taesiri/BlindLoop-Difficulty-Feedback

BlindLoop Difficulty Feedback

This is the public, hash-bound release of BlindLoop Section 3. Coding agents generated executable visual-question tasks; each task's inverse program checked the answer from rendered pixels. For complete feedback transactions, the exact same five images were evaluated by three frontier VLMs and the resulting difficulty signal was returned to the next generation episode.

Contents

Config	Unit	Rows
tasks	generated task	266
sources	candidate source file	2,926
replay	replayed visual-question prompt	78,659
feedback_eval5	exact in-loop feedback image	1,260
evaluator_responses	evaluator response	3,780
hard_eval5	exact image from a frozen hard task	105
audit_incomplete_tasks	incomplete feedback transaction	14
audit_incomplete_responses	durable response from an incomplete transaction	45
audit_replay_quarantine	replay-quarantined task	2

The release contains 266 generated tasks from five builder configurations and two frozen seed pools. Of these, 252 have complete 5-image x 3-evaluator feedback transactions, 21 satisfy the frozen panel-hard rule, 264 replay successfully, and 2 remain quarantined.

Frozen hardness rule

An evaluator fails a task when it answers at most 3 of the 5 exact feedback images correctly. A task is panel-hard when at least 2 of the 3 evaluators fail it. The 21 hard tasks comprise 5 consensus_hard tasks and 16 majority_hard tasks. This is panel-specific model difficulty, not a human difficulty label.

The evaluator panel is:

• openai/gpt-5.6-sol at high reasoning;
• anthropic/claude-opus-5 at high reasoning;
• google/gemini-3.7-flash at high reasoning.

Exact feedback panels

feedback_eval5 contains the original images evaluated inside the loop. It is not a new replay sample. Every image is checked against the SHA-256 recorded in the original sample-set.json. hard_eval5 is an ID- and hash-preserving view of the 21 hard tasks.

Safe evaluation boundary

The dataset contains gold answers for scoring. A model request must use only:

• image;
• question;
• answer_options.

Never serialize an entire row into the prompt. In particular, do not expose source code, scene specifications, oracle output, hardness labels, prior model responses, or gold answers. The gold-free request ledger is ledgers/feedback_eval5_inputs.csv; answers are isolated in ledgers/feedback_eval5_gold.csv.

Mechanical validity, replay, and difficulty are different

• machine_validated means the submitted executable task passed the protected program and pixel-verification gates.
• replay_status=verified means the archived task successfully generated the requested offline replay dataset.
• hardness_class summarizes performance of the frozen three-model panel.
• human_admission remains pending and canonical_world_member is false for every task in this release.

Source programs

The sources config stores forward renderer/generator code, inverse oracle/verifier code, tests, metadata, and supporting text as searchable UTF-8 rows. No opaque source tarball is required to inspect the tasks.

Known boundaries

• Fourteen feedback transactions are incomplete and are excluded from all accuracy and hardness claims. Their durable evidence is preserved in audit configs.
• Two tasks failed the requested 200-scene replay and are excluded from replay.
• Seven arms use episodic-sequential@0.7.0; three corrected arms use episodic-sequential@0.8.0. Cross-revision aggregates are descriptive unless the guard revision is modeled.
• The builders and evaluator panel do not span all model families.
• No signed human-admission study is included in this release.

Loading

from datasets import load_dataset

panels = load_dataset(
    "taesiri/BlindLoop-Difficulty-Feedback",
    "feedback_eval5",
    split="test",
    revision="v0.1.0",
)
row = panels[0]
row["image"].show()
print(row["question"], row["answer_options"], row["oracle_answer"])

For a paper run, replace the tag with the full immutable Hugging Face commit SHA recorded in manifests/remote_release.json.