{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5NJNDZA3RSX4JIJW4GFUZX3GHY","short_pith_number":"pith:5NJNDZA3","schema_version":"1.0","canonical_sha256":"eb52d1e41b8cafc4a136e18b4cdf663e38fedcf5d7c5a6c687079cf1ce6c70cf","source":{"kind":"arxiv","id":"2507.17273","version":1},"attestation_state":"computed","paper":{"title":"Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Anirudh Deodhar, Rishi Parekh, Saisubramaniam Gopalakrishnan, Zishan Ahmad","submitted_at":"2025-07-23T07:18:55Z","abstract_excerpt":"Analyzing large, complex output datasets from Discrete Event Simulations (DES) of warehouse operations to identify bottlenecks and inefficiencies is a critical yet challenging task, often demanding significant manual effort or specialized analytical tools. Our framework integrates Knowledge Graphs (KGs) and Large Language Model (LLM)-based agents to analyze complex Discrete Event Simulation (DES) output data from warehouse operations. It transforms raw DES data into a semantically rich KG, capturing relationships between simulation events and entities. An LLM-based agent uses iterative reasoni"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.17273","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-23T07:18:55Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3afcea532eed4b8f4661164436bff535568542ad280cd14a5a3c5d1ceef88f2e","abstract_canon_sha256":"9056d701e26eaa945618643994d504047bff89b416af5b9db5b0cef2f9cdcfd0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:42:05.018581Z","signature_b64":"u2PEB17GYqES6eqU+ey22DLQcalG/p2DhXOZReJrYU4vJyvj198TjTZzZDC6WHm3iaHyfFoZDbCE793ZfyRvBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb52d1e41b8cafc4a136e18b4cdf663e38fedcf5d7c5a6c687079cf1ce6c70cf","last_reissued_at":"2026-07-05T11:42:05.018094Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:42:05.018094Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Anirudh Deodhar, Rishi Parekh, Saisubramaniam Gopalakrishnan, Zishan Ahmad","submitted_at":"2025-07-23T07:18:55Z","abstract_excerpt":"Analyzing large, complex output datasets from Discrete Event Simulations (DES) of warehouse operations to identify bottlenecks and inefficiencies is a critical yet challenging task, often demanding significant manual effort or specialized analytical tools. Our framework integrates Knowledge Graphs (KGs) and Large Language Model (LLM)-based agents to analyze complex Discrete Event Simulation (DES) output data from warehouse operations. It transforms raw DES data into a semantically rich KG, capturing relationships between simulation events and entities. An LLM-based agent uses iterative reasoni"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.17273","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/2507.17273/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.17273","created_at":"2026-07-05T11:42:05.018152+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.17273v1","created_at":"2026-07-05T11:42:05.018152+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.17273","created_at":"2026-07-05T11:42:05.018152+00:00"},{"alias_kind":"pith_short_12","alias_value":"5NJNDZA3RSX4","created_at":"2026-07-05T11:42:05.018152+00:00"},{"alias_kind":"pith_short_16","alias_value":"5NJNDZA3RSX4JIJW","created_at":"2026-07-05T11:42:05.018152+00:00"},{"alias_kind":"pith_short_8","alias_value":"5NJNDZA3","created_at":"2026-07-05T11:42:05.018152+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY","json":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY.json","graph_json":"https://pith.science/api/pith-number/5NJNDZA3RSX4JIJW4GFUZX3GHY/graph.json","events_json":"https://pith.science/api/pith-number/5NJNDZA3RSX4JIJW4GFUZX3GHY/events.json","paper":"https://pith.science/paper/5NJNDZA3"},"agent_actions":{"view_html":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY","download_json":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY.json","view_paper":"https://pith.science/paper/5NJNDZA3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.17273&json=true","fetch_graph":"https://pith.science/api/pith-number/5NJNDZA3RSX4JIJW4GFUZX3GHY/graph.json","fetch_events":"https://pith.science/api/pith-number/5NJNDZA3RSX4JIJW4GFUZX3GHY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY/action/storage_attestation","attest_author":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY/action/author_attestation","sign_citation":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY/action/citation_signature","submit_replication":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY/action/replication_record"}},"created_at":"2026-07-05T11:42:05.018152+00:00","updated_at":"2026-07-05T11:42:05.018152+00:00"}