FAILURE MAP

Understand the failure.
Verify the repair.

Small, reproducible software failures. The broken implementation, the fix that didn’t work, and the one that passed—preserved together.

Explore the cases ↓How results are verified ↗
100840Executable case variants
20168Distinct failure mechanisms
302520Executed implementations
20168Open-access cases

WHAT THE ARCHIVE CONTAINS

100840 executable cases. 20168 are open.

Every case records the implementation that fails, the fix that did not work, and the repair that passed its checks—with recorded outputs and source hashes. This release adds 100840 cases across 20168 failure mechanisms and 254 domains.

The open tier gives you the failure and the unsuccessful fix for one case in every mechanism. The remaining 80672 cases, 5 variants per mechanism, are member-only: the verified repair, its recorded checks, and the full fixture suite are held in the member archive. Read the methodology ↗

A RECORD OF WHAT WENT WRONG

Browse the archive / 100840

Python · Standard library
REFERENCEFAILURE MECHANISMDOMAINACCESS
FA-99301

Reduce-scatter gives every rank the first slice · case 01

Reduce-scatter gives every rank the first slice.

Distributed training aggregation● Open access↗
FA-99302

Reduce-scatter gives every rank the first slice · case 02

Reduce-scatter gives every rank the first slice.

Distributed training aggregation◈ Members↗
FA-99303

Reduce-scatter gives every rank the first slice · case 03

Reduce-scatter gives every rank the first slice.

Distributed training aggregation◈ Members↗
FA-99304

Reduce-scatter gives every rank the first slice · case 04

Reduce-scatter gives every rank the first slice.

Distributed training aggregation◈ Members↗
FA-99305

Reduce-scatter gives every rank the first slice · case 05

Reduce-scatter gives every rank the first slice.

Distributed training aggregation◈ Members↗
FA-99306

All-reduce silently truncates vectors with mismatched shapes · case 01

All-reduce silently truncates vectors with mismatched shapes.

Distributed training aggregation● Open access↗
FA-99307

All-reduce silently truncates vectors with mismatched shapes · case 02

All-reduce silently truncates vectors with mismatched shapes.

Distributed training aggregation◈ Members↗
FA-99308

All-reduce silently truncates vectors with mismatched shapes · case 03

All-reduce silently truncates vectors with mismatched shapes.

Distributed training aggregation◈ Members↗
FA-99309

All-reduce silently truncates vectors with mismatched shapes · case 04

All-reduce silently truncates vectors with mismatched shapes.

Distributed training aggregation◈ Members↗
FA-99310

All-reduce silently truncates vectors with mismatched shapes · case 05

All-reduce silently truncates vectors with mismatched shapes.

Distributed training aggregation◈ Members↗
FA-99311

Shard values are placed by list position instead of declared offsets · case 01

Shard values are placed by list position instead of declared offsets.

Distributed training aggregation● Open access↗
FA-99312

Shard values are placed by list position instead of declared offsets · case 02

Shard values are placed by list position instead of declared offsets.

Distributed training aggregation◈ Members↗
FA-99313

Shard values are placed by list position instead of declared offsets · case 03

Shard values are placed by list position instead of declared offsets.

Distributed training aggregation◈ Members↗
FA-99314

Shard values are placed by list position instead of declared offsets · case 04

Shard values are placed by list position instead of declared offsets.

Distributed training aggregation◈ Members↗
FA-99315

Shard values are placed by list position instead of declared offsets · case 05

Shard values are placed by list position instead of declared offsets.

Distributed training aggregation◈ Members↗
FA-99316

Collective slot IDs are replaced by row positions · case 01

Collective slot IDs are replaced by row positions.

Distributed training aggregation● Open access↗
FA-99317

Collective slot IDs are replaced by row positions · case 02

Collective slot IDs are replaced by row positions.

Distributed training aggregation◈ Members↗
FA-99318

Collective slot IDs are replaced by row positions · case 03

Collective slot IDs are replaced by row positions.

Distributed training aggregation◈ Members↗
FA-99319

Collective slot IDs are replaced by row positions · case 04

Collective slot IDs are replaced by row positions.

Distributed training aggregation◈ Members↗
FA-99320

Collective slot IDs are replaced by row positions · case 05

Collective slot IDs are replaced by row positions.

