{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:A4AV5QEPVIKOR6VBEC54GFJIH7","short_pith_number":"pith:A4AV5QEP","schema_version":"1.0","canonical_sha256":"07015ec08faa14e8faa120bbc315283fe22300c41388244688d278c22c009a2b","source":{"kind":"arxiv","id":"2605.30102","version":1},"attestation_state":"computed","paper":{"title":"When Cloud Agents Meet Device Agents: Lessons from Hybrid Multi-Agent Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.MA","authors_text":"Arash Behboodi, Bence Major, Corrado Rainone, Davide Belli","submitted_at":"2026-05-28T15:45:02Z","abstract_excerpt":"The design space of agentic AI inference spans two extremes: frontier large language models (LLMs), typically hosted in the cloud and offering strong performance across a wide range of tasks at substantially high cost, and more cost-efficient small language models (SLMs), which are amenable to on-device inference. Hybrid multi-agent systems (MASs) combining on-device and cloud models offer a promising middle ground, but they also introduce a complex and poorly understood design space in which task accuracy, monetary cost, and edge energy consumption are tightly coupled; in the absence of gener"},"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":"2605.30102","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.MA","submitted_at":"2026-05-28T15:45:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7a039dd3879310c455678df4723142030d92353f15b5e3c484b1eba24272515a","abstract_canon_sha256":"46f6a3a9c962ea986d2101e081e2679e4f45f52fdb6970c9b6b2e68b282a80ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T02:06:09.882779Z","signature_b64":"RqcTAO83OCBHw3ahabZRTx4EctikMQg320Qwa8hzJkR219+1kWP6cJAt0ibrLiVqUVWUJQAoqNAu8o1Q4giOBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07015ec08faa14e8faa120bbc315283fe22300c41388244688d278c22c009a2b","last_reissued_at":"2026-05-29T02:06:09.882382Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T02:06:09.882382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When Cloud Agents Meet Device Agents: Lessons from Hybrid Multi-Agent Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.MA","authors_text":"Arash Behboodi, Bence Major, Corrado Rainone, Davide Belli","submitted_at":"2026-05-28T15:45:02Z","abstract_excerpt":"The design space of agentic AI inference spans two extremes: frontier large language models (LLMs), typically hosted in the cloud and offering strong performance across a wide range of tasks at substantially high cost, and more cost-efficient small language models (SLMs), which are amenable to on-device inference. Hybrid multi-agent systems (MASs) combining on-device and cloud models offer a promising middle ground, but they also introduce a complex and poorly understood design space in which task accuracy, monetary cost, and edge energy consumption are tightly coupled; in the absence of gener"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30102","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/2605.30102/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":"2605.30102","created_at":"2026-05-29T02:06:09.882446+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.30102v1","created_at":"2026-05-29T02:06:09.882446+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30102","created_at":"2026-05-29T02:06:09.882446+00:00"},{"alias_kind":"pith_short_12","alias_value":"A4AV5QEPVIKO","created_at":"2026-05-29T02:06:09.882446+00:00"},{"alias_kind":"pith_short_16","alias_value":"A4AV5QEPVIKOR6VB","created_at":"2026-05-29T02:06:09.882446+00:00"},{"alias_kind":"pith_short_8","alias_value":"A4AV5QEP","created_at":"2026-05-29T02:06:09.882446+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/A4AV5QEPVIKOR6VBEC54GFJIH7","json":"https://pith.science/pith/A4AV5QEPVIKOR6VBEC54GFJIH7.json","graph_json":"https://pith.science/api/pith-number/A4AV5QEPVIKOR6VBEC54GFJIH7/graph.json","events_json":"https://pith.science/api/pith-number/A4AV5QEPVIKOR6VBEC54GFJIH7/events.json","paper":"https://pith.science/paper/A4AV5QEP"},"agent_actions":{"view_html":"https://pith.science/pith/A4AV5QEPVIKOR6VBEC54GFJIH7","download_json":"https://pith.science/pith/A4AV5QEPVIKOR6VBEC54GFJIH7.json","view_paper":"https://pith.science/paper/A4AV5QEP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.30102&json=true","fetch_graph":"https://pith.science/api/pith-number/A4AV5QEPVIKOR6VBEC54GFJIH7/graph.json","fetch_events":"https://pith.science/api/pith-number/A4AV5QEPVIKOR6VBEC54GFJIH7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A4AV5QEPVIKOR6VBEC54GFJIH7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A4AV5QEPVIKOR6VBEC54GFJIH7/action/storage_attestation","attest_author":"https://pith.science/pith/A4AV5QEPVIKOR6VBEC54GFJIH7/action/author_attestation","sign_citation":"https://pith.science/pith/A4AV5QEPVIKOR6VBEC54GFJIH7/action/citation_signature","submit_replication":"https://pith.science/pith/A4AV5QEPVIKOR6VBEC54GFJIH7/action/replication_record"}},"created_at":"2026-05-29T02:06:09.882446+00:00","updated_at":"2026-05-29T02:06:09.882446+00:00"}