{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:WJZYQB3G2T4SLQYJUHTCGWOFDN","short_pith_number":"pith:WJZYQB3G","canonical_record":{"source":{"id":"2510.05544","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-07T03:07:47Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"34798bf7465f6c7cb7f42c266717cea53bd1141be3b0e8d800653131dce3c2cb","abstract_canon_sha256":"dd6dfb7ddb20a24e9846f34531361cd8bcca995180b814a2ab9cf358f2fe6357"},"schema_version":"1.0"},"canonical_sha256":"b273880766d4f925c309a1e62359c51b72e2eb88e0ca4b913a594b7f29e0cc91","source":{"kind":"arxiv","id":"2510.05544","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.05544","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"arxiv_version","alias_value":"2510.05544v2","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.05544","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"pith_short_12","alias_value":"WJZYQB3G2T4S","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"pith_short_16","alias_value":"WJZYQB3G2T4SLQYJ","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"pith_short_8","alias_value":"WJZYQB3G","created_at":"2026-06-05T00:13:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:WJZYQB3G2T4SLQYJUHTCGWOFDN","target":"record","payload":{"canonical_record":{"source":{"id":"2510.05544","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-07T03:07:47Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"34798bf7465f6c7cb7f42c266717cea53bd1141be3b0e8d800653131dce3c2cb","abstract_canon_sha256":"dd6dfb7ddb20a24e9846f34531361cd8bcca995180b814a2ab9cf358f2fe6357"},"schema_version":"1.0"},"canonical_sha256":"b273880766d4f925c309a1e62359c51b72e2eb88e0ca4b913a594b7f29e0cc91","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T00:13:43.931206Z","signature_b64":"8KdHD4NQKD2YzlQDjc0L6w4q4aG8/eooqqTcQwpkvmHMMqdEzeNoCkVHbW+3m92pL4/3Ww1jQUiEtZ5WBlF0CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b273880766d4f925c309a1e62359c51b72e2eb88e0ca4b913a594b7f29e0cc91","last_reissued_at":"2026-06-05T00:13:43.930694Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T00:13:43.930694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2510.05544","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-05T00:13:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c0Qa4IGzgXM+jzYjwH+NrWfxye1u7z+Bi5Lyf6XiyBE5siFXg15JV5ko0bZ+OcqTQQ3vSNAoLxrrv9g7CDHhBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T18:32:19.860957Z"},"content_sha256":"ad9cad4e5e1e631035b9fa37db9a9e948beb70419fe6f383da70ab4ea7bca066","schema_version":"1.0","event_id":"sha256:ad9cad4e5e1e631035b9fa37db9a9e948beb70419fe6f383da70ab4ea7bca066"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:WJZYQB3G2T4SLQYJUHTCGWOFDN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Jiayi Tian, Jing Liu, Nathan Susanj, Parsa Madinei, Rupak Swaminathan, Ryan Solgi, Zheng Zhang","submitted_at":"2025-10-07T03:07:47Z","abstract_excerpt":"Large language models (LLM) and vision-language models (VLM) have achieved state-of-the-art performance, but they impose significant memory and computing challenges in deployment. We present a novel low-rank compression framework to address this challenge. First, we upper bound the change of network loss via layer-wise activation-based compression errors, filling a theoretical gap in the literature. We then formulate low-rank model compression as a bi-objective optimization and prove that a single uniform tolerance yields surrogate Pareto-optimal heterogeneous ranks. Based on our theoretical i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.05544","kind":"arxiv","version":2},"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/2510.05544/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-05T00:13:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/qA+3iYGNyxUOBw7yyDRuaRUsdkc6ydpX9yIiYDBUDrntCAWXNb2pa1ggLDn2GDII4Mt/RQPXvUO9VKR8NDEDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T18:32:19.861714Z"},"content_sha256":"bd8aaae0234dd94ec2c058976d776f8965892e025342fa705eb9ae1046740700","schema_version":"1.0","event_id":"sha256:bd8aaae0234dd94ec2c058976d776f8965892e025342fa705eb9ae1046740700"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WJZYQB3G2T4SLQYJUHTCGWOFDN/bundle.json","state_url":"https://pith.science/pith