{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:HDMGIAHXVMLIO3RJL24FC7AADB","short_pith_number":"pith:HDMGIAHX","canonical_record":{"source":{"id":"2505.01855","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-03T16:16:55Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"65884cb9a45cf7bdd9b5f5882d5ac0743906a8b38877d4047f0d2b80e7659704","abstract_canon_sha256":"1099ab2b925b5aedd94108687596f845058d2ce9f59db876e1c4ab8c66f3b22f"},"schema_version":"1.0"},"canonical_sha256":"38d86400f7ab16876e295eb8517c001842fbf45853d677de6561e9841ee384b6","source":{"kind":"arxiv","id":"2505.01855","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.01855","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"arxiv_version","alias_value":"2505.01855v2","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.01855","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"pith_short_12","alias_value":"HDMGIAHXVMLI","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"pith_short_16","alias_value":"HDMGIAHXVMLIO3RJ","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"pith_short_8","alias_value":"HDMGIAHX","created_at":"2026-07-05T11:08:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:HDMGIAHXVMLIO3RJL24FC7AADB","target":"record","payload":{"canonical_record":{"source":{"id":"2505.01855","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-03T16:16:55Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"65884cb9a45cf7bdd9b5f5882d5ac0743906a8b38877d4047f0d2b80e7659704","abstract_canon_sha256":"1099ab2b925b5aedd94108687596f845058d2ce9f59db876e1c4ab8c66f3b22f"},"schema_version":"1.0"},"canonical_sha256":"38d86400f7ab16876e295eb8517c001842fbf45853d677de6561e9841ee384b6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:41.030508Z","signature_b64":"K07RSlf6h8baMu+LnfYZcdbNdNchBZFH4gDwwsKnKfvMtFc8Sq8qNQXk4HdcX7UJ6Z+CrQIZo/rDgLj3lIIXAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38d86400f7ab16876e295eb8517c001842fbf45853d677de6561e9841ee384b6","last_reissued_at":"2026-07-05T11:08:41.030033Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:41.030033Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.01855","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:08:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eOKSJi34ghzUiJvwpll+AENOYbfHokzuWd3dQQ78Pq3FJJFUG9mbIis+FiQCGlFFaf9hY9gdYKqYgyjqMqHYCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T02:43:39.428256Z"},"content_sha256":"a88bdc3f78832631ee8b65070b942e7989b5119fcd8d477b16a4a403e86bcb43","schema_version":"1.0","event_id":"sha256:a88bdc3f78832631ee8b65070b942e7989b5119fcd8d477b16a4a403e86bcb43"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:HDMGIAHXVMLIO3RJL24FC7AADB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Intra-Layer Recurrence in Transformers for Language Modeling","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Anthony Nguyen, Wenjun Lin","submitted_at":"2025-05-03T16:16:55Z","abstract_excerpt":"Transformer models have established new benchmarks in natural language processing; however, their increasing depth results in substantial growth in parameter counts. While existing recurrent transformer methods address this issue by reprocessing layers multiple times, they often apply recurrence indiscriminately across entire blocks of layers. In this work, we investigate Intra-Layer Recurrence (ILR), a more targeted approach that applies recurrence selectively to individual layers within a single forward pass. Our experiments show that allocating more iterations to earlier layers yields optim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.01855","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.01855/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:08:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nQGgOMn0bK7faYwfI3B5kF+0ah3s4qrzv408wAyIqoYnQPoim8Q8pE2XPcDsXGgbuyR5kNNUAZwbXHOJajqkAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T02:43:39.428878Z"},"content_sha256":"fe69b196cc6558a1a5cd8174d16f3a72559a597f028a26555b27514779852aeb","schema_version":"1.0","event_id":"sha256:fe69b196cc6558a1a5cd8174d16f3a72559a597f028a26555b27514779852aeb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HDMGIAHXVMLIO3RJL24FC7AADB/bundle.json","state_url":"https://pith.science/pith/HDMGIAHXVMLIO3RJL24FC7AADB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HDMGIAHXVMLIO3RJL24FC7AADB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-21T02:43:39Z","links":{"resolver":"https://pith.science/pith/HDMGIAHXVMLIO3RJL24FC7AADB","bundle":"https://pith.science/pith/HDMGIAHXVMLIO3RJL24FC7AADB/bundle.json","state":"https://pith.science/pith/HDMGIAHXVMLIO3RJL24FC7AADB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HDMGIAHXVMLIO3RJL24FC7AADB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:HDMGIAHXVMLIO3RJL24FC7AADB","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":"1099ab2b925b5aedd94108687596f845058d2ce9f59db876e1c4ab8c66f3b22f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-03T16:16:55Z","title_canon_sha256":"65884cb9a45cf7bdd9b5f5882d5ac0743906a8b38877d4047f0d2b80e7659704"},"schema_version":"1.0","source":{"id":"2505.01855","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.01855","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"arxiv_version","alias_value":"2505.01855v2","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.01855","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"pith_short_12","alias_value":"HDMGIAHXVMLI","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"pith_short_16","alias_value":"HDMGIAHXVMLIO3RJ","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"pith_short_8","alias_value":"HDMGIAHX","created_at":"2026-07-05T11:08:41Z"}],"graph_snapshots":[{"event_id":"sha256:fe69b196cc6558a1a5cd8174d16f3a72559a597f028a26555b27514779852aeb","target":"graph","created_at":"2026-07-05T11:08:41Z","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/2505.01855/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transformer models have established new benchmarks in natural language processing; however, their increasing depth results in substantial growth in parameter counts. While existing recurrent transformer methods address this issue by reprocessing layers multiple times, they often apply recurrence indiscriminately across entire blocks of layers. In this work, we investigate Intra-Layer Recurrence (ILR), a more targeted approach that applies recurrence selectively to individual layers within a single forward pass. Our experiments show that allocating more iterations to earlier layers yields optim","authors_text":"Anthony Nguyen, Wenjun Lin","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-03T16:16:55Z","title":"Intra-Layer Recurrence in Transformers for Language Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.01855","kind":"arxiv","version":2},"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:a88bdc3f78832631ee8b65070b942e7989b5119fcd8d477b16a4a403e86bcb43","target":"record","created_at":"2026-07-05T11:08:41Z","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":"1099ab2b925b5aedd94108687596f845058d2ce9f59db876e1c4ab8c66f3b22f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-03T16:16:55Z","title_canon_sha256":"65884cb9a45cf7bdd9b5f5882d5ac0743906a8b38877d4047f0d2b80e7659704"},"schema_version":"1.0","source":{"id":"2505.01855","kind":"arxiv","version":2}},"canonical_sha256":"38d86400f7ab16876e295eb8517c001842fbf45853d677de6561e9841ee384b6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38d86400f7ab16876e295eb8517c001842fbf45853d677de6561e9841ee384b6","first_computed_at":"2026-07-05T11:08:41.030033Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:08:41.030033Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"K07RSlf6h8baMu+LnfYZcdbNdNchBZFH4gDwwsKnKfvMtFc8Sq8qNQXk4HdcX7UJ6Z+CrQIZo/rDgLj3lIIXAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:08:41.030508Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.01855","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a88bdc3f78832631ee8b65070b942e7989b5119fcd8d477b16a4a403e86bcb43","sha256:fe69b196cc6558a1a5cd8174d16f3a72559a597f028a26555b27514779852aeb"],"state_sha256":"fd5b7709f0affe69241d45d69935672e04b1ecf51144b3c9ffd3b29487973e92"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G8wZZxAvcCVV0EJ/TIJpNNujUtn923WDg0N0a6TDrCjQ9uplWOPokdhO9fZzErABsR56EaZYC+SNdR6trzg4CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T02:43:39.434245Z","bundle_sha256":"613093ef8fd3498e782204f921b41b46ff20c3e1018ac00f34acf9a72628522b"}}