{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:3RKO2WW7H5KASGBCRNYTIM3PI2","short_pith_number":"pith:3RKO2WW7","canonical_record":{"source":{"id":"2412.08284","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2024-12-11T10:59:05Z","cross_cats_sorted":[],"title_canon_sha256":"590e121dde80219c948a0ee39d0b8d4d79a748a3e197e6c5867ba40bf6a615a8","abstract_canon_sha256":"daae38f579351a6f92c586302ee51107db73b298a8f2e25ab086409d05560fb3"},"schema_version":"1.0"},"canonical_sha256":"dc54ed5adf3f540918228b7134336f4684b4077c3c9eaf0cf643028535f6299f","source":{"kind":"arxiv","id":"2412.08284","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.08284","created_at":"2026-07-05T09:47:47Z"},{"alias_kind":"arxiv_version","alias_value":"2412.08284v1","created_at":"2026-07-05T09:47:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08284","created_at":"2026-07-05T09:47:47Z"},{"alias_kind":"pith_short_12","alias_value":"3RKO2WW7H5KA","created_at":"2026-07-05T09:47:47Z"},{"alias_kind":"pith_short_16","alias_value":"3RKO2WW7H5KASGBC","created_at":"2026-07-05T09:47:47Z"},{"alias_kind":"pith_short_8","alias_value":"3RKO2WW7","created_at":"2026-07-05T09:47:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:3RKO2WW7H5KASGBCRNYTIM3PI2","target":"record","payload":{"canonical_record":{"source":{"id":"2412.08284","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2024-12-11T10:59:05Z","cross_cats_sorted":[],"title_canon_sha256":"590e121dde80219c948a0ee39d0b8d4d79a748a3e197e6c5867ba40bf6a615a8","abstract_canon_sha256":"daae38f579351a6f92c586302ee51107db73b298a8f2e25ab086409d05560fb3"},"schema_version":"1.0"},"canonical_sha256":"dc54ed5adf3f540918228b7134336f4684b4077c3c9eaf0cf643028535f6299f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:47:47.712388Z","signature_b64":"RV3AsOqGFDyPYxYMviG9YLSP92xnZLOOWYcwCuww13UHz1LLOkEHGAseiGxNzGdKjL8aKF2QISu/coY2hBeMBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc54ed5adf3f540918228b7134336f4684b4077c3c9eaf0cf643028535f6299f","last_reissued_at":"2026-07-05T09:47:47.711920Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:47:47.711920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.08284","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:47:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZXQm2r3vQfqmjgSWd4VPg8ikkC8EL9Ivo7kWp82WcB25UklHhpP9Svkf0BTsnZj8rAVEF3iQlND13UV1QH4gAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T08:17:57.597784Z"},"content_sha256":"2019996e1d3a989ceeed1da5ac63a951f0666e84549a62945aa9038d8824c494","schema_version":"1.0","event_id":"sha256:2019996e1d3a989ceeed1da5ac63a951f0666e84549a62945aa9038d8824c494"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:3RKO2WW7H5KASGBCRNYTIM3PI2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Collaborative Inference for Large Models with Task Offloading and Early Exiting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Hongli Xu, Yang Xu, Yunming Liao, Zhiyuan Yao, Zuan Xie","submitted_at":"2024-12-11T10:59:05Z","abstract_excerpt":"In 5G smart cities, edge computing is employed to provide nearby computing services for end devices, and the large-scale models (e.g., GPT and LLaMA) can be deployed at the network edge to boost the service quality. However, due to the constraints of memory size and computing capacity, it is difficult to run these large-scale models on a single edge node. To meet the resource constraints, a large-scale model can be partitioned into multiple sub-models and deployed across multiple edge nodes. Then tasks are offloaded to the edge nodes for collaborative inference. Additionally, we incorporate th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08284","kind":"arxiv","version":1},"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/2412.08284/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":"2026-07-05T09:47:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"plmytaOpnrq3MXVzOimePpnHFdVNLCVE/cPrZtq0zQMDgf1rHlhdGTbaIMZ15GaJX4VrVPbpbhnmUK4JS7ckCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T08:17:57.598526Z"},"content_sha256":"2e57b5f372c6ffdb89c658bd1a2aafa76e31444c5deb43a7c2e68f39bf35466a","schema_version":"1.0","event_id":"sha256:2e57b5f372c6ffdb89c658bd1a2aafa76e31444c5deb43a7c2e68f39bf35466a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3RKO2WW7H5KASGBCRNYTIM3PI2/bundle.json","state_url":"https://pith.science/pith