{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:A2WLWH226MYWYOJWC4YK2NCJKT","short_pith_number":"pith:A2WLWH22","schema_version":"1.0","canonical_sha256":"06acbb1f5af3316c39361730ad344954c23133c83774c52b7b25be699e341f69","source":{"kind":"arxiv","id":"2607.18244","version":1},"attestation_state":"computed","paper":{"title":"Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.NI","authors_text":"Fen Hou, Tianji He, Yulin Shao","submitted_at":"2026-04-29T13:14:00Z","abstract_excerpt":"Deploying large language models (LLMs) at the network edge is hindered by their enormous cost, yet the reasoning quality they provide remains indispensable. Heterogeneous collaboration between edge small models and a server LLM has emerged as a promising direction, but existing methods fail under the dynamic conditions of multi-user contention, autoregressive generation, and time-varying resources. This paper puts forward a process reward model (PRM)-aided two-stage decoupled acceleration (PRADA) framework, which is built on a fundamental change of perspective: instead of querying a PRM online"},"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":"2607.18244","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NI","submitted_at":"2026-04-29T13:14:00Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"14f38cfc31ab6f37dbd5fbe1049ea756b83848c944072352851591352990de9a","abstract_canon_sha256":"47d2f420aa70a2df3f4efc150e41f40197b2be67545e2d59c0c6c248e92080c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-22T00:22:38.270792Z","signature_b64":"8NMBE6P2uCqXquMUscpMtLkr12I5ElNTE53S2eRpilhSRB2pKTFCp9NfT8a+ZMvWqIjnxv3p1F86jhh+SiMUDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"06acbb1f5af3316c39361730ad344954c23133c83774c52b7b25be699e341f69","last_reissued_at":"2026-07-22T00:22:38.269957Z","signature_status":"signed_v1","first_computed_at":"2026-07-22T00:22:38.269957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.NI","authors_text":"Fen Hou, Tianji He, Yulin Shao","submitted_at":"2026-04-29T13:14:00Z","abstract_excerpt":"Deploying large language models (LLMs) at the network edge is hindered by their enormous cost, yet the reasoning quality they provide remains indispensable. Heterogeneous collaboration between edge small models and a server LLM has emerged as a promising direction, but existing methods fail under the dynamic conditions of multi-user contention, autoregressive generation, and time-varying resources. This paper puts forward a process reward model (PRM)-aided two-stage decoupled acceleration (PRADA) framework, which is built on a fundamental change of perspective: instead of querying a PRM online"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.18244","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/2607.18244/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":"2607.18244","created_at":"2026-07-22T00:22:38.270390+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.18244v1","created_at":"2026-07-22T00:22:38.270390+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.18244","created_at":"2026-07-22T00:22:38.270390+00:00"},{"alias_kind":"pith_short_12","alias_value":"A2WLWH226MYW","created_at":"2026-07-22T00:22:38.270390+00:00"},{"alias_kind":"pith_short_16","alias_value":"A2WLWH226MYWYOJW","created_at":"2026-07-22T00:22:38.270390+00:00"},{"alias_kind":"pith_short_8","alias_value":"A2WLWH22","created_at":"2026-07-22T00:22:38.270390+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/A2WLWH226MYWYOJWC4YK2NCJKT","json":"https://pith.science/pith/A2WLWH226MYWYOJWC4YK2NCJKT.json","graph_json":"https://pith.science/api/pith-number/A2WLWH226MYWYOJWC4YK2NCJKT/graph.json","events_json":"https://pith.science/api/pith-number/A2WLWH226MYWYOJWC4YK2NCJKT/events.json","paper":"https://pith.science/paper/A2WLWH22"},"agent_actions":{"view_html":"https://pith.science/pith/A2WLWH226MYWYOJWC4YK2NCJKT","download_json":"https://pith.science/pith/A2WLWH226MYWYOJWC4YK2NCJKT.json","view_paper":"https://pith.science/paper/A2WLWH22","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.18244&json=true","fetch_graph":"https://pith.science/api/pith-number/A2WLWH226MYWYOJWC4YK2NCJKT/graph.json","fetch_events":"https://pith.science/api/pith-number/A2WLWH226MYWYOJWC4YK2NCJKT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A2WLWH226MYWYOJWC4YK2NCJKT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A2WLWH226MYWYOJWC4YK2NCJKT/action/storage_attestation","attest_author":"https://pith.science/pith/A2WLWH226MYWYOJWC4YK2NCJKT/action/author_attestation","sign_citation":"https://pith.science/pith/A2WLWH226MYWYOJWC4YK2NCJKT/action/citation_signature","submit_replication":"https://pith.science/pith/A2WLWH226MYWYOJWC4YK2NCJKT/action/replication_record"}},"created_at":"2026-07-22T00:22:38.270390+00:00","updated_at":"2026-07-22T00:22:38.270390+00:00"}