{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UTGBR63A4B5JILBHFPPQNRXF6A","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":"d7af9c4738e27e7a508cb4405cfaa3a757399df63193309eefca9ac343e295f1","cross_cats_sorted":["cs.CL","cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-10-25T17:53:47Z","title_canon_sha256":"8b4c474671338fed3bc99382ee383f6709240906792657904bf46433691d82e7"},"schema_version":"1.0","source":{"id":"2410.19727","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.19727","created_at":"2026-07-05T09:26:00Z"},{"alias_kind":"arxiv_version","alias_value":"2410.19727v1","created_at":"2026-07-05T09:26:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.19727","created_at":"2026-07-05T09:26:00Z"},{"alias_kind":"pith_short_12","alias_value":"UTGBR63A4B5J","created_at":"2026-07-05T09:26:00Z"},{"alias_kind":"pith_short_16","alias_value":"UTGBR63A4B5JILBH","created_at":"2026-07-05T09:26:00Z"},{"alias_kind":"pith_short_8","alias_value":"UTGBR63A","created_at":"2026-07-05T09:26:00Z"}],"graph_snapshots":[{"event_id":"sha256:310c828bd6386f4db75e25843a98c0fd0e24fb0ed08216f476c2fcf4545b5222","target":"graph","created_at":"2026-07-05T09:26:00Z","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/2410.19727/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Financial intelligence generation from vast data sources has typically relied on traditional methods of knowledge-graph construction or database engineering. Recently, fine-tuned financial domain-specific Large Language Models (LLMs), have emerged. While these advancements are promising, limitations such as high inference costs, hallucinations, and the complexity of concurrently analyzing high-dimensional financial data, emerge. This motivates our invention FISHNET (Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert swarming, and Task planning), an agentic archi","authors_text":"Lucas Cecchi, Nicole Cho, Nishan Srishankar, William Watson","cross_cats":["cs.CL","cs.IR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-10-25T17:53:47Z","title":"FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.19727","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:da6aab7bfc9234ca053c861ff6ea505e983712ccce3ec8af638467dda4ada124","target":"record","created_at":"2026-07-05T09:26:00Z","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":"d7af9c4738e27e7a508cb4405cfaa3a757399df63193309eefca9ac343e295f1","cross_cats_sorted":["cs.CL","cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-10-25T17:53:47Z","title_canon_sha256":"8b4c474671338fed3bc99382ee383f6709240906792657904bf46433691d82e7"},"schema_version":"1.0","source":{"id":"2410.19727","kind":"arxiv","version":1}},"canonical_sha256":"a4cc18fb60e07a942c272bdf06c6e5f03305ce3aaec5779898f0b53736fd851a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a4cc18fb60e07a942c272bdf06c6e5f03305ce3aaec5779898f0b53736fd851a","first_computed_at":"2026-07-05T09:26:00.889850Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:26:00.889850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zObpk6ODggWl0ipimtUaMwQv8nk1kMDVst/ujMvX8j6DzhlR8a1Fq5OZY1giu/CB/lgY2n8GlvlHVooMHJVHDg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:26:00.890352Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.19727","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:da6aab7bfc9234ca053c861ff6ea505e983712ccce3ec8af638467dda4ada124","sha256:310c828bd6386f4db75e25843a98c0fd0e24fb0ed08216f476c2fcf4545b5222"],"state_sha256":"5b3476392c73088306bce73954f8a62e2fe1807332d6b6a6440540ba29c0b6a3"}