/3RKO2WW7H5KASGBCRNYTIM3PI2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3RKO2WW7H5KASGBCRNYTIM3PI2/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":"2026-08-17T08:17:57Z","links":{"resolver":"https://pith.science/pith/3RKO2WW7H5KASGBCRNYTIM3PI2","bundle":"https://pith.science/pith/3RKO2WW7H5KASGBCRNYTIM3PI2/bundle.json","state":"https://pith.science/pith/3RKO2WW7H5KASGBCRNYTIM3PI2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3RKO2WW7H5KASGBCRNYTIM3PI2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:3RKO2WW7H5KASGBCRNYTIM3PI2","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":"daae38f579351a6f92c586302ee51107db73b298a8f2e25ab086409d05560fb3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2024-12-11T10:59:05Z","title_canon_sha256":"590e121dde80219c948a0ee39d0b8d4d79a748a3e197e6c5867ba40bf6a615a8"},"schema_version":"1.0","source":{"id":"2412.08284","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.08284","created_at":"2026-07-05T09:47:47Z"},{"alias_kind":"arxiv_version","alias_value":"2412.08284v1","created_at":"2026-07-05T09:47:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08284","created_at":"2026-07-05T09:47:47Z"},{"alias_kind":"pith_short_12","alias_value":"3RKO2WW7H5KA","created_at":"2026-07-05T09:47:47Z"},{"alias_kind":"pith_short_16","alias_value":"3RKO2WW7H5KASGBC","created_at":"2026-07-05T09:47:47Z"},{"alias_kind":"pith_short_8","alias_value":"3RKO2WW7","created_at":"2026-07-05T09:47:47Z"}],"graph_snapshots":[{"event_id":"sha256:2e57b5f372c6ffdb89c658bd1a2aafa76e31444c5deb43a7c2e68f39bf35466a","target":"graph","created_at":"2026-07-05T09:47:47Z","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/2412.08284/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In 5G smart cities, edge computing is employed to provide nearby computing services for end devices, and the large-scale models (e.g., GPT and LLaMA) can be deployed at the network edge to boost the service quality. However, due to the constraints of memory size and computing capacity, it is difficult to run these large-scale models on a single edge node. To meet the resource constraints, a large-scale model can be partitioned into multiple sub-models and deployed across multiple edge nodes. Then tasks are offloaded to the edge nodes for collaborative inference. Additionally, we incorporate th","authors_text":"Hongli Xu, Yang Xu, Yunming Liao, Zhiyuan Yao, Zuan Xie","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2024-12-11T10:59:05Z","title":"Collaborative Inference for Large Models with Task Offloading and Early Exiting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08284","kind":"arxiv","version":1},"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":"sha256:2019996e1d3a989ceeed1da5ac63a951f0666e84549a62945aa9038d8824c494","target":"record","created_at":"2026-07-05T09:47:47Z","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":"daae38f579351a6f92c586302ee51107db73b298a8f2e25ab086409d05560fb3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2024-12-11T10:59:05Z","title_canon_sha256":"590e121dde80219c948a0ee39d0b8d4d79a748a3e197e6c5867ba40bf6a615a8"},"schema_version":"1.0","source":{"id":"2412.08284","kind":"arxiv","version":1}},"canonical_sha256":"dc54ed5adf3f540918228b7134336f4684b4077c3c9eaf0cf643028535f6299f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dc54ed5adf3f540918228b7134336f4684b4077c3c9eaf0cf643028535f6299f","first_computed_at":"2026-07-05T09:47:47.711920Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:47:47.711920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RV3AsOqGFDyPYxYMviG9YLSP92xnZLOOWYcwCuww13UHz1LLOkEHGAseiGxNzGdKjL8aKF2QISu/coY2hBeMBw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:47:47.712388Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.08284","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2019996e1d3a989ceeed1da5ac63a951f0666e84549a62945aa9038d8824c494","sha256:2e57b5f372c6ffdb89c658bd1a2aafa76e31444c5deb43a7c2e68f39bf35466a"],"state_sha256":"b055a9683465f51ef263f104eac3d1022bb16f7260100ef0916f32537ed17c51"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A3NbTcmTnjV9Bo2QM3YQ3yGJzm5Srzwa1bRYZvXwiJSL49sa8b+jFIEX/TYebVgWOck7bu0fs6GJRuNCMqxUBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T08:17:57.603691Z","bundle_sha256":"1cabfd7975959fdd135f959b7bf44c41f98a0d8e7e2307c6df16512f48820c6c"}}