Measured Heuristics With Conservative Fallbacks

Summary#

Static allowlists and hand-tuned heuristics age poorly when behavior depends on real shapes, sizes, or runtime context. A better default is to compute eligibility from available metadata at decision time, while keeping a conservative fallback when inference is missing or untrustworthy.

Problem#

Hardcoded rules such as fixed "cheap" operation lists or assumed output layouts misclassify work once tensor shapes, target platforms, or build settings change.

Solution#

Use actual graph, shape, cost, or environment metadata to drive the decision, define an explicit threshold or validation rule, and fall back to the older safe behavior when the required metadata cannot be derived reliably.

Failure Modes#

Replacing static rules without a fallback creates new failures when metadata inference is incomplete.
Using dynamic estimates without a clear threshold makes behavior unstable and hard to debug.
Trusting unvalidated environment-derived paths can introduce wrong-target restores or repository escapes.

Sources#

https://github.com/tensorflow/tensorflow/pull/109358
https://github.com/tensorflow/tensorflow/pull/123015
https://github.com/tensorflow/tensorflow/pull/119869
https://github.com/huggingface/transformers/pull/46842
https://github.com/huggingface/transformers/pull/47381
https://github.com/huggingface/transformers/pull/47382
https://github.com/huggingface/transformers/pull/46854
https://github.com/huggingface/transformers/pull/47252
https://github.com/huggingface/transformers/pull/47369
https://github.com/huggingface/transformers/pull/47218
https://github.com/huggingface/transformers/pull/47032
https://github.com/huggingface/transformers/pull/47150
https://github.com/huggingface/transformers/pull/46603
https://github.com/huggingface/transformers/pull/47141
https://github.com/microsoft/ML-For-Beginners/pull/1002
https://github.com/microsoft/ML-For-Beginners/pull/1001
https://github.com/microsoft/ML-For-Beginners/pull/1000
https://github.com/microsoft/ML-For-Beginners/pull/994
https://github.com/microsoft/ML-For-Beginners/pull/991
https://github.com/microsoft/ML-For-Beginners/pull/990
https://github.com/microsoft/ML-For-Beginners/pull/989
https://github.com/ClickHouse/ClickHouse/pull/110779
https://github.com/ClickHouse/ClickHouse/pull/110397
https://github.com/ClickHouse/ClickHouse/pull/109529
https://github.com/ClickHouse/ClickHouse/pull/110849
https://github.com/ClickHouse/ClickHouse/pull/110519
https://github.com/ClickHouse/ClickHouse/pull/110688
https://github.com/ClickHouse/ClickHouse/pull/110864
https://github.com/ClickHouse/ClickHouse/pull/109901
https://github.com/ClickHouse/ClickHouse/pull/110491
https://github.com/ClickHouse/ClickHouse/pull/106724
https://github.com/ClickHouse/ClickHouse/pull/110598
https://github.com/ClickHouse/ClickHouse/pull/110528
https://github.com/vercel/turborepo/pull/13392
https://github.com/vercel/turborepo/pull/13391
https://github.com/vercel/turborepo/pull/13358
https://github.com/vercel/turborepo/pull/13382
https://github.com/vercel/turborepo/pull/13383
https://github.com/vercel/turborepo/pull/13380
https://github.com/vercel/turborepo/pull/13381
https://github.com/vercel/turborepo/pull/13378
https://github.com/vercel/turborepo/pull/13373
https://github.com/vercel/turborepo/pull/13374
https://github.com/vercel/turborepo/pull/13370
https://github.com/vercel/turborepo/pull/13369
https://github.com/vercel/turborepo/pull/13366
mined_at: 2026-07-17T23:22:26Z

Sagwan Revalidation 2026-07-18T00:00:13Z#

verdict: ok
note: 원칙형 캡슐로 수치 의존이 없고 보수적 fallback 권장은 여전히 유효함

Sagwan Revalidation 2026-07-20T00:49:56Z#

verdict: ok
note: 원칙 중심 내용으로 최근 practice와 충돌 없고 재사용 가능함