{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:O7QDJUUER6AXZAD46U3FPLEEE7","short_pith_number":"pith:O7QDJUUE","canonical_record":{"source":{"id":"1608.05604","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-19T14:03:46Z","cross_cats_sorted":[],"title_canon_sha256":"c5f2ffef328fdbc19d054e4e4b78a1fd635f596cc092a98f6fbbe27d69405dfd","abstract_canon_sha256":"fff4bb8750335db46901f10a5fc7d91db80aea759e63e4f7e1365301d4fc77b2"},"schema_version":"1.0"},"canonical_sha256":"77e034d2848f817c807cf53657ac8427dff37d118f2f58e56c61efa6e33aea81","source":{"kind":"arxiv","id":"1608.05604","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.05604","created_at":"2026-05-18T00:45:54Z"},{"alias_kind":"arxiv_version","alias_value":"1608.05604v2","created_at":"2026-05-18T00:45:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.05604","created_at":"2026-05-18T00:45:54Z"},{"alias_kind":"pith_short_12","alias_value":"O7QDJUUER6AX","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"O7QDJUUER6AXZAD4","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"O7QDJUUE","created_at":"2026-05-18T12:30:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:O7QDJUUER6AXZAD46U3FPLEEE7","target":"record","payload":{"canonical_record":{"source":{"id":"1608.05604","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-19T14:03:46Z","cross_cats_sorted":[],"title_canon_sha256":"c5f2ffef328fdbc19d054e4e4b78a1fd635f596cc092a98f6fbbe27d69405dfd","abstract_canon_sha256":"fff4bb8750335db46901f10a5fc7d91db80aea759e63e4f7e1365301d4fc77b2"},"schema_version":"1.0"},"canonical_sha256":"77e034d2848f817c807cf53657ac8427dff37d118f2f58e56c61efa6e33aea81","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:54.305792Z","signature_b64":"kcxlXSmEGNcV6t66QiUP7SYBNyBj1GuaNibHYCiJ2zQaKan7au03UucLEUpqpagZ5ju6kn5yuDv5X6atODlJCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77e034d2848f817c807cf53657ac8427dff37d118f2f58e56c61efa6e33aea81","last_reissued_at":"2026-05-18T00:45:54.305187Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:54.305187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.05604","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":"2026-05-18T00:45:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iwtzbYmZI9jUQChGu/5ORd4BA4ONZCX4v8q8gm8JQ/Hj+fRl06wH3fo8KiGjb6m7B/hyUcgmiUi7H6myDXjQDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T19:25:03.142482Z"},"content_sha256":"db96532d7e365fb762bc552abb2d3e6ff6a32b84a511072a5323db946dd39624","schema_version":"1.0","event_id":"sha256:db96532d7e365fb762bc552abb2d3e6ff6a32b84a511072a5323db946dd39624"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:O7QDJUUER6AXZAD46U3FPLEEE7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modeling Human Reading with Neural Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Frank Keller, Michael Hahn","submitted_at":"2016-08-19T14:03:46Z","abstract_excerpt":"When humans read text, they fixate some words and skip others. However, there have been few attempts to explain skipping behavior with computational models, as most existing work has focused on predicting reading times (e.g.,~using surprisal). In this paper, we propose a novel approach that models both skipping and reading, using an unsupervised architecture that combines a neural attention with autoencoding, trained on raw text using reinforcement learning. Our model explains human reading behavior as a tradeoff between precision of language understanding (encoding the input accurately) and e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.05604","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":""},"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":"2026-05-18T00:45:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CUWwiKVAVerJ+YWv+hgGyGaaJdL/C1Ube/IKXOM3MSvl0OueQ0XkNLs85bj6vZTaL9yiM+2wb13I5SH1YbYdAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T19:25:03.143209Z"},"content_sha256":"ee9a7f5c1db5f62ea4a98d69a7e86d207218349995a1ad6a65900818b9dcc37a","schema_version":"1.0","event_id":"sha256:ee9a7f5c1db5f62ea4a98d69a7e86d207218349995a1ad6a65900818b9dcc37a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O7QDJUUER6AXZAD46U3FPLEEE7/bundle.json","state_url":"https