{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:I5I3UHUE2SI5D2FELGOS6CMOJD","short_pith_number":"pith:I5I3UHUE","canonical_record":{"source":{"id":"2607.11614","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-13T14:37:24Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d087672539be881d82b04693b434782193312c9a0bc0e07055f8c30ee5c4d506","abstract_canon_sha256":"42744a27ba310e638012ac4633d6005364c50ada5abd92de8821f2dd3a79f268"},"schema_version":"1.0"},"canonical_sha256":"4751ba1e84d491d1e8a4599d2f098e48ef1d4cc5332dd4422cdbe45866f06785","source":{"kind":"arxiv","id":"2607.11614","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.11614","created_at":"2026-07-14T02:22:15Z"},{"alias_kind":"arxiv_version","alias_value":"2607.11614v1","created_at":"2026-07-14T02:22:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.11614","created_at":"2026-07-14T02:22:15Z"},{"alias_kind":"pith_short_12","alias_value":"I5I3UHUE2SI5","created_at":"2026-07-14T02:22:15Z"},{"alias_kind":"pith_short_16","alias_value":"I5I3UHUE2SI5D2FE","created_at":"2026-07-14T02:22:15Z"},{"alias_kind":"pith_short_8","alias_value":"I5I3UHUE","created_at":"2026-07-14T02:22:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:I5I3UHUE2SI5D2FELGOS6CMOJD","target":"record","payload":{"canonical_record":{"source":{"id":"2607.11614","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-13T14:37:24Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d087672539be881d82b04693b434782193312c9a0bc0e07055f8c30ee5c4d506","abstract_canon_sha256":"42744a27ba310e638012ac4633d6005364c50ada5abd92de8821f2dd3a79f268"},"schema_version":"1.0"},"canonical_sha256":"4751ba1e84d491d1e8a4599d2f098e48ef1d4cc5332dd4422cdbe45866f06785","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T02:22:15.038038Z","signature_b64":"T41LT6BZWukG4rEip46Ccq0Z0b78UQmVd869hXJvqJKfggMihzZGuFAuZQupN8sh9L3BmEwf3vGXXNROe0I/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4751ba1e84d491d1e8a4599d2f098e48ef1d4cc5332dd4422cdbe45866f06785","last_reissued_at":"2026-07-14T02:22:15.037165Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T02:22:15.037165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.11614","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":"2026-07-14T02:22:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QwJN3SbqGY7VNOAUTQaFani6xF6MC2Qj04gvRFWjNF74GsSQPY+t501JqXomEnZyFSkYHT/98lkkCZZ1Ybc/Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:03:20.245014Z"},"content_sha256":"504ad5cac2f21ce33069314010a75643d7bbcbf65b34bf6a5929afc92ae39d0b","schema_version":"1.0","event_id":"sha256:504ad5cac2f21ce33069314010a75643d7bbcbf65b34bf6a5929afc92ae39d0b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:I5I3UHUE2SI5D2FELGOS6CMOJD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Extending LLM Context via Associative Recurrent Memory","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Artem Shelmanov, Aydar Bulatov, Gleb Kuzmin, Ilia Sochenkov, Ivan Rodkin, Lyudmila Rvanova, Mikhail Burtsev, Mikhail Katkov, Misha Tsodyks, Timothy Baldwin, Yuri Kuratov","submitted_at":"2026-07-13T14:37:24Z","abstract_excerpt":"Extending the context length of large language models (LLMs) is critical for many real-world applications, yet standard transformers remain constrained by quadratic compute and linear memory scaling. In this work, we investigate the Associative Recurrent Memory Transformer (ARMT) as a practical approach for enabling long-context processing in LLMs, constant memory scaling, and better efficiency. We make three main contributions. First, we construct two domain-specific long-context datasets designed to evaluate realistic workloads, focusing on narrow-domain fine-tuning scenarios. Second, we pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11614","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/2607.11614/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":"2026-07-14T02:22:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FfbojEM6W3/yd1GCRcgLY+fCM9lQPtzqGh3/CxeUuXMW8x0nG8LxBF9ZdpaQHIs6gqOyvrI+f+8ee+a7fjLaCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:03:20.245908Z"},"content_sha256":"3890a2a3786c2d4fac47a444582b3796c9f3cbe8351cc82bb3901e298553853f","schema_version":"1.0","event_id":"sha256:3890a2a3786c2d4fac47a444582b3796c9f3cbe8351cc82bb3901e298553853f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I5I3UHUE2SI5D2FELGOS6CMOJD/bundle.json","state_url":"https://pith.science