{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:INXQZHVRQJ7SKJPUMLPIECYPBA","short_pith_number":"pith:INXQZHVR","schema_version":"1.0","canonical_sha256":"436f0c9eb1827f2525f462de820b0f080fbc1a30c3277bc807cb4d01915b4729","source":{"kind":"arxiv","id":"2407.06245","version":2},"attestation_state":"computed","paper":{"title":"ORAN-Bench-13K: An Open Source Benchmark for Assessing LLMs in Open Radio Access Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.NI","authors_text":"Pranshav Gajjar, Vijay K. Shah","submitted_at":"2024-07-08T13:07:50Z","abstract_excerpt":"Large Language Models (LLMs) can revolutionize how we deploy and operate Open Radio Access Networks (O-RAN) by enhancing network analytics, anomaly detection, and code generation and significantly increasing the efficiency and reliability of a plethora of O-RAN tasks. In this paper, we present ORAN-Bench-13K, the first comprehensive benchmark designed to evaluate the performance of Large Language Models (LLMs) within the context of O-RAN. Our benchmark consists of 13,952 meticulously curated multiple-choice questions generated from 116 O-RAN specification documents. We leverage a novel three-s"},"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":"2407.06245","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-07-08T13:07:50Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"title_canon_sha256":"6242047b1791628848d73011d2d336050760e72a60a7b6f324480bea9fbd9295","abstract_canon_sha256":"ae2df071bbc0806281e738d716747ea21e6f7a2bb1d4b6eea68b89ec282a82b4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:43:30.196483Z","signature_b64":"Znh64qqh3oP+CpTg6vUst3pEwM9d1h6mQ3O8pxyCzSKMKFDCMLm191jpQkXWNW66zqj+4Ft9R00I8kZ+4OvbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"436f0c9eb1827f2525f462de820b0f080fbc1a30c3277bc807cb4d01915b4729","last_reissued_at":"2026-07-05T08:43:30.196006Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:43:30.196006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ORAN-Bench-13K: An Open Source Benchmark for Assessing LLMs in Open Radio Access Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.NI","authors_text":"Pranshav Gajjar, Vijay K. Shah","submitted_at":"2024-07-08T13:07:50Z","abstract_excerpt":"Large Language Models (LLMs) can revolutionize how we deploy and operate Open Radio Access Networks (O-RAN) by enhancing network analytics, anomaly detection, and code generation and significantly increasing the efficiency and reliability of a plethora of O-RAN tasks. In this paper, we present ORAN-Bench-13K, the first comprehensive benchmark designed to evaluate the performance of Large Language Models (LLMs) within the context of O-RAN. Our benchmark consists of 13,952 meticulously curated multiple-choice questions generated from 116 O-RAN specification documents. We leverage a novel three-s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.06245","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/2407.06245/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":"2407.06245","created_at":"2026-07-05T08:43:30.196067+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.06245v2","created_at":"2026-07-05T08:43:30.196067+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.06245","created_at":"2026-07-05T08:43:30.196067+00:00"},{"alias_kind":"pith_short_12","alias_value":"INXQZHVRQJ7S","created_at":"2026-07-05T08:43:30.196067+00:00"},{"alias_kind":"pith_short_16","alias_value":"INXQZHVRQJ7SKJPU","created_at":"2026-07-05T08:43:30.196067+00:00"},{"alias_kind":"pith_short_8","alias_value":"INXQZHVR","created_at":"2026-07-05T08:43:30.196067+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.03608","citing_title":"Benchmarking Vector, Graph and Hybrid Retrieval Augmented Generation (RAG) Pipelines for Open Radio Access Networks (ORAN)","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/INXQZHVRQJ7SKJPUMLPIECYPBA","json":"https://pith.science/pith/INXQZHVRQJ7SKJPUMLPIECYPBA.json","graph_json":"https://pith.science/api/pith-number/INXQZHVRQJ7SKJPUMLPIECYPBA/graph.json","events_json":"https://pith.science/api/pith-number/INXQZHVRQJ7SKJPUMLPIECYPBA/events.json","paper":"https://pith.science/paper/INXQZHVR"},"agent_actions":{"view_html":"https://pith.science/pith/INXQZHVRQJ7SKJPUMLPIECYPBA","download_json":"https://pith.science/pith/INXQZHVRQJ7SKJPUMLPIECYPBA.json","view_paper":"https://pith.science/paper/INXQZHVR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.06245&json=true","fetch_graph":"https://pith.science/api/pith-number/INXQZHVRQJ7SKJPUMLPIECYPBA/graph.json","fetch_events":"https://pith.science/api/pith-number/INXQZHVRQJ7SKJPUMLPIECYPBA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/INXQZHVRQJ7SKJPUMLPIECYPBA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/INXQZHVRQJ7SKJPUMLPIECYPBA/action/storage_attestation","attest_author":"https://pith.science/pith/INXQZHVRQJ7SKJPUMLPIECYPBA/action/author_attestation","sign_citation":"https://pith.science/pith/INXQZHVRQJ7SKJPUMLPIECYPBA/action/citation_signature","submit_replication":"https://pith.science/pith/INXQZHVRQJ7SKJPUMLPIECYPBA/action/replication_record"}},"created_at":"2026-07-05T08:43:30.196067+00:00","updated_at":"2026-07-05T08:43:30.196067+00:00"}