{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:72E4ABJ6HUZHQ2CGDNN2NLEYNS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"eee6eedc89b02e3b4475da1e59cce17e16164eae091290ccfc2b0a1ec2a31032","cross_cats_sorted":["cs.AR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-23T08:36:56Z","title_canon_sha256":"54ebdad46882a13d9b21eb9494411cc8dce757a3d1e989dbf2c5cad449bdec50"},"schema_version":"1.0","source":{"id":"2505.17626","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.17626","created_at":"2026-07-05T11:08:29Z"},{"alias_kind":"arxiv_version","alias_value":"2505.17626v1","created_at":"2026-07-05T11:08:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.17626","created_at":"2026-07-05T11:08:29Z"},{"alias_kind":"pith_short_12","alias_value":"72E4ABJ6HUZH","created_at":"2026-07-05T11:08:29Z"},{"alias_kind":"pith_short_16","alias_value":"72E4ABJ6HUZHQ2CG","created_at":"2026-07-05T11:08:29Z"},{"alias_kind":"pith_short_8","alias_value":"72E4ABJ6","created_at":"2026-07-05T11:08:29Z"}],"graph_snapshots":[{"event_id":"sha256:b8197710d8bcb246fd8254858988938b1fef8c241e4ab7c25467229bd2182dec","target":"graph","created_at":"2026-07-05T11:08:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.17626/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dynamic DNN optimization techniques such as layer-skipping offer increased adaptability and efficiency gains but can lead to i) a larger memory footprint as in decision gates, ii) increased training complexity (e.g., with non-differentiable operations), and iii) less control over performance-quality trade-offs due to its inherent input-dependent execution. To approach these issues, we propose a simpler yet effective alternative for adaptive inference with a zero-overhead, single-model, and time-predictable inference. Central to our approach is the observation that models trained with Stochasti","authors_text":"Antonio Carlos Schneider Beck, Guilherme Korol, Jeronimo Castrillon","cross_cats":["cs.AR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-23T08:36:56Z","title":"Leveraging Stochastic Depth Training for Adaptive Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.17626","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c643f8a35fbccf2c3e23234713b534775760b8f7d9fc4e4158d30838d0101cdb","target":"record","created_at":"2026-07-05T11:08:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"eee6eedc89b02e3b4475da1e59cce17e16164eae091290ccfc2b0a1ec2a31032","cross_cats_sorted":["cs.AR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-23T08:36:56Z","title_canon_sha256":"54ebdad46882a13d9b21eb9494411cc8dce757a3d1e989dbf2c5cad449bdec50"},"schema_version":"1.0","source":{"id":"2505.17626","kind":"arxiv","version":1}},"canonical_sha256":"fe89c0053e3d327868461b5ba6ac986c8692a457538a3f90abc3c6dff3e1bf78","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fe89c0053e3d327868461b5ba6ac986c8692a457538a3f90abc3c6dff3e1bf78","first_computed_at":"2026-07-05T11:08:29.528051Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:08:29.528051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sXUF/Ewi9aj/pHjBFbR4rNqr5u+Uss81sVju66ZVZI8I//lXCj/gbMQiu4lAOfpZvwI1e4/UNRTgEn0427RDAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:08:29.528479Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.17626","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c643f8a35fbccf2c3e23234713b534775760b8f7d9fc4e4158d30838d0101cdb","sha256:b8197710d8bcb246fd8254858988938b1fef8c241e4ab7c25467229bd2182dec"],"state_sha256":"b85fab819d408a70e6f8daef1f24af37c3afba9107ff8d37ae981b8812ffe476"}