{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PGVVBZEBZAD4VNNLWOAYBTBNZ7","short_pith_number":"pith:PGVVBZEB","canonical_record":{"source":{"id":"2605.17903","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-18T06:07:49Z","cross_cats_sorted":["cs.CL","cs.HC","cs.IR"],"title_canon_sha256":"dec5c3d76598c11548f37cb9e58811477ada7498f1bab6b452e78f05de41039d","abstract_canon_sha256":"881e6a69b1e04518d7f563bbf63b36bc51a401125d9f8520803ba1245e46c058"},"schema_version":"1.0"},"canonical_sha256":"79ab50e481c807cab5abb38180cc2dcffc9722c0680ed03c92fe2439e6a74d13","source":{"kind":"arxiv","id":"2605.17903","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17903","created_at":"2026-05-20T00:05:04Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17903v1","created_at":"2026-05-20T00:05:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17903","created_at":"2026-05-20T00:05:04Z"},{"alias_kind":"pith_short_12","alias_value":"PGVVBZEBZAD4","created_at":"2026-05-20T00:05:04Z"},{"alias_kind":"pith_short_16","alias_value":"PGVVBZEBZAD4VNNL","created_at":"2026-05-20T00:05:04Z"},{"alias_kind":"pith_short_8","alias_value":"PGVVBZEB","created_at":"2026-05-20T00:05:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PGVVBZEBZAD4VNNLWOAYBTBNZ7","target":"record","payload":{"canonical_record":{"source":{"id":"2605.17903","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-18T06:07:49Z","cross_cats_sorted":["cs.CL","cs.HC","cs.IR"],"title_canon_sha256":"dec5c3d76598c11548f37cb9e58811477ada7498f1bab6b452e78f05de41039d","abstract_canon_sha256":"881e6a69b1e04518d7f563bbf63b36bc51a401125d9f8520803ba1245e46c058"},"schema_version":"1.0"},"canonical_sha256":"79ab50e481c807cab5abb38180cc2dcffc9722c0680ed03c92fe2439e6a74d13","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:05:04.892076Z","signature_b64":"OEN4r7mZmozhmFxQslIPICy8edaBXGjOysmMzYlMdy7Sj1p8680duIuhxuoynTTTx0g5aNrRFAJN/+BcPZ/KCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79ab50e481c807cab5abb38180cc2dcffc9722c0680ed03c92fe2439e6a74d13","last_reissued_at":"2026-05-20T00:05:04.891382Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:05:04.891382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.17903","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":"2026-05-20T00:05:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OOyjNLZ9ldstH1IbI3qNugPcZYqtDcKMjubk5y5YVJ4u3T+FzNNCwqWApeM201x60AdmBoDD1+arqGDE3FWXDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T09:52:04.520875Z"},"content_sha256":"f66276c665d67c41645afb40117c11359e3776cc8fe74d0da4500a0450506d38","schema_version":"1.0","event_id":"sha256:f66276c665d67c41645afb40117c11359e3776cc8fe74d0da4500a0450506d38"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PGVVBZEBZAD4VNNLWOAYBTBNZ7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.HC","cs.IR"],"primary_cat":"cs.AI","authors_text":"Akash Kumar Panda, Bart Kosko, Olaoluwa Adigun","submitted_at":"2026-05-18T06:07:49Z","abstract_excerpt":"We automatically generate feedback causal fuzzy cognitive maps (FCMs) from text by teaching large-language-model agents to break the text into overlapping chunks of text. Convex mixing of these chunk FCMs gives a representative cyclic FCM knowledge graph. The text chunks can have different levels of overlap. The chunk FCMs still mix to form a new FCM causal knowledge graph. The mixing technique scales because it uses light computation with sparse causal chunk matrices. The mixing structure allows an operator-level type of Bayesian inference that produces \"de-chunked\" or posterior-like FCMs fro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17903","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/2605.17903/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":"2026-05-20T00:05:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EHJ0Vp0VEp/UcqjKYc0CWXDpwLl2tPD3oDnrLPlBNKiBer9zNMrl7fYTxJMhC2uEl6siNMs9tu4+KQl8uVksCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T09:52:04.521533Z"},"content_sha256":"16745f212a5d7c235c5d1d6c985a2e416192d8d6d0bc1bf7767873e22fb787bd","schema_version":"1.0","event_id":"sha256:16745f212a5d7c235c5d1d6c985a2e416192d8d6d0bc1bf7767873e22fb787bd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PGVVBZEBZAD4VNNLWOAYBTBNZ7/bundle.json","state_url":"https://pith.science/pith/PGVVBZEBZAD4VNNLWOAYBTBNZ7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PGVVBZEBZAD4VNNLWOAYBTBNZ7/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":"2026-06-07T09:52:04Z","links":{"resolver":"https://pith.science/pith/PGVVBZEBZAD4VNNLWOAYBTBNZ7","bundle":"https://pith.science/pith/PGVVBZEBZAD4VNNLWOAYBTBNZ7/bundle.json","state":"https://pith.science/pith/PGVVBZEBZAD4VNNLWOAYBTBNZ7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PGVVBZEBZAD4VNNLWOAYBTBNZ7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PGVVBZEBZAD4VNNLWOAYBTBNZ7","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":"881e6a69b1e04518d7f563bbf63b36bc51a401125d9f8520803ba1245e46c058","cross_cats_sorted":["cs.CL","cs.HC","cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-18T06:07:49Z","title_canon_sha256":"dec5c3d76598c11548f37cb9e58811477ada7498f1bab6b452e78f05de41039d"},"schema_version":"1.0","source":{"id":"2605.17903","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17903","created_at":"2026-05-20T00:05:04Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17903v1","created_at":"2026-05-20T00:05:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17903","created_at":"2026-05-20T00:05:04Z"},{"alias_kind":"pith_short_12","alias_value":"PGVVBZEBZAD4","created_at":"2026-05-20T00:05:04Z"},{"alias_kind":"pith_short_16","alias_value":"PGVVBZEBZAD4VNNL","created_at":"2026-05-20T00:05:04Z"},{"alias_kind":"pith_short_8","alias_value":"PGVVBZEB","created_at":"2026-05-20T00:05:04Z"}],"graph_snapshots":[{"event_id":"sha256:16745f212a5d7c235c5d1d6c985a2e416192d8d6d0bc1bf7767873e22fb787bd","target":"graph","created_at":"2026-05-20T00:05:04Z","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/2605.17903/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We automatically generate feedback causal fuzzy cognitive maps (FCMs) from text by teaching large-language-model agents to break the text into overlapping chunks of text. Convex mixing of these chunk FCMs gives a representative cyclic FCM knowledge graph. The text chunks can have different levels of overlap. The chunk FCMs still mix to form a new FCM causal knowledge graph. The mixing technique scales because it uses light computation with sparse causal chunk matrices. The mixing structure allows an operator-level type of Bayesian inference that produces \"de-chunked\" or posterior-like FCMs fro","authors_text":"Akash Kumar Panda, Bart Kosko, Olaoluwa Adigun","cross_cats":["cs.CL","cs.HC","cs.IR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-18T06:07:49Z","title":"Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17903","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:f66276c665d67c41645afb40117c11359e3776cc8fe74d0da4500a0450506d38","target":"record","created_at":"2026-05-20T00:05:04Z","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":"881e6a69b1e04518d7f563bbf63b36bc51a401125d9f8520803ba1245e46c058","cross_cats_sorted":["cs.CL","cs.HC","cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-18T06:07:49Z","title_canon_sha256":"dec5c3d76598c11548f37cb9e58811477ada7498f1bab6b452e78f05de41039d"},"schema_version":"1.0","source":{"id":"2605.17903","kind":"arxiv","version":1}},"canonical_sha256":"79ab50e481c807cab5abb38180cc2dcffc9722c0680ed03c92fe2439e6a74d13","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"79ab50e481c807cab5abb38180cc2dcffc9722c0680ed03c92fe2439e6a74d13","first_computed_at":"2026-05-20T00:05:04.891382Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:05:04.891382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OEN4r7mZmozhmFxQslIPICy8edaBXGjOysmMzYlMdy7Sj1p8680duIuhxuoynTTTx0g5aNrRFAJN/+BcPZ/KCA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:05:04.892076Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17903","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f66276c665d67c41645afb40117c11359e3776cc8fe74d0da4500a0450506d38","sha256:16745f212a5d7c235c5d1d6c985a2e416192d8d6d0bc1bf7767873e22fb787bd"],"state_sha256":"9148d17e3d3450ba17afb00b46a3c34e69628f63ca4e3e26a217fe2fce0351e5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/4leVaEGYkWQPJpXGEFWadvjyLi5UJRNI+fICXRTQMvz2njJ5FYb48+k77H3qSE2+qt0QpkMJ0E2FPBpj3WBDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T09:52:04.525514Z","bundle_sha256":"e82962a3dbb28859068bfc90a4d96e7065d224891a66b496bf16d8cf64b7649d"}}