{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:U5MFHSC4VEDUY3CD3NXFDQFNX7","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":"b07041690d9d23e1269a1abbb7a416720f02a79c07a454ed5d0bd8b8f702fe02","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-21T02:55:33Z","title_canon_sha256":"b224dbc9f903950908984d042a1776f903de946e142d07a021bf7867c5b11e69"},"schema_version":"1.0","source":{"id":"2507.15198","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.15198","created_at":"2026-07-05T11:40:20Z"},{"alias_kind":"arxiv_version","alias_value":"2507.15198v1","created_at":"2026-07-05T11:40:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.15198","created_at":"2026-07-05T11:40:20Z"},{"alias_kind":"pith_short_12","alias_value":"U5MFHSC4VEDU","created_at":"2026-07-05T11:40:20Z"},{"alias_kind":"pith_short_16","alias_value":"U5MFHSC4VEDUY3CD","created_at":"2026-07-05T11:40:20Z"},{"alias_kind":"pith_short_8","alias_value":"U5MFHSC4","created_at":"2026-07-05T11:40:20Z"}],"graph_snapshots":[{"event_id":"sha256:1d5c914ce47cd3b1debc2bb7c861f5309b98e3685348fd9a6b2a8d8a0a5239f7","target":"graph","created_at":"2026-07-05T11:40:20Z","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/2507.15198/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper addresses the challenges of high computational cost and slow inference in deploying large language models. It proposes a distillation strategy guided by multiple teacher models. The method constructs several teacher models and integrates their output probability distributions and intermediate semantic features. This guides the student model to learn from multiple sources of knowledge. As a result, the student model gains stronger language understanding and generation ability while maintaining a small parameter size. To achieve this, the paper introduces a weighted output fusion mech","authors_text":"Junliang Du, Tianze Kang, Xiandong Meng, Xin Hu, Yan Wu, Yexin Tian","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-21T02:55:33Z","title":"Collaborative Distillation Strategies for Parameter-Efficient Language Model Deployment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.15198","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":"sha256:80c6d0017030e41b895fa6cd5cbb5948e423efba0d8650da51231f5fce97fe8c","target":"record","created_at":"2026-07-05T11:40:20Z","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":"b07041690d9d23e1269a1abbb7a416720f02a79c07a454ed5d0bd8b8f702fe02","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-21T02:55:33Z","title_canon_sha256":"b224dbc9f903950908984d042a1776f903de946e142d07a021bf7867c5b11e69"},"schema_version":"1.0","source":{"id":"2507.15198","kind":"arxiv","version":1}},"canonical_sha256":"a75853c85ca9074c6c43db6e51c0adbff4f0dcad513a5fba872b97d59133fd40","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a75853c85ca9074c6c43db6e51c0adbff4f0dcad513a5fba872b97d59133fd40","first_computed_at":"2026-07-05T11:40:20.061968Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:40:20.061968Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BxhQCES5ISPkSCUTlA5XNSVhSCGo6CkazVa9xf3g9adg1uPnfYZBvlCjAc7Ng5uqWYM1fDNfS4il1tgQBFoeCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:40:20.062436Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.15198","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:80c6d0017030e41b895fa6cd5cbb5948e423efba0d8650da51231f5fce97fe8c","sha256:1d5c914ce47cd3b1debc2bb7c861f5309b98e3685348fd9a6b2a8d8a0a5239f7"],"state_sha256":"4838163f32fbe2335f08ff78f3e625740e7bc8b99fa3c7e538f2ed88c3a827d2"}