Distributed training aggregation◈ Members↗
FA-99321

Worker variances omit between-worker spread · case 01

Worker variances omit between-worker spread.

Distributed training aggregation● Open access↗
FA-99322

Worker variances omit between-worker spread · case 02

Worker variances omit between-worker spread.

Distributed training aggregation◈ Members↗
FA-99323

Worker variances omit between-worker spread · case 03

Worker variances omit between-worker spread.

Distributed training aggregation◈ Members↗
FA-99324

Worker variances omit between-worker spread · case 04

Worker variances omit between-worker spread.

Distributed training aggregation◈ Members↗
FA-99325

Worker variances omit between-worker spread · case 05

Worker variances omit between-worker spread.

Distributed training aggregation◈ Members↗
FA-99326

Gradient bucket reconstruction discards global offsets · case 01

Gradient bucket reconstruction discards global offsets.

Distributed training aggregation● Open access↗
FA-99327

Gradient bucket reconstruction discards global offsets · case 02

Gradient bucket reconstruction discards global offsets.

Distributed training aggregation◈ Members↗
FA-99328

Gradient bucket reconstruction discards global offsets · case 03

Gradient bucket reconstruction discards global offsets.

Distributed training aggregation◈ Members↗
FA-99329

Gradient bucket reconstruction discards global offsets · case 04

Gradient bucket reconstruction discards global offsets.

Distributed training aggregation◈ Members↗
FA-99330

Gradient bucket reconstruction discards global offsets · case 05

Gradient bucket reconstruction discards global offsets.

Distributed training aggregation◈ Members↗
FA-99331

Equal-offset shard fragments retain arrival order · case 01

Equal-offset shard fragments retain arrival order.

Distributed training aggregation● Open access↗
FA-99332

Equal-offset shard fragments retain arrival order · case 02

Equal-offset shard fragments retain arrival order.

Distributed training aggregation◈ Members↗
FA-99333

Equal-offset shard fragments retain arrival order · case 03

Equal-offset shard fragments retain arrival order.

Distributed training aggregation◈ Members↗
FA-99334

Equal-offset shard fragments retain arrival order · case 04

Equal-offset shard fragments retain arrival order.

Distributed training aggregation◈ Members↗
FA-99335

Equal-offset shard fragments retain arrival order · case 05

Equal-offset shard fragments retain arrival order.

Distributed training aggregation◈ Members↗
FA-99336

Unknown worker IDs inflate a replica average · case 01

Unknown worker IDs inflate a replica average.

Distributed training aggregation● Open access↗
FA-99337

Unknown worker IDs inflate a replica average · case 02

Unknown worker IDs inflate a replica average.

Distributed training aggregation◈ Members↗
FA-99338

Unknown worker IDs inflate a replica average · case 03

Unknown worker IDs inflate a replica average.

Distributed training aggregation◈ Members↗
FA-99339

Unknown worker IDs inflate a replica average · case 04

Unknown worker IDs inflate a replica average.

Distributed training aggregation◈ Members↗
FA-99340

Unknown worker IDs inflate a replica average · case 05

Unknown worker IDs inflate a replica average.

Distributed training aggregation◈ Members↗
FA-99341

L2 normalization uses the squared magnitude as divisor · case 01

L2 normalization uses the squared magnitude as divisor.

Embedding vector normalization● Open access↗
FA-99342

L2 normalization uses the squared magnitude as divisor · case 02

L2 normalization uses the squared magnitude as divisor.

Embedding vector normalization◈ Members↗
FA-99343

L2 normalization uses the squared magnitude as divisor · case 03

L2 normalization uses the squared magnitude as divisor.

Embedding vector normalization◈ Members↗
FA-99344

L2 normalization uses the squared magnitude as divisor · case 04

L2 normalization uses the squared magnitude as divisor.

Embedding vector normalization◈ Members↗
FA-99345

L2 normalization uses the squared magnitude as divisor · case 05

L2 normalization uses the squared magnitude as divisor.

Embedding vector normalization◈ Members↗
FA-99346

L1 normalization divides by the largest coordinate · case 01

L1 normalization divides by the largest coordinate.

Embedding vector normalization● Open access↗
FA-99347

L1 normalization divides by the largest coordinate · case 02

L1 normalization divides by the largest coordinate.

Embedding vector normalization◈ Members↗
FA-99348

L1 normalization divides by the largest coordinate · case 03

L1 normalization divides by the largest coordinate.