/pith/I5I3UHUE2SI5D2FELGOS6CMOJD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I5I3UHUE2SI5D2FELGOS6CMOJD/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":"2026-08-05T03:03:20Z","links":{"resolver":"https://pith.science/pith/I5I3UHUE2SI5D2FELGOS6CMOJD","bundle":"https://pith.science/pith/I5I3UHUE2SI5D2FELGOS6CMOJD/bundle.json","state":"https://pith.science/pith/I5I3UHUE2SI5D2FELGOS6CMOJD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I5I3UHUE2SI5D2FELGOS6CMOJD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:I5I3UHUE2SI5D2FELGOS6CMOJD","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":"42744a27ba310e638012ac4633d6005364c50ada5abd92de8821f2dd3a79f268","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-13T14:37:24Z","title_canon_sha256":"d087672539be881d82b04693b434782193312c9a0bc0e07055f8c30ee5c4d506"},"schema_version":"1.0","source":{"id":"2607.11614","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.11614","created_at":"2026-07-14T02:22:15Z"},{"alias_kind":"arxiv_version","alias_value":"2607.11614v1","created_at":"2026-07-14T02:22:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.11614","created_at":"2026-07-14T02:22:15Z"},{"alias_kind":"pith_short_12","alias_value":"I5I3UHUE2SI5","created_at":"2026-07-14T02:22:15Z"},{"alias_kind":"pith_short_16","alias_value":"I5I3UHUE2SI5D2FE","created_at":"2026-07-14T02:22:15Z"},{"alias_kind":"pith_short_8","alias_value":"I5I3UHUE","created_at":"2026-07-14T02:22:15Z"}],"graph_snapshots":[{"event_id":"sha256:3890a2a3786c2d4fac47a444582b3796c9f3cbe8351cc82bb3901e298553853f","target":"graph","created_at":"2026-07-14T02:22:15Z","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/2607.11614/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Extending the context length of large language models (LLMs) is critical for many real-world applications, yet standard transformers remain constrained by quadratic compute and linear memory scaling. In this work, we investigate the Associative Recurrent Memory Transformer (ARMT) as a practical approach for enabling long-context processing in LLMs, constant memory scaling, and better efficiency. We make three main contributions. First, we construct two domain-specific long-context datasets designed to evaluate realistic workloads, focusing on narrow-domain fine-tuning scenarios. Second, we pro","authors_text":"Artem Shelmanov, Aydar Bulatov, Gleb Kuzmin, Ilia Sochenkov, Ivan Rodkin, Lyudmila Rvanova, Mikhail Burtsev, Mikhail Katkov, Misha Tsodyks, Timothy Baldwin, Yuri Kuratov","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-13T14:37:24Z","title":"Extending LLM Context via Associative Recurrent Memory"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11614","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":"sha256:504ad5cac2f21ce33069314010a75643d7bbcbf65b34bf6a5929afc92ae39d0b","target":"record","created_at":"2026-07-14T02:22:15Z","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":"42744a27ba310e638012ac4633d6005364c50ada5abd92de8821f2dd3a79f268","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-13T14:37:24Z","title_canon_sha256":"d087672539be881d82b04693b434782193312c9a0bc0e07055f8c30ee5c4d506"},"schema_version":"1.0","source":{"id":"2607.11614","kind":"arxiv","version":1}},"canonical_sha256":"4751ba1e84d491d1e8a4599d2f098e48ef1d4cc5332dd4422cdbe45866f06785","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4751ba1e84d491d1e8a4599d2f098e48ef1d4cc5332dd4422cdbe45866f06785","first_computed_at":"2026-07-14T02:22:15.037165Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-14T02:22:15.037165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"T41LT6BZWukG4rEip46Ccq0Z0b78UQmVd869hXJvqJKfggMihzZGuFAuZQupN8sh9L3BmEwf3vGXXNROe0I/Cg==","signature_status":"signed_v1","signed_at":"2026-07-14T02:22:15.038038Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.11614","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:504ad5cac2f21ce33069314010a75643d7bbcbf65b34bf6a5929afc92ae39d0b","sha256:3890a2a3786c2d4fac47a444582b3796c9f3cbe8351cc82bb3901e298553853f"],"state_sha256":"8295080e57d2de9b420dd4a6d62f544a354883590ad7b70184ee0cd9a4df9519"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MKDjrjzePgK6H3a/YdWFcI7MVvNUbQpbFSRlQ0Jibi5My+IhxcgVJ9nFVbqLtdzWwGbxnfKytnn82ppY2B76AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T03:03:20.251949Z","bundle_sha256":"8584f70c9128cff34edc4a243dd3bc083defeba8d37ef85c12a542faf4537fc8"}}