{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:G3KSXZ5NMHFGZXD7VSURZAE42T","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":"eda13958633f9bd9242607b6e01bc39eda462092f0b49862543e90931e316bbe","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-04-27T12:07:40Z","title_canon_sha256":"c3549c1f51cffadfb142ff548c22f9d13ddb61ebe9e0459771fbfe1bc34a65a8"},"schema_version":"1.0","source":{"id":"2604.24374","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.24374","created_at":"2026-06-03T01:05:14Z"},{"alias_kind":"arxiv_version","alias_value":"2604.24374v2","created_at":"2026-06-03T01:05:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.24374","created_at":"2026-06-03T01:05:14Z"},{"alias_kind":"pith_short_12","alias_value":"G3KSXZ5NMHFG","created_at":"2026-06-03T01:05:14Z"},{"alias_kind":"pith_short_16","alias_value":"G3KSXZ5NMHFGZXD7","created_at":"2026-06-03T01:05:14Z"},{"alias_kind":"pith_short_8","alias_value":"G3KSXZ5N","created_at":"2026-06-03T01:05:14Z"}],"graph_snapshots":[{"event_id":"sha256:439de0861da33589f5e798f8be983455d0876df1678efaeff2001ef15f666ee9","target":"graph","created_at":"2026-06-03T01:05:14Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"MIPIC yields Matryoshka representations that are highly competitive across all capacities, with significant performance advantages observed under extreme low-dimensional."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That aligning intra-relational structures via top-k CKA self-distillation and incrementally chaining semantics from deeper to earlier layers will produce structurally coherent and semantically compact representations without introducing distortions or requiring heavy task-specific tuning."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"MIPIC trains nested Matryoshka representations via self-distilled intra-relational alignment with top-k CKA and progressive information chaining across depths, yielding competitive performance especially at extreme low dimensions."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"MIPIC produces Matryoshka embeddings that remain competitive at any truncation size by aligning intra-layer relations through self-distillation and chaining semantics from deep to shallow layers."}],"snapshot_sha256":"19efbc5bb6a4d17cd2131b64050eb4a67fcac1757add79f647fa2597a598f131"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-21T06:41:31.633335Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T22:06:38.125027Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2604.24374/integrity.json","findings":[],"snapshot_sha256":"34def373d91086ebef96a60c284c2ad6014e75262f99cd7a3d8030168451e316","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Representation Learning (MRL) offers a flexible inference paradigm through nested embeddings; however, learning such structures requires explicit coordination of how information is arranged across embedding dimensionality and model depth. In this work, we propose MIPIC (Matryoshka Representation Learning via Self-Distilled Intra-Relational Alignment and Progressive Information Chaining), a unified training framework designed to produce structurally","authors_text":"Hai An Vu, Linh Ngo Van, Minh-Phuc Truong, Phung Gia Huy, Thang Duc Tran, Thanh Hong Nguyen, Trung Le","cross_cats":[],"headline":"MIPIC produces Matryoshka embeddings that remain competitive at any truncation size by aligning intra-layer relations through self-distillation and chaining semantics from deep to shallow layers.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-04-27T12:07:40Z","title":"MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.24374","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-08T03:31:22.754461Z","id":"4216dd7e-6530-44dd-8fc6-4265f83ee3b5","model_set":{"reader":"grok-4.3"},"one_line_summary":"MIPIC trains nested Matryoshka representations via self-distilled intra-relational alignment with top-k CKA and progressive information chaining across depths, yielding competitive performance especially at extreme low dimensions.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"MIPIC produces Matryoshka embeddings that remain competitive at any truncation size by aligning intra-layer relations through self-distillation and chaining semantics from deep to shallow layers.","strongest_claim":"MIPIC yields Matryoshka representations that are highly competitive across all capacities, with significant performance advantages observed under extreme low-dimensional.","weakest_assumption":"That aligning intra-relational structures via top-k CKA self-distillation and incrementally chaining semantics from deeper to earlier layers will produce structurally coherent and semantically compact representations without introducing distortions or requiring heavy task-specific tuning."}},"verdict_id":"4216dd7e-6530-44dd-8fc6-4265f83ee3b5"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:273f8e69dde301562bad42b1acf97532a0454aefc32fc014acc777a01e261a8b","target":"record","created_at":"2026-06-03T01:05:14Z","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":"eda13958633f9bd9242607b6e01bc39eda462092f0b49862543e90931e316bbe","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-04-27T12:07:40Z","title_canon_sha256":"c3549c1f51cffadfb142ff548c22f9d13ddb61ebe9e0459771fbfe1bc34a65a8"},"schema_version":"1.0","source":{"id":"2604.24374","kind":"arxiv","version":2}},"canonical_sha256":"36d52be7ad61ca6cdc7faca91c809cd4cc109c3110791b51a24c53c3ce7bb0b3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"36d52be7ad61ca6cdc7faca91c809cd4cc109c3110791b51a24c53c3ce7bb0b3","first_computed_at":"2026-06-03T01:05:14.078919Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-03T01:05:14.078919Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MXvoVw/RCfmiFpTKd872HrrrympQwPNdBVxHygmNgmEIc9D1U6UbnLyN67faaMLgY8Pew7UEroVFxY0ZydETDQ==","signature_status":"signed_v1","signed_at":"2026-06-03T01:05:14.079431Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.24374","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:273f8e69dde301562bad42b1acf97532a0454aefc32fc014acc777a01e261a8b","sha256:439de0861da33589f5e798f8be983455d0876df1678efaeff2001ef15f666ee9"],"state_sha256":"fd84e970e0be1d2ecb957331bcd3a84dcaf2347da38092df303ba81a8bb509d9"}