Embedding vector normalization◈ Members↗
FA-99349

L1 normalization divides by the largest coordinate · case 04

L1 normalization divides by the largest coordinate.

Embedding vector normalization◈ Members↗
FA-99350

L1 normalization divides by the largest coordinate · case 05

L1 normalization divides by the largest coordinate.

Embedding vector normalization◈ Members↗
FA-99351

Infinity normalization divides by total magnitude · case 01

Infinity normalization divides by total magnitude.

Embedding vector normalization● Open access↗
FA-99352

Infinity normalization divides by total magnitude · case 02

Infinity normalization divides by total magnitude.

Embedding vector normalization◈ Members↗
FA-99353

Infinity normalization divides by total magnitude · case 03

Infinity normalization divides by total magnitude.

Embedding vector normalization◈ Members↗
FA-99354

Infinity normalization divides by total magnitude · case 04

Infinity normalization divides by total magnitude.

Embedding vector normalization◈ Members↗
FA-99355

Infinity normalization divides by total magnitude · case 05

Infinity normalization divides by total magnitude.

Embedding vector normalization◈ Members↗
FA-99356

Zero embeddings become a fabricated unit vector · case 01

Zero embeddings become a fabricated unit vector.

Embedding vector normalization● Open access↗
FA-99357

Zero embeddings become a fabricated unit vector · case 02

Zero embeddings become a fabricated unit vector.

Embedding vector normalization◈ Members↗
FA-99358

Zero embeddings become a fabricated unit vector · case 03

Zero embeddings become a fabricated unit vector.

Embedding vector normalization◈ Members↗
FA-99359

Zero embeddings become a fabricated unit vector · case 04

Zero embeddings become a fabricated unit vector.

Embedding vector normalization◈ Members↗
FA-99360

Zero embeddings become a fabricated unit vector · case 05

Zero embeddings become a fabricated unit vector.

Embedding vector normalization◈ Members↗
FA-99361

Vector centering divides by the signed coordinate sum · case 01

Vector centering divides by the signed coordinate sum.

Embedding vector normalization● Open access↗
FA-99362

Vector centering divides by the signed coordinate sum · case 02

Vector centering divides by the signed coordinate sum.

Embedding vector normalization◈ Members↗
FA-99363

Vector centering divides by the signed coordinate sum · case 03

Vector centering divides by the signed coordinate sum.

Embedding vector normalization◈ Members↗
FA-99364

Vector centering divides by the signed coordinate sum · case 04

Vector centering divides by the signed coordinate sum.

Embedding vector normalization◈ Members↗
FA-99365

Vector centering divides by the signed coordinate sum · case 05

Vector centering divides by the signed coordinate sum.

Embedding vector normalization◈ Members↗
FA-99366

Weighted pooling divides by token count · case 01

Weighted pooling divides by token count.

Embedding vector normalization● Open access↗
FA-99367

Weighted pooling divides by token count · case 02

Weighted pooling divides by token count.

Embedding vector normalization◈ Members↗
FA-99368

Weighted pooling divides by token count · case 03

Weighted pooling divides by token count.

Embedding vector normalization◈ Members↗
FA-99369

Weighted pooling divides by token count · case 04

Weighted pooling divides by token count.

Embedding vector normalization◈ Members↗
FA-99370

Weighted pooling divides by token count · case 05

Weighted pooling divides by token count.

Embedding vector normalization◈ Members↗
FA-99371

Masked mean pooling counts masked positions · case 01

Masked mean pooling counts masked positions.

Embedding vector normalization● Open access↗
FA-99372

Masked mean pooling counts masked positions · case 02

Masked mean pooling counts masked positions.

Embedding vector normalization◈ Members↗
FA-99373

Masked mean pooling counts masked positions · case 03

Masked mean pooling counts masked positions.

Embedding vector normalization◈ Members↗
FA-99374

Masked mean pooling counts masked positions · case 04

Masked mean pooling counts masked positions.

Embedding vector normalization◈ Members↗
FA-99375

Masked mean pooling counts masked positions · case 05

Masked mean pooling counts masked positions.

Embedding vector normalization◈ Members↗
FA-99376

Length-normalized pooling divides by squared token count · case 01

Length-normalized pooling divides by squared token count.

