{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:GWW2LNLUTCJMDDM3X6P53IC3UB","short_pith_number":"pith:GWW2LNLU","canonical_record":{"source":{"id":"2507.07955","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-10T17:39:37Z","cross_cats_sorted":[],"title_canon_sha256":"b16515100df1eae92459da2e907bae3722ce79a0c30c4ad8443d8d3bf242a63b","abstract_canon_sha256":"f4958cbad3f30475cfb2f0f552bc0dab7ac00c2e8d78cb1adbbb6980e46cd2a9"},"schema_version":"1.0"},"canonical_sha256":"35ada5b5749892c18d9bbf9fdda05ba04933aea667412f985887b223172d4ecb","source":{"kind":"arxiv","id":"2507.07955","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.07955","created_at":"2026-07-05T11:37:25Z"},{"alias_kind":"arxiv_version","alias_value":"2507.07955v2","created_at":"2026-07-05T11:37:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.07955","created_at":"2026-07-05T11:37:25Z"},{"alias_kind":"pith_short_12","alias_value":"GWW2LNLUTCJM","created_at":"2026-07-05T11:37:25Z"},{"alias_kind":"pith_short_16","alias_value":"GWW2LNLUTCJMDDM3","created_at":"2026-07-05T11:37:25Z"},{"alias_kind":"pith_short_8","alias_value":"GWW2LNLU","created_at":"2026-07-05T11:37:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:GWW2LNLUTCJMDDM3X6P53IC3UB","target":"record","payload":{"canonical_record":{"source":{"id":"2507.07955","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-10T17:39:37Z","cross_cats_sorted":[],"title_canon_sha256":"b16515100df1eae92459da2e907bae3722ce79a0c30c4ad8443d8d3bf242a63b","abstract_canon_sha256":"f4958cbad3f30475cfb2f0f552bc0dab7ac00c2e8d78cb1adbbb6980e46cd2a9"},"schema_version":"1.0"},"canonical_sha256":"35ada5b5749892c18d9bbf9fdda05ba04933aea667412f985887b223172d4ecb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:37:25.796599Z","signature_b64":"UPplbw6vGgsaY8u1c+b5nXihx4bFx6dVxflv+7tCAS1qrQS3zG3YC2v8rAAp7Rr29PuzpBFx42j6M2YS6Nw0AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35ada5b5749892c18d9bbf9fdda05ba04933aea667412f985887b223172d4ecb","last_reissued_at":"2026-07-05T11:37:25.796028Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:37:25.796028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.07955","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":"2026-07-05T11:37:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V5l9AhuCD8zHXAMmvDxvqCWwqRPDC7ykdgBPRdJQkz1zenjJTo/swMPkgHl/C6NsKlbpVaQdUNi6X9HlJMqkAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-23T17:08:06.455446Z"},"content_sha256":"5d32a6b750d9737898800184bb5ab217fff816608009aa48a41b4adcea130b54","schema_version":"1.0","event_id":"sha256:5d32a6b750d9737898800184bb5ab217fff816608009aa48a41b4adcea130b54"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:GWW2LNLUTCJMDDM3X6P53IC3UB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dynamic Chunking for End-to-End Hierarchical Sequence Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Albert Gu, Brandon Wang, Sukjun Hwang","submitted_at":"2025-07-10T17:39:37Z","abstract_excerpt":"Major progress on language models (LMs) in recent years has largely resulted from moving away from specialized models designed for specific tasks, to general models based on powerful architectures (e.g. the Transformer) that learn everything from raw data. Despite this trend, pre-processing steps such as tokenization remain a barrier to true end-to-end foundation models. We introduce a collection of new techniques that enable a dynamic chunking mechanism which automatically learns content- and context- dependent segmentation strategies learned jointly with the rest of the model. Incorporating "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.07955","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/2507.07955/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":"2026-07-05T11:37:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q4YV0uYQsJEKjcsHCdBQLDzglTJOtL0gR3bkWj7BRHARJ8ngo4O0FlaxIOHM9mr6Kd+CShOO03l9hk7vdOFiBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-23T17:08:06.455814Z"},"content_sha256":"18bbfa0b15e78540e83bf359f9e3f0e7501e478fec4d2ee27ad29508cb9ab3ca","schema_version":"1.0","event_id":"sha256:18bbfa0b15e78540e83bf359f9e3f0e7501e478fec4d2ee27ad29508cb9ab3ca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GWW2LNLUTCJMDDM3X6P53IC3UB/bundle.json","state_url":"https://pith.science/pith/GWW2LNLUTCJMDDM3X6P53IC3UB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GWW2LNLUTCJMDDM3X6P53IC3UB/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":"2026-07-23T17:08:06Z","links":{"resolver":"https://pith.science/pith/GWW2LNLUTCJMDDM3X6P53IC3UB","bundle":"https://pith.science/pith/GWW2LNLUTCJMDDM3X6P53IC3UB/bundle.json","state":"https://pith.science/pith/GWW2LNLUTCJMDDM3X6P53IC3UB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GWW2LNLUTCJMDDM3X6P53IC3UB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GWW2LNLUTCJMDDM3X6P53IC3UB","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":"f4958cbad3f30475cfb2f0f552bc0dab7ac00c2e8d78cb1adbbb6980e46cd2a9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-10T17:39:37Z","title_canon_sha256":"b16515100df1eae92459da2e907bae3722ce79a0c30c4ad8443d8d3bf242a63b"},"schema_version":"1.0","source":{"id":"2507.07955","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.07955","created_at":"2026-07-05T11:37:25Z"},{"alias_kind":"arxiv_version","alias_value":"2507.07955v2","created_at":"2026-07-05T11:37:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.07955","created_at":"2026-07-05T11:37:25Z"},{"alias_kind":"pith_short_12","alias_value":"GWW2LNLUTCJM","created_at":"2026-07-05T11:37:25Z"},{"alias_kind":"pith_short_16","alias_value":"GWW2LNLUTCJMDDM3","created_at":"2026-07-05T11:37:25Z"},{"alias_kind":"pith_short_8","alias_value":"GWW2LNLU","created_at":"2026-07-05T11:37:25Z"}],"graph_snapshots":[{"event_id":"sha256:18bbfa0b15e78540e83bf359f9e3f0e7501e478fec4d2ee27ad29508cb9ab3ca","target":"graph","created_at":"2026-07-05T11:37:25Z","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/2507.07955/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Major progress on language models (LMs) in recent years has largely resulted from moving away from specialized models designed for specific tasks, to general models based on powerful architectures (e.g. the Transformer) that learn everything from raw data. Despite this trend, pre-processing steps such as tokenization remain a barrier to true end-to-end foundation models. We introduce a collection of new techniques that enable a dynamic chunking mechanism which automatically learns content- and context- dependent segmentation strategies learned jointly with the rest of the model. Incorporating ","authors_text":"Albert Gu, Brandon Wang, Sukjun Hwang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-10T17:39:37Z","title":"Dynamic Chunking for End-to-End Hierarchical Sequence Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.07955","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":"sha256:5d32a6b750d9737898800184bb5ab217fff816608009aa48a41b4adcea130b54","target":"record","created_at":"2026-07-05T11:37:25Z","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":"f4958cbad3f30475cfb2f0f552bc0dab7ac00c2e8d78cb1adbbb6980e46cd2a9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-10T17:39:37Z","title_canon_sha256":"b16515100df1eae92459da2e907bae3722ce79a0c30c4ad8443d8d3bf242a63b"},"schema_version":"1.0","source":{"id":"2507.07955","kind":"arxiv","version":2}},"canonical_sha256":"35ada5b5749892c18d9bbf9fdda05ba04933aea667412f985887b223172d4ecb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"35ada5b5749892c18d9bbf9fdda05ba04933aea667412f985887b223172d4ecb","first_computed_at":"2026-07-05T11:37:25.796028Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:37:25.796028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UPplbw6vGgsaY8u1c+b5nXihx4bFx6dVxflv+7tCAS1qrQS3zG3YC2v8rAAp7Rr29PuzpBFx42j6M2YS6Nw0AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:37:25.796599Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.07955","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5d32a6b750d9737898800184bb5ab217fff816608009aa48a41b4adcea130b54","sha256:18bbfa0b15e78540e83bf359f9e3f0e7501e478fec4d2ee27ad29508cb9ab3ca"],"state_sha256":"a1041b74a4ffa8c6b4ee64c6a3df62303da58cfd4692c5e143ace29579645240"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SoDxZA9wDJswRoSVnJdZCk87SBgiO4Wfz+BegdUjwWbAYV66Yavc+gzYJz6GBShHkEo4brYA4hPqLme8BJpBAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-23T17:08:06.457930Z","bundle_sha256":"563949c1f1c3f0daab6039749951c817c4d542e46fdf85c320d7dcba63b1dc41"}}