{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:JBOMINV7QLJKP7IQGFRT33JU6W","short_pith_number":"pith:JBOMINV7","canonical_record":{"source":{"id":"2407.05483","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-07T19:55:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9255a364f44c19fc18058289255b873e2c6de7fa1d409ac34ccb87e423d1f4b0","abstract_canon_sha256":"0ece68899cccaacbebb7882515dccf9419e9c06d5d7db213789ad340defb0939"},"schema_version":"1.0"},"canonical_sha256":"485cc436bf82d2a7fd1031633ded34f59ae0b41f6283296145352d8ff624fa2f","source":{"kind":"arxiv","id":"2407.05483","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.05483","created_at":"2026-07-05T08:41:14Z"},{"alias_kind":"arxiv_version","alias_value":"2407.05483v1","created_at":"2026-07-05T08:41:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.05483","created_at":"2026-07-05T08:41:14Z"},{"alias_kind":"pith_short_12","alias_value":"JBOMINV7QLJK","created_at":"2026-07-05T08:41:14Z"},{"alias_kind":"pith_short_16","alias_value":"JBOMINV7QLJKP7IQ","created_at":"2026-07-05T08:41:14Z"},{"alias_kind":"pith_short_8","alias_value":"JBOMINV7","created_at":"2026-07-05T08:41:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:JBOMINV7QLJKP7IQGFRT33JU6W","target":"record","payload":{"canonical_record":{"source":{"id":"2407.05483","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-07T19:55:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9255a364f44c19fc18058289255b873e2c6de7fa1d409ac34ccb87e423d1f4b0","abstract_canon_sha256":"0ece68899cccaacbebb7882515dccf9419e9c06d5d7db213789ad340defb0939"},"schema_version":"1.0"},"canonical_sha256":"485cc436bf82d2a7fd1031633ded34f59ae0b41f6283296145352d8ff624fa2f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:41:14.103345Z","signature_b64":"kICXu3DIOzPegai+JCMMY6hASj8YlCQmLrUmWd92obohzHYk/jGqBqw3P0l+xixvVTkHk6h0HPUN0vv4Ie79AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"485cc436bf82d2a7fd1031633ded34f59ae0b41f6283296145352d8ff624fa2f","last_reissued_at":"2026-07-05T08:41:14.102892Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:41:14.102892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.05483","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:41:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"58K2xzzYgxcWejLhXikKANnUlnHClM0C8m9eSxQdHhcsjMDMqmv13/JrLq0XVf3rPOnqv2xY8SNtzkeIyMofCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T20:59:42.077948Z"},"content_sha256":"a856c75e896b082f3b34f8042849ddcafd82c832648160162af56eb15fa6a2cd","schema_version":"1.0","event_id":"sha256:a856c75e896b082f3b34f8042849ddcafd82c832648160162af56eb15fa6a2cd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:JBOMINV7QLJKP7IQGFRT33JU6W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Just read twice: closing the recall gap for recurrent language models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Aaryan Singhal, Aman Timalsina, Ashish Rao, Atri Rudra, Benjamin Spector, Christopher R\\'e, Sabri Eyuboglu, Simran Arora, Xinyi Zhao","submitted_at":"2024-07-07T19:55:09Z","abstract_excerpt":"Recurrent large language models that compete with Transformers in language modeling perplexity are emerging at a rapid rate (e.g., Mamba, RWKV). Excitingly, these architectures use a constant amount of memory during inference. However, due to the limited memory, recurrent LMs cannot recall and use all the information in long contexts leading to brittle in-context learning (ICL) quality. A key challenge for efficient LMs is selecting what information to store versus discard. In this work, we observe the order in which information is shown to the LM impacts the selection difficulty. To formalize"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.05483","kind":"arxiv","version":1},"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/2407.05483/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":"2026-07-05T08:41:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wuvpdIKuUrqiKBgi6eDg5u4DdrASwtYcJt5j9ixpORZz9g2SEQWKv0WRBeIqvi7eDE7aGk//FO6Mvlo+TRyGAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T20:59:42.078473Z"},"content_sha256":"e2204bc8952c1020960d192774b25bdc84c6c59cdfa73e64c952c68023ed6d4a","schema_version":"1.0","event_id":"sha256:e2204bc8952c1020960d192774b25bdc84c6c59cdfa73e64c952c68023ed6d4a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JBOMINV7QLJKP7IQGFRT33JU6W/bundle.json","state_url":"https