{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:GWTQHJBXBXB3V2UCYZ52MIFKK5","short_pith_number":"pith:GWTQHJBX","canonical_record":{"source":{"id":"2501.10054","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T09:20:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a3c54ae91dd8ffe934c9dc98d880ac4e7254aea3d7474c4a8d5f8e4c6c943462","abstract_canon_sha256":"f233a493e2aad86e5163cf288a76df9bcc8c0d81db51d11b39cfafc001872378"},"schema_version":"1.0"},"canonical_sha256":"35a703a4370dc3baea82c67ba620aa5772b829cc3dcd00abf6b47b74ee9ffda3","source":{"kind":"arxiv","id":"2501.10054","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.10054","created_at":"2026-07-05T10:07:29Z"},{"alias_kind":"arxiv_version","alias_value":"2501.10054v1","created_at":"2026-07-05T10:07:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10054","created_at":"2026-07-05T10:07:29Z"},{"alias_kind":"pith_short_12","alias_value":"GWTQHJBXBXB3","created_at":"2026-07-05T10:07:29Z"},{"alias_kind":"pith_short_16","alias_value":"GWTQHJBXBXB3V2UC","created_at":"2026-07-05T10:07:29Z"},{"alias_kind":"pith_short_8","alias_value":"GWTQHJBX","created_at":"2026-07-05T10:07:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:GWTQHJBXBXB3V2UCYZ52MIFKK5","target":"record","payload":{"canonical_record":{"source":{"id":"2501.10054","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T09:20:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a3c54ae91dd8ffe934c9dc98d880ac4e7254aea3d7474c4a8d5f8e4c6c943462","abstract_canon_sha256":"f233a493e2aad86e5163cf288a76df9bcc8c0d81db51d11b39cfafc001872378"},"schema_version":"1.0"},"canonical_sha256":"35a703a4370dc3baea82c67ba620aa5772b829cc3dcd00abf6b47b74ee9ffda3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:07:29.553393Z","signature_b64":"+6i/AAUD2JbgnDODiA2On6GrxHBL3sP/VbhwQ9aA2rAfgHQp0ZND6KQMZ0BbpSH/TUCnZhCqvWaR27EkY6ZHDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35a703a4370dc3baea82c67ba620aa5772b829cc3dcd00abf6b47b74ee9ffda3","last_reissued_at":"2026-07-05T10:07:29.552946Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:07:29.552946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.10054","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:07:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uIoEVxDeAwccEL2YNVGxXFuNM6gHUON8ux4ffdT8RDNHezLAN/90ih0wTcwOyPvVkRxI6mM/io83xJ/+c1DvCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T01:28:48.665745Z"},"content_sha256":"92f632f91963a40c51aeaefec525497b8b85eddb867de6397b184848a368a275","schema_version":"1.0","event_id":"sha256:92f632f91963a40c51aeaefec525497b8b85eddb867de6397b184848a368a275"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:GWTQHJBXBXB3V2UCYZ52MIFKK5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerating Large Language Models through Partially Linear Feed-Forward Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Gansen Hu, Haibo Chen, Jinglin Wei, Wei Huang, Zhaoguo Wang","submitted_at":"2025-01-17T09:20:56Z","abstract_excerpt":"Large language models (LLMs) demonstrate remarkable capabilities but face deployment challenges due to their massive parameter counts. While existing compression techniques like pruning can reduce model size, it leads to significant accuracy degradation under high compression ratios. We present a novel perspective inspired by constant folding in compiler optimization. Our approach enables parameter reduction by treating activation functions in LLMs as linear functions.\n  However, recent LLMs use complex non-linear activations like GELU that prevent direct application of this technique. We prop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10054","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/2501.10054/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":"2026-07-05T10:07:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ol6Qf6rEZ5F353yWRqCgZrb2huiZ1gJZht2tnulQCko1IU4jipBIYo6bfxdl8xs8up0dzHTl1TrSG5bO1vqbAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T01:28:48.666271Z"},"content_sha256":"9d16caa080c783266ff16151af0bebde06de9b7c66a24dbca4b405c1fcd2fab8","schema_version":"1.0","event_id":"sha256:9d16caa080c783266ff16151af0bebde06de9b7c66a24dbca4b405c1fcd2fab8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GWTQHJBXBXB3V2UCYZ52MIFKK5/bundle.json","state_url":"https://