{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:5P2LTQZTSBCVLJ3ZWI3DP2NXYU","short_pith_number":"pith:5P2LTQZT","canonical_record":{"source":{"id":"2407.04363","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-05T09:06:47Z","cross_cats_sorted":[],"title_canon_sha256":"0818be0900499cdd7e55d84c2062f28d4e0123f23c48677702b7d1d6baad323d","abstract_canon_sha256":"b93ceea500c5f541f572ad11163067c5c3cae7ade0898c4a16265208e5e4d4bc"},"schema_version":"1.0"},"canonical_sha256":"ebf4b9c333904555a779b23637e9b7c53ac4b6f82c87575ba6d5cab7af57d6aa","source":{"kind":"arxiv","id":"2407.04363","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.04363","created_at":"2026-07-05T11:03:15Z"},{"alias_kind":"arxiv_version","alias_value":"2407.04363v3","created_at":"2026-07-05T11:03:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.04363","created_at":"2026-07-05T11:03:15Z"},{"alias_kind":"pith_short_12","alias_value":"5P2LTQZTSBCV","created_at":"2026-07-05T11:03:15Z"},{"alias_kind":"pith_short_16","alias_value":"5P2LTQZTSBCVLJ3Z","created_at":"2026-07-05T11:03:15Z"},{"alias_kind":"pith_short_8","alias_value":"5P2LTQZT","created_at":"2026-07-05T11:03:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:5P2LTQZTSBCVLJ3ZWI3DP2NXYU","target":"record","payload":{"canonical_record":{"source":{"id":"2407.04363","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-05T09:06:47Z","cross_cats_sorted":[],"title_canon_sha256":"0818be0900499cdd7e55d84c2062f28d4e0123f23c48677702b7d1d6baad323d","abstract_canon_sha256":"b93ceea500c5f541f572ad11163067c5c3cae7ade0898c4a16265208e5e4d4bc"},"schema_version":"1.0"},"canonical_sha256":"ebf4b9c333904555a779b23637e9b7c53ac4b6f82c87575ba6d5cab7af57d6aa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:03:15.578719Z","signature_b64":"/q1UUewwwLqY8Oy/iQlHqoL6yMmO4OLR1zepKtl1D6xKVwFm+95OKEEb4KTvX0ZgeiIqp5ndoGxuDYMpha7sAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebf4b9c333904555a779b23637e9b7c53ac4b6f82c87575ba6d5cab7af57d6aa","last_reissued_at":"2026-07-05T11:03:15.578209Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:03:15.578209Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.04363","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:03:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r3pNZHhhfJnIjmi+k1e0REng/z0lllGPNI4hqAsCDN2N6dRTPizGHQdsF8WyMTFDb5+pM0ILLZrp2/tYoRwRCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T16:25:42.915079Z"},"content_sha256":"baf0e888063061f281b61a2c64d85f2be291f3e9d6e9b46b821c7eed899c9f70","schema_version":"1.0","event_id":"sha256:baf0e888063061f281b61a2c64d85f2be291f3e9d6e9b46b821c7eed899c9f70"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:5P2LTQZTSBCVLJ3ZWI3DP2NXYU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Andrey Kravchenko, Artyom Sorokin, Dmitry Evseev, Evgeny Burnaev, Mikhail Burtsev, Nikita Semenov, Petr Anokhin","submitted_at":"2024-07-05T09:06:47Z","abstract_excerpt":"Advancements in the capabilities of Large Language Models (LLMs) have created a promising foundation for developing autonomous agents. With the right tools, these agents could learn to solve tasks in new environments by accumulating and updating their knowledge. Current LLM-based agents process past experiences using a full history of observations, summarization, retrieval augmentation. However, these unstructured memory representations do not facilitate the reasoning and planning essential for complex decision-making. In our study, we introduce AriGraph, a novel method wherein the agent const"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.04363","kind":"arxiv","version":3},"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/2407.04363/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":"2026-07-05T11:03:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gwy+OtkKL67CzJZQBM1CU9Au4ANNYl7kWAz4ejhA5bB/TKAkDXVhR88ESmCtKXN4ADg1yMx4Y//NJXlNDm/BBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T16:25:42.915560Z"},"content_sha256":"457e52cb4d898aa7ad28effa059a7d6db2e897b306d271089a8028df5cff593e","schema_version":"1.0","event_id":"sha256:457e52cb4d898aa7ad28effa059a7d6db2e897b306d271089a8028df5cff593e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5P2LTQZTSBCVLJ3ZWI3DP2NXYU/bundle.json","state_url":"https://pith.science/pith/5P2LTQZTSBCVLJ3ZWI3DP2NXYU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5P2LTQZTSBCVLJ3ZWI3DP2NXYU/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":"2026-08-07T16:25:42Z","links":{"resolver":"https://pith.science/pith/5P2LTQZTSBCVLJ3ZWI3DP2NXYU","bundle":"https://pith.science/pith/5P2LTQZTSBCVLJ3ZWI3DP2NXYU/bundle.json","state":"https://pith.science/pith/5P2LTQZTSBCVLJ3ZWI3DP2NXYU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5P2LTQZTSBCVLJ3ZWI3DP2NXYU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:5P2LTQZTSBCVLJ3ZWI3DP2NXYU","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":"b93ceea500c5f541f572ad11163067c5c3cae7ade0898c4a16265208e5e4d4bc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-05T09:06:47Z","title_canon_sha256":"0818be0900499cdd7e55d84c2062f28d4e0123f23c48677702b7d1d6baad323d"},"schema_version":"1.0","source":{"id":"2407.04363","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.04363","created_at":"2026-07-05T11:03:15Z"},{"alias_kind":"arxiv_version","alias_value":"2407.04363v3","created_at":"2026-07-05T11:03:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.04363","created_at":"2026-07-05T11:03:15Z"},{"alias_kind":"pith_short_12","alias_value":"5P2LTQZTSBCV","created_at":"2026-07-05T11:03:15Z"},{"alias_kind":"pith_short_16","alias_value":"5P2LTQZTSBCVLJ3Z","created_at":"2026-07-05T11:03:15Z"},{"alias_kind":"pith_short_8","alias_value":"5P2LTQZT","created_at":"2026-07-05T11:03:15Z"}],"graph_snapshots":[{"event_id":"sha256:457e52cb4d898aa7ad28effa059a7d6db2e897b306d271089a8028df5cff593e","target":"graph","created_at":"2026-07-05T11:03:15Z","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/2407.04363/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Advancements in the capabilities of Large Language Models (LLMs) have created a promising foundation for developing autonomous agents. With the right tools, these agents could learn to solve tasks in new environments by accumulating and updating their knowledge. Current LLM-based agents process past experiences using a full history of observations, summarization, retrieval augmentation. However, these unstructured memory representations do not facilitate the reasoning and planning essential for complex decision-making. In our study, we introduce AriGraph, a novel method wherein the agent const","authors_text":"Andrey Kravchenko, Artyom Sorokin, Dmitry Evseev, Evgeny Burnaev, Mikhail Burtsev, Nikita Semenov, Petr Anokhin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-05T09:06:47Z","title":"AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.04363","kind":"arxiv","version":3},"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:baf0e888063061f281b61a2c64d85f2be291f3e9d6e9b46b821c7eed899c9f70","target":"record","created_at":"2026-07-05T11:03:15Z","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":"b93ceea500c5f541f572ad11163067c5c3cae7ade0898c4a16265208e5e4d4bc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-05T09:06:47Z","title_canon_sha256":"0818be0900499cdd7e55d84c2062f28d4e0123f23c48677702b7d1d6baad323d"},"schema_version":"1.0","source":{"id":"2407.04363","kind":"arxiv","version":3}},"canonical_sha256":"ebf4b9c333904555a779b23637e9b7c53ac4b6f82c87575ba6d5cab7af57d6aa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ebf4b9c333904555a779b23637e9b7c53ac4b6f82c87575ba6d5cab7af57d6aa","first_computed_at":"2026-07-05T11:03:15.578209Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:03:15.578209Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/q1UUewwwLqY8Oy/iQlHqoL6yMmO4OLR1zepKtl1D6xKVwFm+95OKEEb4KTvX0ZgeiIqp5ndoGxuDYMpha7sAg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:03:15.578719Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.04363","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:baf0e888063061f281b61a2c64d85f2be291f3e9d6e9b46b821c7eed899c9f70","sha256:457e52cb4d898aa7ad28effa059a7d6db2e897b306d271089a8028df5cff593e"],"state_sha256":"5a6275894ac32f08569aca793c40f3164b9d9d26359b1abfe3f640c5c0b4e10a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9n6xNbbXwq3YPmPA92mhjVMFUIyaBfz1DKmzmqEx3a3kZ59La6Imc2E3HGG8x1r93IiorvweFDepsZwcxRffCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T16:25:42.920412Z","bundle_sha256":"97335ec93c37d702f3af5a46aeddf8ea35e0e85fa213483bc1d331cdc66dd989"}}