{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:5X6HSZBCGSVR2TXJ4WIIAWBDEJ","short_pith_number":"pith:5X6HSZBC","canonical_record":{"source":{"id":"2201.11410","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-01-27T10:02:54Z","cross_cats_sorted":["cs.AI","cs.NI","math.IT"],"title_canon_sha256":"f90b44a938456d86330c32328fb0923fbb8d5fa78c99a958133d43fa1ef1ee08","abstract_canon_sha256":"f2259af4d26463010ab23c3c196c61cd4f14dfd41939854e2d0823ea22ac409b"},"schema_version":"1.0"},"canonical_sha256":"edfc79642234ab1d4ee9e590805823227ce9d644c80da433855d064553604999","source":{"kind":"arxiv","id":"2201.11410","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.11410","created_at":"2026-07-05T04:32:55Z"},{"alias_kind":"arxiv_version","alias_value":"2201.11410v4","created_at":"2026-07-05T04:32:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.11410","created_at":"2026-07-05T04:32:55Z"},{"alias_kind":"pith_short_12","alias_value":"5X6HSZBCGSVR","created_at":"2026-07-05T04:32:55Z"},{"alias_kind":"pith_short_16","alias_value":"5X6HSZBCGSVR2TXJ","created_at":"2026-07-05T04:32:55Z"},{"alias_kind":"pith_short_8","alias_value":"5X6HSZBC","created_at":"2026-07-05T04:32:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:5X6HSZBCGSVR2TXJ4WIIAWBDEJ","target":"record","payload":{"canonical_record":{"source":{"id":"2201.11410","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-01-27T10:02:54Z","cross_cats_sorted":["cs.AI","cs.NI","math.IT"],"title_canon_sha256":"f90b44a938456d86330c32328fb0923fbb8d5fa78c99a958133d43fa1ef1ee08","abstract_canon_sha256":"f2259af4d26463010ab23c3c196c61cd4f14dfd41939854e2d0823ea22ac409b"},"schema_version":"1.0"},"canonical_sha256":"edfc79642234ab1d4ee9e590805823227ce9d644c80da433855d064553604999","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:32:55.158192Z","signature_b64":"IJiGOej6wI1jnMqkyrD2I5rGt+pmjxvgfrs+dAJMZGRqU9EbfXHZ2LVpqpha8okNxEyZOGD0Qip+5RfshJedAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"edfc79642234ab1d4ee9e590805823227ce9d644c80da433855d064553604999","last_reissued_at":"2026-07-05T04:32:55.157728Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:32:55.157728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2201.11410","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:32:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W35FN/DT7rcgD7PfH59iR5EKMOEVn2DB+M6x7qoodEbkrCDqpgyaQJq8rBqjJsIcjQkbORx6d9t6HIP+TdJmDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T13:04:08.391391Z"},"content_sha256":"a367f1ff17b1b7077fbbb9285c858b0910cc876be8b72b96548ada3e1bae3a40","schema_version":"1.0","event_id":"sha256:a367f1ff17b1b7077fbbb9285c858b0910cc876be8b72b96548ada3e1bae3a40"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:5X6HSZBCGSVR2TXJ4WIIAWBDEJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NI","math.IT"],"primary_cat":"cs.IT","authors_text":"Jue Wang, Kun Guo, Ning Ge, Peng Wei, Shi Jin, Wei Feng, Ye Li, Ying-Chang Liang","submitted_at":"2022-01-27T10:02:54Z","abstract_excerpt":"Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization problems. Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC. Furthermore, its evolved versions, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.11410","kind":"arxiv","version":4},"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/2201.11410/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":"2026-07-05T04:32:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EexmQZAI/lKHUm7hgiAceL7rsrVyXBvlJMP5G4EJw+N5vclghdsRoYVZ2p+HXHv8JEC+8BJRk9g4ktJwdC6uCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T13:04:08.391754Z"},"content_sha256":"9e624fff7c5f0b558d5e57bc1986c3fd1e4caf7b207299224826759f7fb32f3d","schema_version":"1.0","event_id":"sha256:9e624fff7c5f0b558d5e57bc1986c3fd1e4caf7b207299224826759f7fb32f3d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5X6HSZBCGSVR2TXJ4WIIAWBDEJ/bundle.json","state_url":"https://pith.science/