{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:ZTU3VR2IDGP7AO3WCPVQQUX6CS","short_pith_number":"pith:ZTU3VR2I","canonical_record":{"source":{"id":"2402.02338","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-02-04T04:21:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"11ab8429287fa980add99058959a82f8b6f084a8417b9914879ff4603b7a703d","abstract_canon_sha256":"dd59e1778658c6eecfe9c0c9a580aa6683b831e07c11a1b890bd6cd2baed1130"},"schema_version":"1.0"},"canonical_sha256":"cce9bac748199ff03b7613eb0852fe14b28e9a45f00a5c876ca1ba018c2a9c54","source":{"kind":"arxiv","id":"2402.02338","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.02338","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"arxiv_version","alias_value":"2402.02338v3","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.02338","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"pith_short_12","alias_value":"ZTU3VR2IDGP7","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"pith_short_16","alias_value":"ZTU3VR2IDGP7AO3W","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"pith_short_8","alias_value":"ZTU3VR2I","created_at":"2026-07-05T08:52:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:ZTU3VR2IDGP7AO3WCPVQQUX6CS","target":"record","payload":{"canonical_record":{"source":{"id":"2402.02338","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-02-04T04:21:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"11ab8429287fa980add99058959a82f8b6f084a8417b9914879ff4603b7a703d","abstract_canon_sha256":"dd59e1778658c6eecfe9c0c9a580aa6683b831e07c11a1b890bd6cd2baed1130"},"schema_version":"1.0"},"canonical_sha256":"cce9bac748199ff03b7613eb0852fe14b28e9a45f00a5c876ca1ba018c2a9c54","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:52:28.959847Z","signature_b64":"K0YSPVpgfz+GoyNNyl9lVtaKrrIKJ/J5WG2X6ohO0K3H9OcVU/kAr8Wr7nfrBKJ/nFxrpEXiThNQhV/5g7M1Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cce9bac748199ff03b7613eb0852fe14b28e9a45f00a5c876ca1ba018c2a9c54","last_reissued_at":"2026-07-05T08:52:28.959424Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:52:28.959424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.02338","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:52:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WQMfQ/HX7I38gkcUEsqWvA0XS/UvqBQ4kE4pzv8GlFyNnO4lB7YWOuWZrIuPIWxaVTkTWxv5XsiaQlLntA8JBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:33:49.101250Z"},"content_sha256":"7768f7ea999e4d1a303103d3dcebe98663127b6217baad3a2dd577aeab8b5332","schema_version":"1.0","event_id":"sha256:7768f7ea999e4d1a303103d3dcebe98663127b6217baad3a2dd577aeab8b5332"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:ZTU3VR2IDGP7AO3WCPVQQUX6CS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"NetLLM: Adapting Large Language Models for Networking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NI","authors_text":"Duo Wu, Fangxin Wang, Junchen Jiang, Shuguang Cui, Xianda Wang, Yaqi Qiao, Zhi Wang","submitted_at":"2024-02-04T04:21:34Z","abstract_excerpt":"Many networking tasks now employ deep learning (DL) to solve complex prediction and optimization problems. However, current design philosophy of DL-based algorithms entails intensive engineering overhead due to the manual design of deep neural networks (DNNs) for different networking tasks. Besides, DNNs tend to achieve poor generalization performance on unseen data distributions/environments.\n  Motivated by the recent success of large language models (LLMs), this work studies the LLM adaptation for networking to explore a more sustainable design philosophy. With the powerful pre-trained knowl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.02338","kind":"arxiv","version":3},"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/2402.02338/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":"2026-07-05T08:52:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VT5OHFu8s9C4+YV17+BdHVHu4x++pBJosw36oFQTqff21YV9hET5KYh4hUZ0y3ZMkn6omh9RPDK8Tn8TMGh2Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:33:49.102219Z"},"content_sha256":"b8f86a2e5f15c96dc8ca062b1ff629edf67bd307cd77d0a4dbdf2b63416bcc0e","schema_version":"1.0","event_id":"sha256:b8f86a2e5f15c96dc8ca062b1ff629edf67bd307cd77d0a4dbdf2b63416bcc0e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZTU3VR2IDGP7AO3WCPVQQUX6CS/bundle.json","state_url":"https://pith