BioAlign-EditDistance-HardCases-10K

RegalFire — AI Data Foundry. An original synthetic biology / computational sequence alignment computational fixture benchmark.

Problem and differentiator

Compute global unit-cost Levenshtein distance between two synthetic sequences and decide whether it is within a supplied threshold.

Why download this instead of existing data?

Each short sequence pair has a globally optimal unit-cost distance, an alignment witness and an exact threshold decision. Homopolymers, tandem repeats and clustered edits create non-unique edit descriptions; this release tests minimum-distance correctness without treating injected edit counts as truth.

Research context: github.com, github.com, arxiv.org. These references support the relevance of the task area; they do not establish customer demand or that this release improves any model.

Verified release

10,000 accepted records; 10,000 unique IDs; 10,000 unique semantic task fingerprints; 10,000 source groups. Zero rejected records. Independent computational validation: PASS. Full restarted generation was byte-identical (see reproduction_report.json). No model training/evaluation run is claimed.

Split	Records
train	8071
validation	960
test	969

Construction family	Records
clustered_indels	2000
homopolymer	2000
literal_N	2000
reverse_complement	2000
tandem_repeat	2000

Ground-truth contract

A/C/G/T/N are literal symbols: N is not a wildcard. Substitution, insertion and deletion each cost 1; matching costs 0. No affine gaps, local alignment or automatic strand choice. The reverse_complement family explicitly pairs a sequence with its reverse complement but still evaluates the ordered pair as supplied. source_sequence equals a and defines the source split group. Alignment witness strings use - for gaps and can be non-unique; any optimal witness is valid. Threshold decision is distance <= threshold.

Generator: full dynamic-programming distance and traceback. Validator: independently authored Myers bit-vector distance, then sequence reconstruction and witness-cost checks. Exhaustive small-alphabet unit tests cross-check both algorithms.

Record and file structure

Each JSONL row has id, index, seed, family, input, expected, fingerprint, group_id, split and provenance. Input contains the complete task; expected contains computable labels and witnesses where relevant. schema.json specifies exact field/type requirements. There are five full worked examples in examples/sample.json. data/dataset.jsonl is the complete canonical dataset; HF default loading uses only the disjoint train/validation/test files.

For agent evaluation, remove expected before passing inputs to the agent and keep the answer files inaccessible to it. These are public fixed-label fixtures, not contamination-resistant secret evaluations or recorded agent rollouts.

Reproduce and validate

Seed: 20261001. Published source and the generation environment are recorded in SOURCE_LICENSE.json. Exact byte reproduction was verified in that environment; matching seed alone is not a guarantee across Python versions.

pip install -r requirements.txt
python generator/generate.py --out reproduced --count 10000 --seed 20261001
python validator/validate.py --dataset reproduced/dataset.jsonl --count 10000 --out reproduced_qa
python generator/generate.py --out reproduced --count 10000 --seed 20261001 --resume
python -m unittest discover -s tests -p test_oracles.py -v

Writes use committed 500-record chunks, atomic checkpoint updates and fresh final assembly. Resume loads committed shards, verifies binding to seed/count/source, and regenerates any uncommitted work. A missing final file or checkpoint does not destroy committed records. Do not mix different seeds, counts or generator versions in one output folder.

Generator and validator implement different label algorithms; shared helpers only cover serialization, schema and canonical semantic identities. This is independent computational implementation, not independent human scientific review. Nine unit tests cover label corruption, exact small cases, schema types, missing/corrupt files, semantic symmetries and crash-resume reproduction. All passed locally; see the QA and reproduction reports.

Deduplication, splits and quality limits

Family quotas are equal. Split assignment is deterministic by source-group hash (approximately 80/10/10; exact counts above). Identical source groups never cross splits. Grid rotations/reflections are canonicalized; agent-label swaps are canonicalized in coordination tasks. Interval translation equivalents are canonicalized. DNA source grouping canonicalizes reverse complements. Each paired edit or sequence pair remains within its record.

The DNA near-duplicate audit screens 7-mer bottom-four sketches and at most 25 previous candidates per bucket, then checks Jaccard >=0.85 across split groups. It found 0 flags. This is a bounded similarity screen, not exhaustive homology analysis. Grid and interval checks cover exact stated equivalences, not all possible near-isomorphic or related scenarios. Splits are by generated source, not proven biological species or real game distribution. Public fixtures can be memorized.

Provenance, privacy and licensing

Entirely generated by the original supplied procedural source. No external dataset records, genomes, annotations, patient data, PHI, human identity profiles, proprietary game images, dialogue, maps, textures or binaries were used. Cited tools and papers are research context only; their data/code were not copied. MIT applies to this original release and generator/validator code; retain the LICENSE notice. SOURCE_LICENSE.json records permissions, attribution and environment.

Scope and limitations

Short synthetic strings, not species/genes, homologous protein annotations, experimental mutation effects, functional biology or clinical interpretation. No calibrated sequencing error model. The small computational scoring rule must not be substituted for a biological alignment protocol without validation.

Tests establish the specified computational cases, not universal software correctness, biological validity of a simulation, clinical suitability, benchmark novelty over every related work or market value. Generator and validator were authored with Codex and have not been reviewed by a domain expert.

Commercial extension: Client edit-distance APIs, scoring contracts, sequence formats and private regression fixtures. Domain rules and privacy/access constraints must be agreed and tested before delivery.

Custom / Private Dataset Work

RegalFire builds custom AI datasets, evaluation sets and reproducible data pipelines for research and production systems.

Available adaptations include:

• private schemas
• larger volumes
• custom domains
• custom validators
• private evaluation sets
• continuous generation
• custom environments

Contact: ootiris@gmail.com Hugging Face: RegalFire