{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:QCST7RHSGYJX6JI2IPKCYALP3H","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":"2c447ed347cd2a2711367cac9225ff32011bc371f65cc138d3b1d41bcbb13b42","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T17:33:24Z","title_canon_sha256":"7e9dbc2eaa848cdcc74f2b8fffe3ff888505f8bbd7e6c17ad612278e7b2ce3bc"},"schema_version":"1.0","source":{"id":"2605.17093","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17093","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17093v1","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17093","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"pith_short_12","alias_value":"QCST7RHSGYJX","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"pith_short_16","alias_value":"QCST7RHSGYJX6JI2","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"pith_short_8","alias_value":"QCST7RHS","created_at":"2026-05-20T00:03:39Z"}],"graph_snapshots":[{"event_id":"sha256:e72910e331c673d96bd1a8daa3693645c3c3a4599e73106871ccd9d7f8690f1f","target":"graph","created_at":"2026-05-20T00:03:39Z","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":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T22:33:23.800514Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T22:21:57.734545Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.17093/integrity.json","findings":[],"snapshot_sha256":"35746a90979cd8063f4bdef402bfb1234eed8ebc174748f9578a96c486aa0dde","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Distilling vision-language models into faster hybrid architectures, such as 3:1 Mamba-2/attention mixes, is now standard practice for making inference efficient. Aggregate benchmarks suggest that this works but they hide selective failures. When we distill Qwen3-VL-8B-Instruct into a 3:1 Mamba-2/attention hybrid, student model stays within 2 points of the teacher across visual reasoning benchmarks like MMStar, MMBench, and MMMU-Pro, while dropping 13 points on optical-character-recognition and document tasks. The student can still understand the scene but loses the fine-grained text needed to ","authors_text":"Niraj K. Jha, Yihao Liang","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T17:33:24Z","title":"HEED: Density-Weighted Residual Alignment for Hybrid Vision-Language Model Distillation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17093","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":"sha256:9d918a0db542196c71f380f7f9f4dc1ebfa460c0797b41530008f4c9e542b100","target":"record","created_at":"2026-05-20T00:03:39Z","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":"2c447ed347cd2a2711367cac9225ff32011bc371f65cc138d3b1d41bcbb13b42","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T17:33:24Z","title_canon_sha256":"7e9dbc2eaa848cdcc74f2b8fffe3ff888505f8bbd7e6c17ad612278e7b2ce3bc"},"schema_version":"1.0","source":{"id":"2605.17093","kind":"arxiv","version":1}},"canonical_sha256":"80a53fc4f236137f251a43d42c016fd9d22b84e294515fb363de65f8d4cbbe5c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"80a53fc4f236137f251a43d42c016fd9d22b84e294515fb363de65f8d4cbbe5c","first_computed_at":"2026-05-20T00:03:39.786310Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:39.786310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bLvuVhp5G9t5e7V6815NXKJ2C3TgEEb0SBHIPWns0tQ9KLZx5ttCjvVwPqWQnwrc/RsE8ZdRMmqWAHAJdyKyAw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:39.786975Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17093","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9d918a0db542196c71f380f7f9f4dc1ebfa460c0797b41530008f4c9e542b100","sha256:e72910e331c673d96bd1a8daa3693645c3c3a4599e73106871ccd9d7f8690f1f"],"state_sha256":"4fa103946683f4eb40c73a80f02c439471c6aad2d71b3390d220d7ecf500ec60"}