pith/5X6HSZBCGSVR2TXJ4WIIAWBDEJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5X6HSZBCGSVR2TXJ4WIIAWBDEJ/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":"2026-07-24T13:04:08Z","links":{"resolver":"https://pith.science/pith/5X6HSZBCGSVR2TXJ4WIIAWBDEJ","bundle":"https://pith.science/pith/5X6HSZBCGSVR2TXJ4WIIAWBDEJ/bundle.json","state":"https://pith.science/pith/5X6HSZBCGSVR2TXJ4WIIAWBDEJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5X6HSZBCGSVR2TXJ4WIIAWBDEJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:5X6HSZBCGSVR2TXJ4WIIAWBDEJ","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":"f2259af4d26463010ab23c3c196c61cd4f14dfd41939854e2d0823ea22ac409b","cross_cats_sorted":["cs.AI","cs.NI","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-01-27T10:02:54Z","title_canon_sha256":"f90b44a938456d86330c32328fb0923fbb8d5fa78c99a958133d43fa1ef1ee08"},"schema_version":"1.0","source":{"id":"2201.11410","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.11410","created_at":"2026-07-05T04:32:55Z"},{"alias_kind":"arxiv_version","alias_value":"2201.11410v4","created_at":"2026-07-05T04:32:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.11410","created_at":"2026-07-05T04:32:55Z"},{"alias_kind":"pith_short_12","alias_value":"5X6HSZBCGSVR","created_at":"2026-07-05T04:32:55Z"},{"alias_kind":"pith_short_16","alias_value":"5X6HSZBCGSVR2TXJ","created_at":"2026-07-05T04:32:55Z"},{"alias_kind":"pith_short_8","alias_value":"5X6HSZBC","created_at":"2026-07-05T04:32:55Z"}],"graph_snapshots":[{"event_id":"sha256:9e624fff7c5f0b558d5e57bc1986c3fd1e4caf7b207299224826759f7fb32f3d","target":"graph","created_at":"2026-07-05T04:32:55Z","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/2201.11410/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization problems. Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC. Furthermore, its evolved versions, ","authors_text":"Jue Wang, Kun Guo, Ning Ge, Peng Wei, Shi Jin, Wei Feng, Ye Li, Ying-Chang Liang","cross_cats":["cs.AI","cs.NI","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-01-27T10:02:54Z","title":"Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.11410","kind":"arxiv","version":4},"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:a367f1ff17b1b7077fbbb9285c858b0910cc876be8b72b96548ada3e1bae3a40","target":"record","created_at":"2026-07-05T04:32:55Z","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":"f2259af4d26463010ab23c3c196c61cd4f14dfd41939854e2d0823ea22ac409b","cross_cats_sorted":["cs.AI","cs.NI","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-01-27T10:02:54Z","title_canon_sha256":"f90b44a938456d86330c32328fb0923fbb8d5fa78c99a958133d43fa1ef1ee08"},"schema_version":"1.0","source":{"id":"2201.11410","kind":"arxiv","version":4}},"canonical_sha256":"edfc79642234ab1d4ee9e590805823227ce9d644c80da433855d064553604999","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"edfc79642234ab1d4ee9e590805823227ce9d644c80da433855d064553604999","first_computed_at":"2026-07-05T04:32:55.157728Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:32:55.157728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IJiGOej6wI1jnMqkyrD2I5rGt+pmjxvgfrs+dAJMZGRqU9EbfXHZ2LVpqpha8okNxEyZOGD0Qip+5RfshJedAg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:32:55.158192Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.11410","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a367f1ff17b1b7077fbbb9285c858b0910cc876be8b72b96548ada3e1bae3a40","sha256:9e624fff7c5f0b558d5e57bc1986c3fd1e4caf7b207299224826759f7fb32f3d"],"state_sha256":"974bd8acebcc9bfc8ed006acffff3dd00049dde3c1e37a9c5a00f45b60391962"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OWmFjkyop0FGxKC6NYzKcuLNjwAmsCY6khWRRaGPbi2jrqwob4sRVN9viZMWo0wvtNl3qXAmuzMPoA3/pA1vBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-24T13:04:08.393950Z","bundle_sha256":"7bf4e79636b6bbdf3e552a8f05b971afcf901102f2be2b5e0e43c6b0435eebf9"}}