://pith.science/pith/O7QDJUUER6AXZAD46U3FPLEEE7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O7QDJUUER6AXZAD46U3FPLEEE7/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":"2026-06-08T19:25:03Z","links":{"resolver":"https://pith.science/pith/O7QDJUUER6AXZAD46U3FPLEEE7","bundle":"https://pith.science/pith/O7QDJUUER6AXZAD46U3FPLEEE7/bundle.json","state":"https://pith.science/pith/O7QDJUUER6AXZAD46U3FPLEEE7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O7QDJUUER6AXZAD46U3FPLEEE7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:O7QDJUUER6AXZAD46U3FPLEEE7","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":"fff4bb8750335db46901f10a5fc7d91db80aea759e63e4f7e1365301d4fc77b2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-19T14:03:46Z","title_canon_sha256":"c5f2ffef328fdbc19d054e4e4b78a1fd635f596cc092a98f6fbbe27d69405dfd"},"schema_version":"1.0","source":{"id":"1608.05604","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.05604","created_at":"2026-05-18T00:45:54Z"},{"alias_kind":"arxiv_version","alias_value":"1608.05604v2","created_at":"2026-05-18T00:45:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.05604","created_at":"2026-05-18T00:45:54Z"},{"alias_kind":"pith_short_12","alias_value":"O7QDJUUER6AX","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"O7QDJUUER6AXZAD4","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"O7QDJUUE","created_at":"2026-05-18T12:30:36Z"}],"graph_snapshots":[{"event_id":"sha256:ee9a7f5c1db5f62ea4a98d69a7e86d207218349995a1ad6a65900818b9dcc37a","target":"graph","created_at":"2026-05-18T00:45:54Z","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"},"paper":{"abstract_excerpt":"When humans read text, they fixate some words and skip others. However, there have been few attempts to explain skipping behavior with computational models, as most existing work has focused on predicting reading times (e.g.,~using surprisal). In this paper, we propose a novel approach that models both skipping and reading, using an unsupervised architecture that combines a neural attention with autoencoding, trained on raw text using reinforcement learning. Our model explains human reading behavior as a tradeoff between precision of language understanding (encoding the input accurately) and e","authors_text":"Frank Keller, Michael Hahn","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-19T14:03:46Z","title":"Modeling Human Reading with Neural Attention"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.05604","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:db96532d7e365fb762bc552abb2d3e6ff6a32b84a511072a5323db946dd39624","target":"record","created_at":"2026-05-18T00:45:54Z","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":"fff4bb8750335db46901f10a5fc7d91db80aea759e63e4f7e1365301d4fc77b2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-08-19T14:03:46Z","title_canon_sha256":"c5f2ffef328fdbc19d054e4e4b78a1fd635f596cc092a98f6fbbe27d69405dfd"},"schema_version":"1.0","source":{"id":"1608.05604","kind":"arxiv","version":2}},"canonical_sha256":"77e034d2848f817c807cf53657ac8427dff37d118f2f58e56c61efa6e33aea81","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"77e034d2848f817c807cf53657ac8427dff37d118f2f58e56c61efa6e33aea81","first_computed_at":"2026-05-18T00:45:54.305187Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:45:54.305187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kcxlXSmEGNcV6t66QiUP7SYBNyBj1GuaNibHYCiJ2zQaKan7au03UucLEUpqpagZ5ju6kn5yuDv5X6atODlJCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:45:54.305792Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.05604","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db96532d7e365fb762bc552abb2d3e6ff6a32b84a511072a5323db946dd39624","sha256:ee9a7f5c1db5f62ea4a98d69a7e86d207218349995a1ad6a65900818b9dcc37a"],"state_sha256":"f2287b97e754cf534642cbb853776c92e276cbeec16c9ed2ad3291153f3e9a46"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UTyKwl6Z29GNYrDU42fWFRTtnJzR0pmeaYcrFfwyMdidv9IYdc9KX2Me5FZ1Dus6/IfSpaxIIPpcqA8pi2aXCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T19:25:03.146976Z","bundle_sha256":"4833bd45ef79ecbbc2335fec1cb96d24033fa3fb9da65b439a45acc604bfabac"}}