{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:5NJNDZA3RSX4JIJW4GFUZX3GHY","short_pith_number":"pith:5NJNDZA3","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"},"canonical_sha256":"eb52d1e41b8cafc4a136e18b4cdf663e38fedcf5d7c5a6c687079cf1ce6c70cf","source":{"kind":"arxiv","id":"2507.17273","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.17273","created_at":"2026-07-05T11:42:05Z"},{"alias_kind":"arxiv_version","alias_value":"2507.17273v1","created_at":"2026-07-05T11:42:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.17273","created_at":"2026-07-05T11:42:05Z"},{"alias_kind":"pith_short_12","alias_value":"5NJNDZA3RSX4","created_at":"2026-07-05T11:42:05Z"},{"alias_kind":"pith_short_16","alias_value":"5NJNDZA3RSX4JIJW","created_at":"2026-07-05T11:42:05Z"},{"alias_kind":"pith_short_8","alias_value":"5NJNDZA3","created_at":"2026-07-05T11:42:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:5NJNDZA3RSX4JIJW4GFUZX3GHY","target":"record","payload":{"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"},"canonical_sha256":"eb52d1e41b8cafc4a136e18b4cdf663e38fedcf5d7c5a6c687079cf1ce6c70cf","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"},"source_kind":"arxiv","source_id":"2507.17273","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":"2026-07-05T11:42:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"khHo9ls2z0LH37bYhTbRmUSLy38OdZc9xYLr54gq0rEuSJ3XRgWP2EtPPeAMPZviPgPZUGnz03VPMcgqoAH9Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T05:26:25.568511Z"},"content_sha256":"e91e6281e9f3cec0838efe25913c569f2e55a1626e714770caeb2f3d7781eb57","schema_version":"1.0","event_id":"sha256:e91e6281e9f3cec0838efe25913c569f2e55a1626e714770caeb2f3d7781eb57"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:5NJNDZA3RSX4JIJW4GFUZX3GHY","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:42:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5vv8Y5IwKBZTx86KTVh2vgBxpUGFRKb4M2wTkpBV/D+juqsyNAVml0KXz0K+5agQcE9iVSrsv1IjLyYhQmvMCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T05:26:25.569111Z"},"content_sha256":"56bb10451aaa0d94eacc17e7660287f2c1092b9642b49db7aa957df83bcc4644","schema_version":"1.0","event_id":"sha256:56bb10451aaa0d94eacc17e7660287f2c1092b9642b49db7aa957df83bcc4644"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY/bundle.json","state_url":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY/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":"2026-08-07T05:26:25Z","links":{"resolver":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY","bundle":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY/bundle.json","state":"https://pith.science/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5NJNDZA3RSX4JIJW4GFUZX3GHY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:5NJNDZA3RSX4JIJW4GFUZX3GHY","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":"9056d701e26eaa945618643994d504047bff89b416af5b9db5b0cef2f9cdcfd0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-23T07:18:55Z","title_canon_sha256":"3afcea532eed4b8f4661164436bff535568542ad280cd14a5a3c5d1ceef88f2e"},"schema_version":"1.0","source":{"id":"2507.17273","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.17273","created_at":"2026-07-05T11:42:05Z"},{"alias_kind":"arxiv_version","alias_value":"2507.17273v1","created_at":"2026-07-05T11:42:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.17273","created_at":"2026-07-05T11:42:05Z"},{"alias_kind":"pith_short_12","alias_value":"5NJNDZA3RSX4","created_at":"2026-07-05T11:42:05Z"},{"alias_kind":"pith_short_16","alias_value":"5NJNDZA3RSX4JIJW","created_at":"2026-07-05T11:42:05Z"},{"alias_kind":"pith_short_8","alias_value":"5NJNDZA3","created_at":"2026-07-05T11:42:05Z"}],"graph_snapshots":[{"event_id":"sha256:56bb10451aaa0d94eacc17e7660287f2c1092b9642b49db7aa957df83bcc4644","target":"graph","created_at":"2026-07-05T11:42:05Z","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/2507.17273/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Anirudh Deodhar, Rishi Parekh, Saisubramaniam Gopalakrishnan, Zishan Ahmad","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-23T07:18:55Z","title":"Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.17273","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:e91e6281e9f3cec0838efe25913c569f2e55a1626e714770caeb2f3d7781eb57","target":"record","created_at":"2026-07-05T11:42:05Z","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":"9056d701e26eaa945618643994d504047bff89b416af5b9db5b0cef2f9cdcfd0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-23T07:18:55Z","title_canon_sha256":"3afcea532eed4b8f4661164436bff535568542ad280cd14a5a3c5d1ceef88f2e"},"schema_version":"1.0","source":{"id":"2507.17273","kind":"arxiv","version":1}},"canonical_sha256":"eb52d1e41b8cafc4a136e18b4cdf663e38fedcf5d7c5a6c687079cf1ce6c70cf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eb52d1e41b8cafc4a136e18b4cdf663e38fedcf5d7c5a6c687079cf1ce6c70cf","first_computed_at":"2026-07-05T11:42:05.018094Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:42:05.018094Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u2PEB17GYqES6eqU+ey22DLQcalG/p2DhXOZReJrYU4vJyvj198TjTZzZDC6WHm3iaHyfFoZDbCE793ZfyRvBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:42:05.018581Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.17273","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e91e6281e9f3cec0838efe25913c569f2e55a1626e714770caeb2f3d7781eb57","sha256:56bb10451aaa0d94eacc17e7660287f2c1092b9642b49db7aa957df83bcc4644"],"state_sha256":"81ff028b5153f4c1cce764a36e287368b0adc07ea3b64a1acba6aeba54f703d0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CTzuqDACJJkZ7plBaJ+dsSLrt9etwz5OqxLZMcuonRhkXrecZcqsQwJx87hbpz0l4lCG7aL/tPUFG6Z6mcnOCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T05:26:25.573779Z","bundle_sha256":"448485547888aa08c42bc80eda4725c30d126f56fd40591250f80140f05dad29"}}