants.
• Resource caps differ between RK1 and hosted routes on R6/R7.
• Latency comes from different instruments per route and includes transport; it is not a serving benchmark. Hosted serving hardware is not disclosed. Power was not metered.
• qwen-plus coverage is incomplete. Hosted models can change behind a stable model ref; results describe the runs made on 2026-10-27 and 2026-10-28.
• English-only prompts; Python-only tasks.

Personal and sensitive information

None. The dataset holds synthetic programming tasks and machine-generated scores. Model answer text, evaluator logs and all operator or infrastructure details were removed before release.

Licence

Data, task files and documentation: CC BY 4.0. Code in the companion repository: MIT.

Citation

@misc{intelligence_ladder_2026,
  title  = {Intelligence Ladder: a frozen, behaviourally graded coding ladder for hosted and edge models},
  author = {cloudlabworks},
  year   = {2026},
  note   = {Dataset and reproduce scripts}
}