{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2ZJQPFHF56OGL4PTJZZFH35AT2","short_pith_number":"pith:2ZJQPFHF","schema_version":"1.0","canonical_sha256":"d6530794e5ef9c65f1f34e7253efa09e85ecdf4aad17e020d554795d93a0834d","source":{"kind":"arxiv","id":"2407.10186","version":2},"attestation_state":"computed","paper":{"title":"Toward Explainable Reasoning in 6G: A Proof of Concept Study on Radio Resource Allocation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Dusit Niyato, Farhad Rezazadeh, Hatim Chergui, Houbing Song, Josep Mangues, Lingjia Liu, Mehdi Bennis, Sergio Barrachina-Mu\\~noz","submitted_at":"2024-07-14T13:09:49Z","abstract_excerpt":"The move toward artificial intelligence (AI)-native sixth-generation (6G) networks has put more emphasis on the importance of explainability and trustworthiness in network management operations, especially for mission-critical use-cases. Such desired trust transcends traditional post-hoc explainable AI (XAI) methods to using contextual explanations for guiding the learning process in an in-hoc way. This paper proposes a novel graph reinforcement learning (GRL) framework named TANGO which relies on a symbolic subsystem. It consists of a Bayesian-graph neural network (GNN) Explainer, whose outpu"},"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":"2407.10186","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-07-14T13:09:49Z","cross_cats_sorted":[],"title_canon_sha256":"72eb279af3676900065ebd4368497fb87a9671ccded1d14e1954bdd0d29d60d7","abstract_canon_sha256":"194795da54868a835d9047f8e3808e64a2450a35ad6a68cdfedd09005e4847da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:09:05.737217Z","signature_b64":"4bTSbBAj4Jh0xYdIY7sOftxMhDsR0hJ/xus/jSVJKXWdAPb31GcP8BYJHSAmUY0akEB/N4sBUOk8V1hirJfJBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6530794e5ef9c65f1f34e7253efa09e85ecdf4aad17e020d554795d93a0834d","last_reissued_at":"2026-07-05T09:09:05.736706Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:09:05.736706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Toward Explainable Reasoning in 6G: A Proof of Concept Study on Radio Resource Allocation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Dusit Niyato, Farhad Rezazadeh, Hatim Chergui, Houbing Song, Josep Mangues, Lingjia Liu, Mehdi Bennis, Sergio Barrachina-Mu\\~noz","submitted_at":"2024-07-14T13:09:49Z","abstract_excerpt":"The move toward artificial intelligence (AI)-native sixth-generation (6G) networks has put more emphasis on the importance of explainability and trustworthiness in network management operations, especially for mission-critical use-cases. Such desired trust transcends traditional post-hoc explainable AI (XAI) methods to using contextual explanations for guiding the learning process in an in-hoc way. This paper proposes a novel graph reinforcement learning (GRL) framework named TANGO which relies on a symbolic subsystem. It consists of a Bayesian-graph neural network (GNN) Explainer, whose outpu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.10186","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/2407.10186/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":"2407.10186","created_at":"2026-07-05T09:09:05.736768+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.10186v2","created_at":"2026-07-05T09:09:05.736768+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.10186","created_at":"2026-07-05T09:09:05.736768+00:00"},{"alias_kind":"pith_short_12","alias_value":"2ZJQPFHF56OG","created_at":"2026-07-05T09:09:05.736768+00:00"},{"alias_kind":"pith_short_16","alias_value":"2ZJQPFHF56OGL4PT","created_at":"2026-07-05T09:09:05.736768+00:00"},{"alias_kind":"pith_short_8","alias_value":"2ZJQPFHF","created_at":"2026-07-05T09:09:05.736768+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/2ZJQPFHF56OGL4PTJZZFH35AT2","json":"https://pith.science/pith/2ZJQPFHF56OGL4PTJZZFH35AT2.json","graph_json":"https://pith.science/api/pith-number/2ZJQPFHF56OGL4PTJZZFH35AT2/graph.json","events_json":"https://pith.science/api/pith-number/2ZJQPFHF56OGL4PTJZZFH35AT2/events.json","paper":"https://pith.science/paper/2ZJQPFHF"},"agent_actions":{"view_html":"https://pith.science/pith/2ZJQPFHF56OGL4PTJZZFH35AT2","download_json":"https://pith.science/pith/2ZJQPFHF56OGL4PTJZZFH35AT2.json","view_paper":"https://pith.science/paper/2ZJQPFHF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.10186&json=true","fetch_graph":"https://pith.science/api/pith-number/2ZJQPFHF56OGL4PTJZZFH35AT2/graph.json","fetch_events":"https://pith.science/api/pith-number/2ZJQPFHF56OGL4PTJZZFH35AT2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2ZJQPFHF56OGL4PTJZZFH35AT2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2ZJQPFHF56OGL4PTJZZFH35AT2/action/storage_attestation","attest_author":"https://pith.science/pith/2ZJQPFHF56OGL4PTJZZFH35AT2/action/author_attestation","sign_citation":"https://pith.science/pith/2ZJQPFHF56OGL4PTJZZFH35AT2/action/citation_signature","submit_replication":"https://pith.science/pith/2ZJQPFHF56OGL4PTJZZFH35AT2/action/replication_record"}},"created_at":"2026-07-05T09:09:05.736768+00:00","updated_at":"2026-07-05T09:09:05.736768+00:00"}