.science/pith/ZTU3VR2IDGP7AO3WCPVQQUX6CS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZTU3VR2IDGP7AO3WCPVQQUX6CS/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":"2026-08-09T18:33:49Z","links":{"resolver":"https://pith.science/pith/ZTU3VR2IDGP7AO3WCPVQQUX6CS","bundle":"https://pith.science/pith/ZTU3VR2IDGP7AO3WCPVQQUX6CS/bundle.json","state":"https://pith.science/pith/ZTU3VR2IDGP7AO3WCPVQQUX6CS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZTU3VR2IDGP7AO3WCPVQQUX6CS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ZTU3VR2IDGP7AO3WCPVQQUX6CS","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":"dd59e1778658c6eecfe9c0c9a580aa6683b831e07c11a1b890bd6cd2baed1130","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-02-04T04:21:34Z","title_canon_sha256":"11ab8429287fa980add99058959a82f8b6f084a8417b9914879ff4603b7a703d"},"schema_version":"1.0","source":{"id":"2402.02338","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.02338","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"arxiv_version","alias_value":"2402.02338v3","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.02338","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"pith_short_12","alias_value":"ZTU3VR2IDGP7","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"pith_short_16","alias_value":"ZTU3VR2IDGP7AO3W","created_at":"2026-07-05T08:52:28Z"},{"alias_kind":"pith_short_8","alias_value":"ZTU3VR2I","created_at":"2026-07-05T08:52:28Z"}],"graph_snapshots":[{"event_id":"sha256:b8f86a2e5f15c96dc8ca062b1ff629edf67bd307cd77d0a4dbdf2b63416bcc0e","target":"graph","created_at":"2026-07-05T08:52:28Z","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/2402.02338/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many networking tasks now employ deep learning (DL) to solve complex prediction and optimization problems. However, current design philosophy of DL-based algorithms entails intensive engineering overhead due to the manual design of deep neural networks (DNNs) for different networking tasks. Besides, DNNs tend to achieve poor generalization performance on unseen data distributions/environments.\n  Motivated by the recent success of large language models (LLMs), this work studies the LLM adaptation for networking to explore a more sustainable design philosophy. With the powerful pre-trained knowl","authors_text":"Duo Wu, Fangxin Wang, Junchen Jiang, Shuguang Cui, Xianda Wang, Yaqi Qiao, Zhi Wang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-02-04T04:21:34Z","title":"NetLLM: Adapting Large Language Models for Networking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.02338","kind":"arxiv","version":3},"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":"sha256:7768f7ea999e4d1a303103d3dcebe98663127b6217baad3a2dd577aeab8b5332","target":"record","created_at":"2026-07-05T08:52:28Z","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":"dd59e1778658c6eecfe9c0c9a580aa6683b831e07c11a1b890bd6cd2baed1130","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-02-04T04:21:34Z","title_canon_sha256":"11ab8429287fa980add99058959a82f8b6f084a8417b9914879ff4603b7a703d"},"schema_version":"1.0","source":{"id":"2402.02338","kind":"arxiv","version":3}},"canonical_sha256":"cce9bac748199ff03b7613eb0852fe14b28e9a45f00a5c876ca1ba018c2a9c54","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cce9bac748199ff03b7613eb0852fe14b28e9a45f00a5c876ca1ba018c2a9c54","first_computed_at":"2026-07-05T08:52:28.959424Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:52:28.959424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"K0YSPVpgfz+GoyNNyl9lVtaKrrIKJ/J5WG2X6ohO0K3H9OcVU/kAr8Wr7nfrBKJ/nFxrpEXiThNQhV/5g7M1Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:52:28.959847Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.02338","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7768f7ea999e4d1a303103d3dcebe98663127b6217baad3a2dd577aeab8b5332","sha256:b8f86a2e5f15c96dc8ca062b1ff629edf67bd307cd77d0a4dbdf2b63416bcc0e"],"state_sha256":"e5d9c61df1c46479dd07d8f1462e528b2986945f9b8491842c94cad8ae76bd5e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vEvxfqquIeLznDVwCjZUIGUjnRFnxwCphmJUK9YFOCiMP44qZPuYJxr31fxO994cjEQFixfYFQN30VW/nMqbCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T18:33:49.107327Z","bundle_sha256":"9751a0af931cb815758e7e1f8329bde0487798ef099d739ed38b25ef9df7ac98"}}