{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:55UIU3YMEGQR5FOJHFACOL5FBN","short_pith_number":"pith:55UIU3YM","schema_version":"1.0","canonical_sha256":"ef688a6f0c21a11e95c93940272fa50b487e6e74dec58dca78fa457cb1198f3e","source":{"kind":"arxiv","id":"2509.07577","version":2},"attestation_state":"computed","paper":{"title":"Towards explainable decision support using hybrid neural models for logistic terminal automation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Alberto Termine, Francesco Flammini, Riccardo D'Elia","submitted_at":"2025-09-09T10:41:08Z","abstract_excerpt":"The integration of Deep Learning (DL) in System Dynamics (SD) modeling for transportation logistics offers significant advantages in scalability and predictive accuracy. However, these gains are often offset by the loss of explainability and causal reliability $-$ key requirements in critical decision-making systems. This paper presents a novel framework for interpretable-by-design neural system dynamics modeling that synergizes DL with techniques from Concept-Based Interpretability, Mechanistic Interpretability, and Causal Machine Learning. The proposed hybrid approach enables the constructio"},"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":"2509.07577","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-09-09T10:41:08Z","cross_cats_sorted":[],"title_canon_sha256":"1c8d156268625fd1a101765e4bcd3f65e44eacbd182fa14a76b34d66c8995f47","abstract_canon_sha256":"39f12546321c5e40c13fcec66741558e2d8e22667d107a2fba68e90ab7f28fcf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:08:08.859223Z","signature_b64":"95BOCj9l3nHxjFpqOgnfMp4GrO3T8CLk1JWmHYOcMdv5LLqReoToVCRfldwcpvBjqf7H2UzJ8adZUOWr4fgEBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef688a6f0c21a11e95c93940272fa50b487e6e74dec58dca78fa457cb1198f3e","last_reissued_at":"2026-07-05T12:08:08.858570Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:08:08.858570Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards explainable decision support using hybrid neural models for logistic terminal automation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Alberto Termine, Francesco Flammini, Riccardo D'Elia","submitted_at":"2025-09-09T10:41:08Z","abstract_excerpt":"The integration of Deep Learning (DL) in System Dynamics (SD) modeling for transportation logistics offers significant advantages in scalability and predictive accuracy. However, these gains are often offset by the loss of explainability and causal reliability $-$ key requirements in critical decision-making systems. This paper presents a novel framework for interpretable-by-design neural system dynamics modeling that synergizes DL with techniques from Concept-Based Interpretability, Mechanistic Interpretability, and Causal Machine Learning. The proposed hybrid approach enables the constructio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.07577","kind":"arxiv","version":2},"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/2509.07577/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":"2509.07577","created_at":"2026-07-05T12:08:08.858643+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.07577v2","created_at":"2026-07-05T12:08:08.858643+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.07577","created_at":"2026-07-05T12:08:08.858643+00:00"},{"alias_kind":"pith_short_12","alias_value":"55UIU3YMEGQR","created_at":"2026-07-05T12:08:08.858643+00:00"},{"alias_kind":"pith_short_16","alias_value":"55UIU3YMEGQR5FOJ","created_at":"2026-07-05T12:08:08.858643+00:00"},{"alias_kind":"pith_short_8","alias_value":"55UIU3YM","created_at":"2026-07-05T12:08:08.858643+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/55UIU3YMEGQR5FOJHFACOL5FBN","json":"https://pith.science/pith/55UIU3YMEGQR5FOJHFACOL5FBN.json","graph_json":"https://pith.science/api/pith-number/55UIU3YMEGQR5FOJHFACOL5FBN/graph.json","events_json":"https://pith.science/api/pith-number/55UIU3YMEGQR5FOJHFACOL5FBN/events.json","paper":"https://pith.science/paper/55UIU3YM"},"agent_actions":{"view_html":"https://pith.science/pith/55UIU3YMEGQR5FOJHFACOL5FBN","download_json":"https://pith.science/pith/55UIU3YMEGQR5FOJHFACOL5FBN.json","view_paper":"https://pith.science/paper/55UIU3YM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.07577&json=true","fetch_graph":"https://pith.science/api/pith-number/55UIU3YMEGQR5FOJHFACOL5FBN/graph.json","fetch_events":"https://pith.science/api/pith-number/55UIU3YMEGQR5FOJHFACOL5FBN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/55UIU3YMEGQR5FOJHFACOL5FBN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/55UIU3YMEGQR5FOJHFACOL5FBN/action/storage_attestation","attest_author":"https://pith.science/pith/55UIU3YMEGQR5FOJHFACOL5FBN/action/author_attestation","sign_citation":"https://pith.science/pith/55UIU3YMEGQR5FOJHFACOL5FBN/action/citation_signature","submit_replication":"https://pith.science/pith/55UIU3YMEGQR5FOJHFACOL5FBN/action/replication_record"}},"created_at":"2026-07-05T12:08:08.858643+00:00","updated_at":"2026-07-05T12:08:08.858643+00:00"}