Embedding vector normalization● Open access↗
FA-99377

Length-normalized pooling divides by squared token count · case 02

Length-normalized pooling divides by squared token count.

Embedding vector normalization◈ Members↗
FA-99378

Length-normalized pooling divides by squared token count · case 03

Length-normalized pooling divides by squared token count.

Embedding vector normalization◈ Members↗
FA-99379

Length-normalized pooling divides by squared token count · case 04

Length-normalized pooling divides by squared token count.

Embedding vector normalization◈ Members↗
FA-99380

Length-normalized pooling divides by squared token count · case 05

Length-normalized pooling divides by squared token count.

Embedding vector normalization◈ Members↗
FA-99381

Square-root length pooling divides by squared token count · case 01

Square-root length pooling divides by squared token count.

Embedding vector normalization● Open access↗
FA-99382

Square-root length pooling divides by squared token count · case 02

Square-root length pooling divides by squared token count.

Embedding vector normalization◈ Members↗
FA-99383

Square-root length pooling divides by squared token count · case 03

Square-root length pooling divides by squared token count.

Embedding vector normalization◈ Members↗
FA-99384

Square-root length pooling divides by squared token count · case 04

Square-root length pooling divides by squared token count.

Embedding vector normalization◈ Members↗
FA-99385

Square-root length pooling divides by squared token count · case 05

Square-root length pooling divides by squared token count.

Embedding vector normalization◈ Members↗
FA-99386

Weighted pooling normalizes token vectors before combining them · case 01

Weighted pooling normalizes token vectors before combining them.

Embedding vector normalization● Open access↗
FA-99387

Weighted pooling normalizes token vectors before combining them · case 02

Weighted pooling normalizes token vectors before combining them.

Embedding vector normalization◈ Members↗
FA-99388

Weighted pooling normalizes token vectors before combining them · case 03

Weighted pooling normalizes token vectors before combining them.

Embedding vector normalization◈ Members↗
FA-99389

Weighted pooling normalizes token vectors before combining them · case 04

Weighted pooling normalizes token vectors before combining them.

Embedding vector normalization◈ Members↗
FA-99390

Weighted pooling normalizes token vectors before combining them · case 05

Weighted pooling normalizes token vectors before combining them.

Embedding vector normalization◈ Members↗
FA-99391

Gradient style clipping expands a vector already under the norm bound · case 01

Gradient style clipping expands a vector already under the norm bound.

Embedding vector normalization● Open access↗
FA-99392

Gradient style clipping expands a vector already under the norm bound · case 02

Gradient style clipping expands a vector already under the norm bound.

Embedding vector normalization◈ Members↗
FA-99393

Gradient style clipping expands a vector already under the norm bound · case 03

Gradient style clipping expands a vector already under the norm bound.

Embedding vector normalization◈ Members↗
FA-99394

Gradient style clipping expands a vector already under the norm bound · case 04

Gradient style clipping expands a vector already under the norm bound.

Embedding vector normalization◈ Members↗
FA-99395

Gradient style clipping expands a vector already under the norm bound · case 05

Gradient style clipping expands a vector already under the norm bound.

Embedding vector normalization◈ Members↗
FA-99396

Epsilon normalization adds epsilon to every coordinate divisor · case 01

Epsilon normalization adds epsilon to every coordinate divisor.

Embedding vector normalization● Open access↗
FA-99397

Epsilon normalization adds epsilon to every coordinate divisor · case 02

Epsilon normalization adds epsilon to every coordinate divisor.

Embedding vector normalization◈ Members↗
FA-99398

Epsilon normalization adds epsilon to every coordinate divisor · case 03

Epsilon normalization adds epsilon to every coordinate divisor.

Embedding vector normalization◈ Members↗
FA-99399

Epsilon normalization adds epsilon to every coordinate divisor · case 04

Epsilon normalization adds epsilon to every coordinate divisor.

Embedding vector normalization◈ Members↗
FA-99400

Epsilon normalization adds epsilon to every coordinate divisor · case 05

Epsilon normalization adds epsilon to every coordinate divisor.

Embedding vector normalization◈ Members↗

INSPECTABLE BY DESIGN

Every result has a runnable source.

Runnable implementations with recorded outputs, source hashes, and explicit contracts. Related variants share a failure mechanism and belong together in evaluation splits.

Read the methodology ↗