/WJZYQB3G2T4SLQYJUHTCGWOFDN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WJZYQB3G2T4SLQYJUHTCGWOFDN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-05T18:32:19Z","links":{"resolver":"https://pith.science/pith/WJZYQB3G2T4SLQYJUHTCGWOFDN","bundle":"https://pith.science/pith/WJZYQB3G2T4SLQYJUHTCGWOFDN/bundle.json","state":"https://pith.science/pith/WJZYQB3G2T4SLQYJUHTCGWOFDN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WJZYQB3G2T4SLQYJUHTCGWOFDN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:WJZYQB3G2T4SLQYJUHTCGWOFDN","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":"dd6dfb7ddb20a24e9846f34531361cd8bcca995180b814a2ab9cf358f2fe6357","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-07T03:07:47Z","title_canon_sha256":"34798bf7465f6c7cb7f42c266717cea53bd1141be3b0e8d800653131dce3c2cb"},"schema_version":"1.0","source":{"id":"2510.05544","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.05544","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"arxiv_version","alias_value":"2510.05544v2","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.05544","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"pith_short_12","alias_value":"WJZYQB3G2T4S","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"pith_short_16","alias_value":"WJZYQB3G2T4SLQYJ","created_at":"2026-06-05T00:13:43Z"},{"alias_kind":"pith_short_8","alias_value":"WJZYQB3G","created_at":"2026-06-05T00:13:43Z"}],"graph_snapshots":[{"event_id":"sha256:bd8aaae0234dd94ec2c058976d776f8965892e025342fa705eb9ae1046740700","target":"graph","created_at":"2026-06-05T00:13:43Z","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/2510.05544/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLM) and vision-language models (VLM) have achieved state-of-the-art performance, but they impose significant memory and computing challenges in deployment. We present a novel low-rank compression framework to address this challenge. First, we upper bound the change of network loss via layer-wise activation-based compression errors, filling a theoretical gap in the literature. We then formulate low-rank model compression as a bi-objective optimization and prove that a single uniform tolerance yields surrogate Pareto-optimal heterogeneous ranks. Based on our theoretical i","authors_text":"Jiayi Tian, Jing Liu, Nathan Susanj, Parsa Madinei, Rupak Swaminathan, Ryan Solgi, Zheng Zhang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-07T03:07:47Z","title":"Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.05544","kind":"arxiv","version":2},"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:ad9cad4e5e1e631035b9fa37db9a9e948beb70419fe6f383da70ab4ea7bca066","target":"record","created_at":"2026-06-05T00:13:43Z","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":"dd6dfb7ddb20a24e9846f34531361cd8bcca995180b814a2ab9cf358f2fe6357","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-07T03:07:47Z","title_canon_sha256":"34798bf7465f6c7cb7f42c266717cea53bd1141be3b0e8d800653131dce3c2cb"},"schema_version":"1.0","source":{"id":"2510.05544","kind":"arxiv","version":2}},"canonical_sha256":"b273880766d4f925c309a1e62359c51b72e2eb88e0ca4b913a594b7f29e0cc91","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b273880766d4f925c309a1e62359c51b72e2eb88e0ca4b913a594b7f29e0cc91","first_computed_at":"2026-06-05T00:13:43.930694Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-05T00:13:43.930694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8KdHD4NQKD2YzlQDjc0L6w4q4aG8/eooqqTcQwpkvmHMMqdEzeNoCkVHbW+3m92pL4/3Ww1jQUiEtZ5WBlF0CA==","signature_status":"signed_v1","signed_at":"2026-06-05T00:13:43.931206Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.05544","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ad9cad4e5e1e631035b9fa37db9a9e948beb70419fe6f383da70ab4ea7bca066","sha256:bd8aaae0234dd94ec2c058976d776f8965892e025342fa705eb9ae1046740700"],"state_sha256":"95ed63f384d69313809056b02c6447340caffaaf9b35355c6b72ddb29da41a42"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UaekwykHX6rKypLjqNW2+zDXJqLS310zIZpI68kmehA/C6Om9l71lKwDzHEq48nOL0hnre4FojXdVdsdcE2bAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T18:32:19.867338Z","bundle_sha256":"34ceb742bf7530cfc379ca55db7dbea8341f8586646fffd341de328105cf6249"}}