pith.science/pith/GWTQHJBXBXB3V2UCYZ52MIFKK5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GWTQHJBXBXB3V2UCYZ52MIFKK5/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":"2026-08-14T01:28:48Z","links":{"resolver":"https://pith.science/pith/GWTQHJBXBXB3V2UCYZ52MIFKK5","bundle":"https://pith.science/pith/GWTQHJBXBXB3V2UCYZ52MIFKK5/bundle.json","state":"https://pith.science/pith/GWTQHJBXBXB3V2UCYZ52MIFKK5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GWTQHJBXBXB3V2UCYZ52MIFKK5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GWTQHJBXBXB3V2UCYZ52MIFKK5","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":"f233a493e2aad86e5163cf288a76df9bcc8c0d81db51d11b39cfafc001872378","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T09:20:56Z","title_canon_sha256":"a3c54ae91dd8ffe934c9dc98d880ac4e7254aea3d7474c4a8d5f8e4c6c943462"},"schema_version":"1.0","source":{"id":"2501.10054","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.10054","created_at":"2026-07-05T10:07:29Z"},{"alias_kind":"arxiv_version","alias_value":"2501.10054v1","created_at":"2026-07-05T10:07:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10054","created_at":"2026-07-05T10:07:29Z"},{"alias_kind":"pith_short_12","alias_value":"GWTQHJBXBXB3","created_at":"2026-07-05T10:07:29Z"},{"alias_kind":"pith_short_16","alias_value":"GWTQHJBXBXB3V2UC","created_at":"2026-07-05T10:07:29Z"},{"alias_kind":"pith_short_8","alias_value":"GWTQHJBX","created_at":"2026-07-05T10:07:29Z"}],"graph_snapshots":[{"event_id":"sha256:9d16caa080c783266ff16151af0bebde06de9b7c66a24dbca4b405c1fcd2fab8","target":"graph","created_at":"2026-07-05T10:07:29Z","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/2501.10054/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) demonstrate remarkable capabilities but face deployment challenges due to their massive parameter counts. While existing compression techniques like pruning can reduce model size, it leads to significant accuracy degradation under high compression ratios. We present a novel perspective inspired by constant folding in compiler optimization. Our approach enables parameter reduction by treating activation functions in LLMs as linear functions.\n  However, recent LLMs use complex non-linear activations like GELU that prevent direct application of this technique. We prop","authors_text":"Gansen Hu, Haibo Chen, Jinglin Wei, Wei Huang, Zhaoguo Wang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T09:20:56Z","title":"Accelerating Large Language Models through Partially Linear Feed-Forward Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10054","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":"sha256:92f632f91963a40c51aeaefec525497b8b85eddb867de6397b184848a368a275","target":"record","created_at":"2026-07-05T10:07:29Z","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":"f233a493e2aad86e5163cf288a76df9bcc8c0d81db51d11b39cfafc001872378","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-17T09:20:56Z","title_canon_sha256":"a3c54ae91dd8ffe934c9dc98d880ac4e7254aea3d7474c4a8d5f8e4c6c943462"},"schema_version":"1.0","source":{"id":"2501.10054","kind":"arxiv","version":1}},"canonical_sha256":"35a703a4370dc3baea82c67ba620aa5772b829cc3dcd00abf6b47b74ee9ffda3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"35a703a4370dc3baea82c67ba620aa5772b829cc3dcd00abf6b47b74ee9ffda3","first_computed_at":"2026-07-05T10:07:29.552946Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:07:29.552946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+6i/AAUD2JbgnDODiA2On6GrxHBL3sP/VbhwQ9aA2rAfgHQp0ZND6KQMZ0BbpSH/TUCnZhCqvWaR27EkY6ZHDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:07:29.553393Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.10054","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:92f632f91963a40c51aeaefec525497b8b85eddb867de6397b184848a368a275","sha256:9d16caa080c783266ff16151af0bebde06de9b7c66a24dbca4b405c1fcd2fab8"],"state_sha256":"2987f08d248baa73e7cdf37ac6efc369f5b95d58b018252233e5c3cf6d6c0bd6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"on19BpvPUTttL+8aaf7/siVUn5W6TIOOUWw+TVjpTJZf2N+ttc7f+es6JqDhTfvq3F1ppR0pUOUX1gNIh9dHAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T01:28:48.680952Z","bundle_sha256":"6b54ff946034facaa798b9a22f0a75f7397168b2b1c9c5ef630906922bb8c6b6"}}