://pith.science/pith/JBOMINV7QLJKP7IQGFRT33JU6W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JBOMINV7QLJKP7IQGFRT33JU6W/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":"2026-08-06T20:59:42Z","links":{"resolver":"https://pith.science/pith/JBOMINV7QLJKP7IQGFRT33JU6W","bundle":"https://pith.science/pith/JBOMINV7QLJKP7IQGFRT33JU6W/bundle.json","state":"https://pith.science/pith/JBOMINV7QLJKP7IQGFRT33JU6W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JBOMINV7QLJKP7IQGFRT33JU6W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JBOMINV7QLJKP7IQGFRT33JU6W","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":"0ece68899cccaacbebb7882515dccf9419e9c06d5d7db213789ad340defb0939","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-07T19:55:09Z","title_canon_sha256":"9255a364f44c19fc18058289255b873e2c6de7fa1d409ac34ccb87e423d1f4b0"},"schema_version":"1.0","source":{"id":"2407.05483","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.05483","created_at":"2026-07-05T08:41:14Z"},{"alias_kind":"arxiv_version","alias_value":"2407.05483v1","created_at":"2026-07-05T08:41:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.05483","created_at":"2026-07-05T08:41:14Z"},{"alias_kind":"pith_short_12","alias_value":"JBOMINV7QLJK","created_at":"2026-07-05T08:41:14Z"},{"alias_kind":"pith_short_16","alias_value":"JBOMINV7QLJKP7IQ","created_at":"2026-07-05T08:41:14Z"},{"alias_kind":"pith_short_8","alias_value":"JBOMINV7","created_at":"2026-07-05T08:41:14Z"}],"graph_snapshots":[{"event_id":"sha256:e2204bc8952c1020960d192774b25bdc84c6c59cdfa73e64c952c68023ed6d4a","target":"graph","created_at":"2026-07-05T08:41:14Z","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/2407.05483/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recurrent large language models that compete with Transformers in language modeling perplexity are emerging at a rapid rate (e.g., Mamba, RWKV). Excitingly, these architectures use a constant amount of memory during inference. However, due to the limited memory, recurrent LMs cannot recall and use all the information in long contexts leading to brittle in-context learning (ICL) quality. A key challenge for efficient LMs is selecting what information to store versus discard. In this work, we observe the order in which information is shown to the LM impacts the selection difficulty. To formalize","authors_text":"Aaryan Singhal, Aman Timalsina, Ashish Rao, Atri Rudra, Benjamin Spector, Christopher R\\'e, Sabri Eyuboglu, Simran Arora, Xinyi Zhao","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-07T19:55:09Z","title":"Just read twice: closing the recall gap for recurrent language models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.05483","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:a856c75e896b082f3b34f8042849ddcafd82c832648160162af56eb15fa6a2cd","target":"record","created_at":"2026-07-05T08:41:14Z","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":"0ece68899cccaacbebb7882515dccf9419e9c06d5d7db213789ad340defb0939","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-07T19:55:09Z","title_canon_sha256":"9255a364f44c19fc18058289255b873e2c6de7fa1d409ac34ccb87e423d1f4b0"},"schema_version":"1.0","source":{"id":"2407.05483","kind":"arxiv","version":1}},"canonical_sha256":"485cc436bf82d2a7fd1031633ded34f59ae0b41f6283296145352d8ff624fa2f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"485cc436bf82d2a7fd1031633ded34f59ae0b41f6283296145352d8ff624fa2f","first_computed_at":"2026-07-05T08:41:14.102892Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:41:14.102892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kICXu3DIOzPegai+JCMMY6hASj8YlCQmLrUmWd92obohzHYk/jGqBqw3P0l+xixvVTkHk6h0HPUN0vv4Ie79AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:41:14.103345Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.05483","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a856c75e896b082f3b34f8042849ddcafd82c832648160162af56eb15fa6a2cd","sha256:e2204bc8952c1020960d192774b25bdc84c6c59cdfa73e64c952c68023ed6d4a"],"state_sha256":"f37ab40d96e00c30432176ca08cebc2ad87b2454d6ffb11da9e116d86dd20027"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VQ9FQYV0RINphTnMAtkDQst/TeGlUiP5k8v40ZSa9Kaic6Id4PLrrHLqtK0v18br+7rf9UG3s15VPeao7x2YCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T20:59:42.082885Z","bundle_sha256":"79c5a01e0295d776adad14a65acd7d7ab897e289f88debeaff36ac5140bbf553"}}